Optimization of human-aware logistics and manufacturing systems: A comprehensive review of modeling approaches and applications

P une , Thibaul ; Absi, Nabil; Bo odin, Vale ia; Ca a uzza, Diego
A icle
Op imiza ion o human-awa e logis ics and manu ac u ing
sys ems: A comp ehensi e e iew o modeling app oaches
and applica ions
EURO Jou nal on T anspo a ion and Logis ics (EJTL)
P o ided in Coope a ion wi h:
Associa ion o Eu opean Ope a ional Resea ch Socie ies (EURO), F ibou g
Sugges ed Ci a ion: P une , Thibaul ; Absi, Nabil; Bo odin, Vale ia; Ca a uzza, Diego (2024) :
Op imiza ion o human-awa e logis ics and manu ac u ing sys ems: A comp ehensi e e iew o
modeling app oaches and applica ions, EURO Jou nal on T anspo a ion and Logis ics (EJTL), ISSN
2192-4384, Else ie , Ams e dam, Vol. 13, Iss. 1, pp. 1-34,
h ps://doi.o g/10.1016/j.ej l.2024.100136
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EURO Jou nal on T anspo a ion and Logis ics
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Op imiza ion o human-awa e logis ics and manu ac u ing sys ems: A
comp ehensi e e iew o modeling app oaches and applica ions
Thibaul P une a,b,∗, Nabil Absia, Vale ia Bo odina,c, Diego Ca a uzzad
aMines Sain -E ienne, Uni . Cle mon Au e gne, CNRS, UMR 6158 LIMOS, Cen e CMP, Depa emen SFL, F-13541 Ga danne, F ance
bCERMICS, Ecole des Pon s, Ma ne la Vallée, F ance
cIMT A lan ique, LS2N-CNRS, La Chan e ie, 4, ue Al ed Kas le , Nan es cedex 3, F-44307, F ance
dUni . Lille, CNRS, Cen ale Lille, In ia, UMR 9189 - CRIS AL - Cen e de Reche che en In o ma ique Signal e Au oma ique de Lille, F-59000 Lille, F ance
ARTICLE INFO
Keywo ds:
Op imiza ion
Human ac o s
Indus y 5.0
Manu ac u ing
Logis ics
E gonomics
ABSTRACT
His o ically, Ope a ions Resea ch (OR) discipline has mainly been ocusing on economic conce ns. Since he
ea ly 2000s, human conside a ions a e gaining inc easing a en ion, pushed by he g owing socie al conce ns
o sus ainable de elopmen on he same e ms as he economic and ecological ones. This pape is he i s
pa o a wo k ha aims a e iewing he e o s dedica ed by he OR communi y o he in eg a ion o human
ac o s in o logis ics and manu ac u ing sys ems. A ocus is pu on he modeling and solu ion app oaches used
o conside human cha ac e is ics, hei p ac ical ele ance, and he complexi y induced by hei in eg a ion
wi hin op imiza ion models. The ma e ial p esen ed in his wo k has been e ie ed h ough a semi-sys ema ic
sea ch o he li e a u e. Then, a comp ehensi e analysis o he e ie ed co pus is ca ied ou o map he
ela ed li e a u e by class o p oblems encoun e ed in logis ics and manu ac u ing. These include wa ehousing,
ehicle ou ing, scheduling, p oduc ion planning, and wo k o ce scheduling and managemen . We in es iga e
he ma hema ical p og amming echniques used o in eg a e human ac o s in o op imiza ion models. Finally,
a numbe o gaps in he li e a u e a e iden i ied, and new sugges ions on how o sui ably in eg a e human
ac o s in OR p oblems encoun e ed in logis ics and manu ac u ing sys ems a e discussed.
Con en s
1. In oduc ion ...................................................................................................................................................................................................... 2
2. Te minology, ela ed backg ound, scope, and mo i a ions..................................................................................................................................... 4
2.1. De ini ions o human- ela ed key concep s................................................................................................................................................ 4
2.2. P e ious e iews .................................................................................................................................................................................... 5
2.3. Scope .................................................................................................................................................................................................... 5
2.4. Mo i a ions............................................................................................................................................................................................ 6
3. Me hodology ..................................................................................................................................................................................................... 6
3.1. Ma e ial collec ion me hodology.............................................................................................................................................................. 6
3.2. Summa y ables: Fo ma and con en ion.................................................................................................................................................. 7
4. Logis ics sys ems .............................................................................................................................................................................................. 8
4.1. Wa ehousing.......................................................................................................................................................................................... 8
4.2. Vehicle ou ing ...................................................................................................................................................................................... 8
5. Manu ac u ing sys ems....................................................................................................................................................................................... 9
5.1. P oduc ion scheduling............................................................................................................................................................................. 9
5.2. Assembly line balancing ........................................................................................................................................................................ 11
5.3. P oduc ion planning and in en o y managemen ....................................................................................................................................... 11
5.4. Design o sys ems................................................................................................................................................................................... 12
6. Wo k o ce- ela ed p oblems ................................................................................................................................................................................ 14
6.1. Wo k o ce scheduling ............................................................................................................................................................................. 14
6.1.1. Job o a ion............................................................................................................................................................................. 14
∗Co esponding au ho a : Mines Sain -E ienne, Uni . Cle mon Au e gne, CNRS, UMR 6158 LIMOS, Cen e CMP, Depa emen SFL, F-13541 Ga danne, F ance.
E-mail add ess: [email p o ec ed] (T. P une ).
h ps://doi.o g/10.1016/j.ej l.2024.100136
Recei ed 20 Sep embe 2022; Recei ed in e ised o m 27 May 2024; Accep ed 31 May 2024
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
A ailable online 4 June 2024
2192-4376/© 2024 The Au ho (s). Published by Else ie B.V. on behal o Associa ion o Eu opean Ope a ional Resea ch Socie ies (EURO). This is an open access
a icle unde he CC BY license (
h p://c ea i ecommons.o g/licenses/by/4.0/ ).
T. P une e al.
6.1.2. Wo k- es scheduling ................................................................................................................................................................ 15
6.1.3. Shi scheduling ....................................................................................................................................................................... 16
6.2. Wo k o ce managemen .......................................................................................................................................................................... 16
6.2.1. Wo k o ce planning.................................................................................................................................................................. 16
6.2.2. Wo k o ce assignmen ............................................................................................................................................................... 17
7. Ma hema ical p og amming conside a ions........................................................................................................................................................... 17
7.1. Objec i e unc ion .................................................................................................................................................................................. 18
7.1.1. Single objec i e op imiza ion..................................................................................................................................................... 18
7.1.2. Mul i-objec i e op imiza ion...................................................................................................................................................... 19
7.2. Cons ain s ............................................................................................................................................................................................ 20
7.3. Pa ame e s ........................................................................................................................................................................................... 21
7.3.1. Va ying ask ime..................................................................................................................................................................... 21
7.3.2. Va ying quali y ........................................................................................................................................................................ 22
7.3.3. He e ogeneous wo k o ce.......................................................................................................................................................... 22
8. Discussion and u u e esea ch............................................................................................................................................................................ 23
8.1. Discussion on he ela ionship be ween HFs and ields o applica ion ......................................................................................................... 23
8.2. Unde - ep esen ed decision p oblems ...................................................................................................................................................... 23
8.3. Modeling o mode n indus ial p ac ices ................................................................................................................................................. 25
9. Conclusions....................................................................................................................................................................................................... 25
CRediT au ho ship con ibu ion s a emen ........................................................................................................................................................... 26
Decla a ion o compe ing in e es ........................................................................................................................................................................ 26
Acknowledgmen s .............................................................................................................................................................................................. 26
Re e ences......................................................................................................................................................................................................... 26
P eamble
Gi en he la ge olume o ma e ial e iewed, which does no enable
he au ho s o p esen a subs an i e analysis in a single pape , he
cu en li e a u e e iew has been spli in o wo sepa a e pape s (pa s),
ha a e highly connec ed. We e e o he p esen pape as he i s
pa , and o i s win pape (P une e al.,2024) as he second pa .
Al hough an in oduc ion is p esen in bo h pa s, he scope de ini ion,
he posi ioning wi hin he landscape o he ela ed li e a u e, and he
me hodology applied o collec he ele an ma e ial a e only discussed
in de ail in he i s pa . Then, he e ie ed co pus is s udied and
discussed by adop ing h ee di e en poin s o iew:
•Applica ion a ea, whe e we discuss how human ac o s a e ea ed
in each class o p oblem wi hin he logis ics and manu ac u ing
communi ies. This wo k is p esen ed in he i s pa , Sec ions 4–
6. The esul s o he ma e ial collec ion a e p o ided wi h ables
in hese sec ions.
•Human ac o s, whe e we discuss which human ac o s a e in es-
iga ed. Fo each ac o , we s udy he Human-Awa e Models used
o model and quan i y i , and he ad an ages and limi a ions o
such models. This is done in he second pa , Sec ions 3–8.
•Ma hema ical op imiza ion, whe e we discuss he in eg a ion o
human-awa e conside a ions wi hin op imiza ion models. The
s udy ocuses on he impac on he models, in e ms o objec i e
unc ion, cons ain s, pa ame e s, and he associa ed complexi y
bu den. This is done in he i s pa , Sec ion 7.
As an illus a ion, le us conside a pape s udying an assembly line
balancing p oblem ha accoun s o he e gonomic isk ela ed o
awkwa d pos u e by imposing a maximal exposu e a each wo ks a ion.
The pape is discussed in e ms o applica ion as an assembly line in
Sec ion 5.2, in e ms o human ac o s ega ding awkwa d pos u es
in P une e al. (2024) Sec ion 5.3, and in e ms o ma hema ical
op imiza ion o using h eshold cons ain s in Sec ion 7.2. Then, u u e
esea ch di ec ions a e discussed in bo h pa s o he wo k. No e ha ,
in he cu en pape ( i s pa ), we o en e e o human-awa e models
wi hou a p ope explana ion o he ela ed concep s. The in e es ed
eade is e e ed o he second pa (P une e al.,2024) o ge his
in o ma ion.
1. In oduc ion
Ope a ions Resea ch (OR) has been his o ically ocused on eco-
nomic conside a ions, e lec ing he conce ns p esen in he indus y.
Wi h he globaliza ion o he economy, he inc easing le el o compe i-
ion and demand cus omiza ion ha e led o g owing economic p essu e
on decision-make s. This p essu e has been pa icula ly high in he
logis ics and manu ac u ing ields o educe cos s and imp o e he
e iciency o ope a ions, quali y o se ices, and sys em agili y. The
concomi an de elopmen o da a-d i en op imiza ion o ope a ions,
applied o a la ge a ie y o complex indus ial p oblems, has shaped
he cu en landscape o OR. The ad ance in he accessibili y o com-
pu ing powe allows us o conside and success ully ea mo e complex
p oblems ha do no limi hei scope o economic conside a ions. In
his sense, addi ional opical conside a ions ha e begun o appea in
decision models a e he ea ly 2000s, ollowing a mo e global socie al
shi , whe e ecological and social esponsibili y is mo e and mo e ac-
coun ed o by decision-make s, as g owing p essu e om s akeholde s.
This end is illus a ed by he ise o wo ela ed pa adigms namely,
sus ainable de elopmen , and Indus y 5.0, p esen ed in he ollowing
pa ag aphs.
Sus ainable de elopmen . This concep is inc easingly becoming a majo
pa adigm ha e ames he global s a egy o companies and o ga-
niza ions. The concep is de ined as ‘‘ he de elopmen ha mee s he
need o he p esen wi hou comp omising he abili y o u u e gene a-
ions o mee hei own needs’’ (Keeble,1988). The main idea o his
pa adigm is o conside social and en i onmen al indica o s oge he
wi h economic ones when conside ing business decisions and pe -
o mance assessmen s. The h ee pilla s o sus ainable de elopmen ,
namely economic, en i onmen al, and social, a e conside ed on he
same le el o impo ance, explaining he inc easing conce ns owa d
he social esponsibili y o companies. Decen wo king condi ions is
ac ually one o he 17 sus ainable de elopmen goals, ha cons i u e
he 2030 Agenda o sus ainable de elopmen a i ied by he Uni ed
Na ions in 2015.1Acco ding o he In e na ional Labou O ganiza ion
1The esolu ion A/RES/70/1 o he se en ie h gene al assembly o he
Uni ed Na ions o 21 Oc obe 2015 on he 2030 Agenda o sus ainable
de elopmen . h ps://sdgs.un.o g/2030agenda Access: Sep embe 20, 2022.
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
2
T. P une e al.
(ILO)2and he Wo ld Heal h O ganiza ion (WHO),3o e 2.3 mil-
lion people die yea ly om wo k- ela ed causes (Takala e al.,2014).
S udies show ha , a any gi en ime, 20% o employed adul s su e
om Wo k- ela ed Musculoskele al Diso de s (WMSD) (Vézina e al.,
2011). Fu he mo e, om an economic pe spec i e, he di ec and
indi ec cos s o wo k- ela ed inju ies a e es ima ed a 4% o he wo ld
GDP (Takala e al.,2014). The e o e, he imp o emen o wo king
condi ions and he educ ion o wo k- ela ed heal h p oblems a e s ill
an impo an challenge wo ldwide.
Indus y 5.0. This is an eme ging concep e e ing o a change o
pa adigm in he o ganiza ion o logis ics and manu ac u ing sys ems,
which a e he ocus o he p esen wo k. Indus y 5.0 p oposes a shi
om a sys em-cen ic ision o a human-cen ic ision o indus ial
sys ems, by placing he ole and well-being o ope a o s a he cen e o
he p ocess design (Lu e al.,2022). Sus ainabili y and esilience a e he
wo o he pilla s o Indus y 5.0. The conside a ion o human ac o s,
a physical, cogni i e, pe cep ual, and psychosocial le els, is especially
ele an in logis ics and manu ac u ing ields, especially conside ing
he de elopmen o human–machine in e ac ions (Wang e al.,2017),
o he aging o he wo king popula ion (Calza a a e al.,2020).
The need o accoun o human ac o s in decision suppo models
o logis ics and manu ac u ing sys ems will con inue o gain impo -
ance in he nea u u e. Two a gumen s suppo his claim:
•Regula o y aspec : Companies in he logis ics and manu ac-
u ing sec o s s ill ely hea ily on manual labo , especially o
asks equi ing a high deg ee o p ecision and lexibili y. This
is, o example, he case o uck d i e s in logis ics, o de
picke s in wa ehousing (G osse e al.,2015b), o assembly line
ope a o s (Ba ini e al.,2016a). Fu he mo e, hese jobs can
accoun o a high le el o physical and cogni i e s ess, epe i i e
mo emen s, li ing asks, and awkwa d posi ions. The inc eas-
ing demand o social accoun abili y o companies has led o a
g owing numbe o laws egula ing he wo king condi ions in
he indus y. In logis ics, he Eu opean egula ion EC 561/20064
and he Ame ican Hou s o Se ice o D i e s egula ion5bo h
p opose a amewo k o egula e he d i ing and wo king imes
o uck d i e s. The manu ac u ing sec o has also been sub-
jec o nume ous egula ions aimed a educing e gonomic isks
in wo kplaces. Among o he s, one can ci e he Di ec i e No.
2006/42/EC o he Eu opean Pa liamen on machine y usage,6
he Di ec i e No. 89/391/EEC on gene al measu es o encou age
imp o emen s in sa e y and heal h o wo ke s,7o he Occu-
pa ional Sa e y and Heal h Ac o 1970.8Fu he mo e, se e al
e gonomics s anda ds ha e been de eloped by he In e na ional
2Da abase on labou s a is ics o The In e na ional Labou O ganiza ion.
h ps://ilos a .ilo.o g/ Access: Sep embe 20, 2022.
3Wo ld Heal h O ganiza ion Mo ali y Da abase. h ps://www.who.
in /da a/da a-collec ion- ools/who-mo ali y-da abase Access: Sep embe 20,
2022.
4The Eu opean Communi y (EC) egula ion No 561/2006 o 15 Ma ch
2006 on machine wo k. h ps://eu -lex.eu opa.eu/legal-con en /EN/ALL/
?u i=CELEX%3A32006R0561 Access: Sep embe 20, 2022.
5The Fede al Mo o Ca ie Sa e y Adminis a ion egula ion No. 49
CFR Pa s 385, 386, 390 and 395 abou Hou s o Se ice o D i e s.
h ps://www.go in o.go /con en /pkg/FR-2011-12-27/pd /2011-32696.pd
Access: Sep embe 20, 2022.
6The Di ec i e No. 2006/42/EC o he Eu opean Pa liamen and o he
Council o 17 May 2006 on machine y usage. h ps://eu -lex.eu opa.eu/legal-
con en /EN/ALL/?u i=CELEX%3A32006L0042 Access: Sep embe 20, 2022.
7The Eu opean Council Di ec i e No. 89/391/EEC o 12 June 1989 on
he in oduc ion o measu es o encou age imp o emen s in he sa e y and
heal h o wo ke s a wo k. h ps://eu -lex.eu opa.eu/legal-con en /EN/ALL/
?u i=CELEX%3A31989L0391 Access: Sep embe 20, 2022.
8The Occupa ional Sa e y and Heal h Ac o 1970 h ps://www.osha.go /
laws- egs/oshac /comple eoshac Access: Sep embe 20, 2022.
O ganiza ion o S anda diza ion and he Eu opean Commi ee on
S anda diza ion o design p oduc ion sys ems. Dul e al. (2004)
lis ed up o 174 in e na ional e gonomic s anda ds o p oduc ion
sys ems, going om gene al ecommenda ions on he design o
p ocesses o speci ic equi emen s o manual handling, human–
compu e in e ac ions, men al load, noise, hea , e c. The e o e,
planning models inc easingly need o accoun o human ac o s
in o de p oduce indus ially cohe en solu ions.
•In eg a ion aspec : The second d i e o he ele ance o hu-
man conside a ions in logis ics and manu ac u ing e e s o he
modeling o human cha ac e is ics and beha io s. Se e al empi -
ical s udies ha e iden i ied a clea link be ween he e gonomic
bu den and indi idual pe o mance in he indus y (see e.g., Shik-
da and Sawaqed (2003), E dinç and Yeow (2011) and I a sson
and Eek (2016)). In addi ion o poo e gonomic condi ions, nu-
me ous o he ac o s can impac he p oduc i i y o employees.
These include a igue (Yung e al.,2020), noise exposu e (Szalma
and Hancock,2011), indi idual lea ning (G osse e al.,2015a),
s ess and psychosocial ac o s (Halkos and Bousinakis,2010),
e c. O e all, he s udy o human ac o s is essen ial o unde s and
and model he p oduc i i y o employees accu a ely. Logis ics
and manu ac u ing a e hea ily elying on he op imiza ion o
hei ope a ions, i is, he e o e, c ucial o model as accu a ely
as possible he du a ion o he ope a ions. This will gain e en
mo e impo ance wi h he sp ead o he Indus y 5.0 pa adigm.
O ien ed owa d p ocess au oma ion, human– obo collabo a ion
is becoming mo e common in p oduc ion sys ems, whe e human
ope a o s and obo s wo k on he same wo ks a ions. A ho -
ough comp ehension o he modeling o human cha ac e is ics is
c ucial o he success o such sys ems (Wang e al.,2017).
F om an academic pe spec i e, he issue o imp o ing wo king
condi ions has been mainly ackled by Human Fac o s and E gonomics
(HF/E), as a human-cen e ed scien i ic discipline aiming a a be e
unde s anding o he in e ac ions be ween human ope a o s and p o-
duc ion o se ice sys ems (see Sec ion 2). HF/E lies a he in e ace
be ween psychology, an h opome y, physiology, mechanics, design,
and enginee ing (B idge ,2018). This in e -disciplina i y esul s in a
a ie y o me hods used o e alua e wo k si ua ions, om obse a-
ional me hods o s a is ics, passing by biomechanics, sys em heo y,
epidemiology, and occupa ional medicine. A numbe o hese me hods
a e p esen ed in he second pa (P une e al.,2024) o his e iew.
Howe e , OR and ma hema ical p og amming ha e no been pa o
he e gonomis oolbox un il he beginning o he 21s cen u y. To ou
bes knowledge, he pionee ing wo k o Ca nahan e al. (2000) was
he i s o use OR as a ool o e gonomic pu poses o op imize a job
o a ion schedule (see Sec ion 6.1.1).
In he OR li e a u e, economic conside a ions ha e his o ically been
la gely dominan (Dekke e al.,2012). Howe e , o he ends ha e
eme ged ecen ly. Ecological conside a ions ha e made hei way in o
he op imiza ion li e a u e, e.g., wi h g een logis ics (Dekke e al.,
2012;Sbihi and Eglese,2010), sus ainable manu ac u ing ope a ions
scheduling (Gi e e al.,2015) o ci cula economy in p oduc ion
planning (Suzanne e al.,2020). Human-awa e conside a ions ha e also
been conside ed o a couple o decades, om di e en pe spec i es,
wi h di e en deg ees o modeling accu acy. I is, howe e , s ill a
ma u ing opic ha can be di icul o ge a g asp o o a newcome ,
add essed by di e en esea ch communi ies wi hin OR ia a ious
app oaches. Human ac o s a e hence qui e challenging o in eg a e
in o exis ing op imiza ion models o se e al easons:
•A he p oblem s a emen le el: Gi en an indus ial si ua ion, iden-
i ying he mos ele an human ac o s o conside is challenging
wi hou su icien ield expe ience.
•A he modeling le el: Once he ele an ac o s a e iden i ied, i
is nei he easy no ob ious o p opose app op ia e quan i a i e
modeling.
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
3
T. P une e al.
•A he ins ance le el: The da a collec ion on his pa o en equi es
ex ensi e ield obse a ions and measu emen s, o dedica ed lab
s udies.
•A he solu ion le el: The in eg a ion o human conside a ions
o en has a signi ican impac on p oblem complexi y. Tools
and amewo ks commonly used in he HF/E communi y do no
usually ha e con enien ma hema ical p ope ies such as linea i y
o con exi y.
This e iew aims a b idging he unde s anding gap o OR academics
and p ac i ione s abou HF/E me hods, and a p o iding hem wi h a
b oad unde s anding o he ela ed p oblems, by p esen ing he mod-
els, me hods, and ools used in he li e a u e o design human-awa e
op imiza ion me hods o logis ics and manu ac u ing sys ems.
Due o he leng h o he manusc ip , he p esen wo k is di ided
in o wo sepa a e pape s. A e he scope delinea ion, he i s pa
o his e iew ( he cu en pape ) conduc s a quan i a i e and he-
ma ic analysis o he e ie ed co pus acco ding o a semi-sys ema ic
me hodology. Sec ion 2p o ides he ela ed backg ound o he p esen
wo k wi h espec o he exis ing li e a u e and de ines clea ly he
scope o he e iew. Sec ion 3p esen s he sea ch me hodology used
o he ma e ial collec ion. Sec ions 4 o 6gi e an o e iew o he
e ie ed li e a u e co pus s uc u ed by class o p oblems encoun e ed
in logis ics and manu ac u ing sys ems, namely: logis ics (wa ehousing
and ehicle ou ing), p oduc ion sys ems (p oduc ion scheduling, as-
sembly line balancing, p oduc ion planning, and sys em design), and
wo k o ce- ela ed p oblems (wo k o ce scheduling and managemen ).
Sec ion 7is specially dedica ed o in es iga ing he impac o human-
awa e conside a ions on ma hema ical models. Pa icula a en ion
is pu on he ea u es added o an op imiza ion model (e.g., new
cons ain s, modi ica ion o he objec i e unc ion) o accoun o he
human ac o s, and he impac on solu ion me hods. Sec ion 8discusses
he main inding o his i s pa , and p oposes esea ch di ec ions.
Finally, Sec ion 9concludes his i s pa o he e iew. In he second
pa o his wo k (P une e al.,2024), he ocus is se on he modeling
o human cha ac e is ics. The di e en modeling cons uc s ound in
he ela ed li e a u e a e p esen ed and con ex ualized. P une e al.
(2024) has been w i en wi h he idea o being used as a su ey o
he di e en modeling o human ac o s o in e es ed academics and
p ac i ione s.
2. Te minology, ela ed backg ound, scope, and mo i a ions
In his sec ion, he p esen e iew is pu in con ex and mo i a ed
wi h espec o he li e a u e. Fi s , we p opose comp ehensi e de i-
ni ions o he human- ela ed key concep s used h oughou his wo k.
Then we su ey and syn hesize ou indings om he exis ing ela ed
e iews. F om his analysis, we p opose a clea delinea ion o he scope
o his e iew.
2.1. De ini ions o human- ela ed key concep s
In he OR li e a u e, he key e ms ela ed o human-awa e op i-
miza ion a e ound wi h a ious de ini ions and in e p e a ions, he
mos p ominen one being he e m human ac o s. A nai e in e p e-
a ion om an OR p ac i ione would de ine i as any cha ac e is ic o
beha io ela ed o he human na u e o employees. Howe e , Human
Fac o s and E gonomics also e e s o a scien i ic discipline, de ined as
ollows by he In e na ional E gonomics Associa ion9:
9The In e na ional E gonomics Associa ion (IEA). h ps://iea.cc/wha -is-
e gonomics/ Access: Sep embe 20, 2022.
E gonomics (o human ac o s) is he scien i ic discipline conce ned
wi h he unde s anding o in e ac ions among humans and o he ele-
men s o a sys em, and he p o ession ha applies heo y, p inciples,
da a, and me hods o design in o de o op imize human well-being
and o e all sys em pe o mance. The e ms e gonomics and human
ac o s a e o en used in e changeably o as a uni (e.g., human
ac o s/e gonomics – HF/E o E/HF), a p ac ice ha is adop ed by
he IEA.
As he opic o his e iew is p one o c oss-disciplina y wo k, we
s ess he impo ance o a oiding he con usion be ween human ac o s
and HF/E o acili a e mu ual comp ehension be ween esea che s wi h
di e en backg ounds. In e es ed eade s can ind a mo e ho ough
de ini ion and explana ions on HF/E in Wogal e e al. (1998). S a ing
om his cla i ica ion, le us adop he ollowing con en ion o he
emainde o his pape :
•Human Fac o s and E gonomics (HF/E): as a scien i ic disci-
pline ha s udies he in e ac ions be ween a human ope a o and
his/he wo k sys em, emphasizing he imp o emen o wo king
condi ions. HF/E esea ch amewo ks usually iden i y ou c i -
ical elemen s in human–sys em in e ac ions: physical ela ed o
he human body, men al ela ed o he ask cogni i e complexi y,
pe cep ual ela ed o he human pe cep ions (e.g., hea ing, sigh ,
e c.) and psychosocial ela ed o he in e ac ions wi h colleagues
and managemen (Sal endy and S ,2022).
•Ope a ions Resea ch (OR): as a scien i ic discipline ha aims a
p o iding be e decisions by he analysis and modeling o com-
plex sys ems, and he de elopmen o e icien decision-suppo
algo i hms based on ad anced ma hema ical me hods. A ypical
OR wo k in ol es op imizing he pe o mances o a wo k sys em
h ough he ollowing s eps: 1. he obse a ion and analysis o
a wo k en i onmen , 2. he de elopmen o a simpli ied ma he-
ma ical model o his en i onmen , 3. he design o an e icien
algo i hm o sol e he p oblem, and 4. he indus ial e alua ion
o he e u ned solu ion.
•Wo k- ela ed Musculoskele al Diso de (WMSD): as an inju y
o a disease ha a ec s he body’s s uc u al sys ems (i.e., he
bones, issues, ne ous o ci cula o y sys ems), caused by a wo k
si ua ion. Examples o WMSDs include (bu a e no limi ed o) low
back pain, ca pal unnel synd ome, endini is, o igge inge .
WMSDs encompass a la ge po ion o wo k- ela ed inju ies, bu
some do no a ec he musculoskele al sys em (e.g., hea ing loss,
excessi e hea exposu e).
•E gonomic Risk (ER): e e ing o he addi ional isk ac o o
de eloping WMSD, o any o he occupa ional acciden , disease,
o inju y, o an employee due o a speci ic wo k si ua ion. This
concep is ound unde di e en names in he li e a u e, wi hou a
clea di e ence: e gonomic load, e gonomic wo kload, e gonomic
bu den, e gonomic s ain, e c.
•Risk Fac o s: as wo kplace si ua ions ha cause wea and ea o
he body, and inc ease he e gonomic isk o employees. These
include epe i ion, awkwa d pos u e, li ing, noise, wo k s ess,
e c.
•AHuman Fac o (HF): as a cha ac e is ic o beha io ypically
human. This can be a ac o a ec ing he indi idual pe o mance
o wo ke s and he abili y o pe o m asks (e.g., a igue, lea ning,
o ge ing), he sa e y o wo ke s (e.g., wo k- ela ed inju ies, awk-
wa d pos u es), he in e es s o wo ke s (e.g., sa is ac ion, mo i a-
ion), o any o he eal-li e human cha ac e is ic in e ac ing wi h
he op imiza ion o a p oduc ion o se ice sys em.
•AHuman-Awa e Model (HAM): as a quan i a i e model ha
explici ly ansla es a HF in o a ma hema ical exp ession, which
can, in u n, be used in an op imiza ion p oblem (e.g., in a
cons ain o as an objec i e unc ion). In o he e ms, he con-
cep o HAM is he ansposi ion o HF/E me hods in o an OR
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
4

T. P une e al.
con ex . A ypical HAM can be exp essed as a unc ion mapping
he di e en s a es o a human–sys em o a se o eal numbe s,
which can be in e p e ed as he ‘‘e gonomic sco e’’ o he s a es.
Fo ins ance, he NIOSH equa ion is a HAM ha e alua es he
e gonomic isk ela ed o manual handling. Based on a se o
c i e ia (e.g., weigh , handling pos u e, du a ion), a sco e can be
compu ed o each handling ask ha needs o be scheduled in a
shi and used as inpu o a ela ed op imiza ion p oblem.
2.2. P e ious e iews
The in eg a ion o a HF in op imiza ion p oblems is a b oad subjec .
As such, se e al e iews/su eys ha e been published on he opic.
Howe e , hese wo ks o en ocus on a single opic o logis ics and
manu ac u ing, ye wi h a scope b oade han pu e op imiza ion p ob-
lems, i.e., including ma e ial om o he disciplines, such as indus ial
enginee ing o HF/E. In his sec ion, a b ie o e iew o he mos
ele an e iews is p o ided.
•Wa ehousing: (G osse e al.,2015b,2017b) s udy he in eg a ion
o HFs in o de picking (i.e., he ac ion o e ie ing, o ‘‘picking’’,
i ems om he s o age a ea o p epa e o de s) models. G osse
e al. (2017b) pe o m a con en analysis o he e ie ed li e -
a u e, and hus gi e a b oad o e iew o he opics o in e es
and ela ed ends. Wi h a igh e scope, G osse e al. (2015b)
place he emphasis on planning models in o de picking, by
p esen ing a sys ema ic me hodology and in oducing a new con-
cep ual amewo k and axonomy o classi y and analyze his
esea ch s eam. De Lombae e al. (2022) s udy human aspec s
in he scope o o de picking planning models, wi h a ocus
on he in eg a ion in o ma hema ical models and insigh s om
semi-s uc u ed in e iews o p ac i ione s.
•Vehicle ou ing p oblem: Vega-Mejía e al. (2019) e iew he
VRP wi h iple bo om line sus ainable objec i es, one o hose
being social. Howe e , he pape ocuses mo e on economic and
ecological aspec s, as hey a e mo e ep esen a i e o hei e iew
co pus, he social aspec only being b ie ly discussed. Ma l e al.
(2018) p opose a e iew on wo kload equi y in he VRP and
p o ide an in-dep h analysis on he opic.
•P oduc ion scheduling: As e lec ed by he e iews on he opic,
he scheduling communi y has been qui e p oli ic in he con-
side a ion o HFs in o hei ma hema ical models. The eade is
e e ed o Lod ee e al. (2009) o a gene al na a i e e iew
abou Human-Awa e Modeling in he scheduling domain. Lod ee
e al. (2009) deal wi h wo ks on he opic published p io o
2007, including bo h OR- ela ed and empi ical HF/E s udies, and
p o ide an insigh ul analysis and a modeling amewo k on he
sequencing o human asks. The in oduc ion o lea ning aspec s
has also been ex ensi ely s udied by he scheduling communi y,
om he ea ly wo ks on lea ning in managemen science o he
mos ecen de elopmen o his s eam. Biskup (2008) p o ides
a e iew on scheduling in eg a ing lea ning join ly wi h a el-
e an analysis o he p ac ical aspec s o lea ning, as well as
a discussion on he ma hema ical implica ions o he di e en
ways o model such e ec s. Azzouz e al. (2018) p esen an
upda e o he e iew o Biskup (2008), and p opose a axonomy
and a classi ica ion amewo k o map he ela ed a eas o he
p oli ic s eam o scheduling wi h lea ning e ec s. F om an OR
pe spec i e, Cheng e al. (2004) p esen a sho su ey on he
ma hema ical implica ions o ime-dependen p ocessing imes on
scheduling models.
•Assembly lines: Assembly lines a e also a well-es ablished e-
sea ch opic o human-awa e op imiza ion, due o he inhe en
balance o he wo kload be ween wo ke s, which can be seen as
a balance o he ER. O o and Ba aia (2017) p opose a sys ema ic
e iew on line balancing and job o a ion o educe he physical
ER in assembly. The au ho s p o ide an analysis o bo h he HAMs
a ailable o assembly line p ac i ione s, and he ma hema ical
pe spec i e o in eg a ing such conside a ions in exis ing models.
Albei no a e iew pape , Pel oko pi e al. (2014) p o ides a
ne wo k analysis o he di e en pe o mance ac o s o assembly
lines.
•Lo sizing: Jabe e al. (2022) e iew he in eg a ion o lea ning
cu es in lo sizing models. The ocus is pu on he di e en
lea ning cu e models and how hey a e in eg a ed in o he
models. The esul s a e p esen ed ch onologically o highligh he
de elopmen o he ield.
•Wo k o ce planning: This class o p oblems has also been s ud-
ied, especially wi h skill conside a ions. De B uecke e al. (2015)
p esen a s a e-o - he-a e iew on wo k o ce planning wi h
skills, ocusing on bo h ma hema ical modeling and manage ial
implica ions. The au ho s also p opose a new axonomy o his
esea ch s eam, based on wha skills a e exac ly modeling, and
how hey a e in eg a ed in o decision models. Thei axonomy
cons i u es he basis o ou analysis o skills in (P une e al.
(2024), Skills). Xu and Hall (2021) s udy a igue in wo k o ce
scheduling in a b oad sense. They ocus on he wo k- es schedul-
ing and shi scheduling p oblems accoun ing o human a igue
and de i e insigh s om empi ical s udies and oppo uni ies o
OR applica ions.
•Re iews ocusing on speci ic HFs: Se e al wo ks e iew he
li e a u e ocusing on a gi en HF ins ead o an a ea o logis ics
and manu ac u ing. Ka su and Mo on (2015) su ey he issue o
equi y in OR models wi h an emphasis on he modeling implica-
ions. Anzanello and Foglia o (2011) p o ide a na a i e e iew
on lea ning cu es, wi h a ocus on ma hema ical models. Glock
e al. (2019b) p opose a sys ema ic e iew on he applica ions
o leaning cu es in ope a ions managemen , and G osse e al.
(2015a) pe o med a me a-analysis o empi ical s udies on he use
o lea ning cu es in a p oduc ion con ex . Ka i aee e al. (2021)
e iew he li e a u e modeling he e ogeneously he wo k o ce in
p oduc ion sys ems. Finally, se e al e iews s udy he in eg a ion
o HFs in logis ics and manu ac u ing sys ems wi h an HF/E poin
o iew, wi hou conside ing ma hema ical models: Padula e al.
(2017) on job o a ion o p e en WMSD, Kolus e al. (2018) on
human e o s and hei implica ions, Jahanmahin e al. (2022a)
on human– obo collabo a ion, and Muhs e al. (2018) on he
empo al a iabili y o asks pe o med by a human ope a o .
To conclude, one can see ha he in eg a ion o HFs in he op imiza-
ion o logis ics and manu ac u ing sys ems a e ex ensi ely s udied and
e iewed. Howe e , mos o hese e iews ocus ei he on a single opic,
o hey conduc he s udy only h ough an OR o HF/E pe spec i e,
hus gi ing a es ic ed iew o his in e disciplina y opic. In he
p esen pape , we aim a b oadening he scope o gi e a gene al pic u e
and a mo e holis ic analysis o he subjec , ha has no been done
p e iously in he li e a u e. In ha sense, he p esen wo k can be
seen as an ex ension o p e ious wo ks on HF an ope a ions manage-
men (La co Ma inelli,2010;Neumann and Dul,2010), bu wi h a
clea ocus on op imiza ion. G osse e al. (2017a) p o ide a i s a emp
a such a wo k, ye he es ic ed leng h o he pape does no allow he
au ho s o p o ide an in-dep h analysis.
2.3. Scope
The p esen wo k aims a su eying he esea ch s eam dedica ed o
he in eg a ion o HF in he op imiza ion o logis ics and manu ac u ing
sys ems. As a consequence, o be ele an o his wo k, a pape mus
deal wi h:
I. Op imiza ion. Despi e being a he in e ace be ween se e al disci-
plines, we chose o e iew only he pape s p esen ing an op imiza ion
model, ei he explici ly o implici ly, bu clea ly de ined.
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
5
T. P une e al.
II. logis ics o manu ac u ing. Acco ding o he Camb idge Business En-
glish Dic iona y (P ess,2011), logis ics and manu ac u ing a e de ined
as ollows:
Logis ics: The p ocess o planning and o ganizing o make su e ha
esou ces a e in he places whe e hey a e needed so ha an ac i i y
o p ocess happens e ec i ely.
Manu ac u ing: The business o p oducing goods in la ge numbe s,
especially in ac o ies.
These de ini ions co e he classical opics o OR, including plan-
ning and scheduling, when applied o a p oduc ion p ocess. Howe e ,
o he side ac i i ies enabling manu ac u ing o be p ocessed, e ec i ely
and e icien ly, a e also co e ed by hese de ini ions, namely: in en o y
managemen , wa ehousing, ehicle ou ing, and lo sizing ope a ions.
III. Human ac o s. We conside ha a pape i s in he scope i i
conside s he modeling o a HF o deals wi h he imp o emen o he
employees’ wel a e.
2.4. Mo i a ions
The esea ch s eam on human-awa e op imiza ion in logis ics and
manu ac u ing sys ems is consis en ly g owing. Howe e , he ela ed
opics a e s udied h ough he lens o a speci ic ield o applica ion,
o a speci ic human ac o . The main goal o his pape is o ill his
gap by p o iding a holis ic pic u e o his b oad opic, and a cohe en
analysis o he esea ch s eam. The second objec i e is o p opose
o in e es ed p ac i ione s a comp ehensi e o e iew o he a ailable
models o ep esen and quan i y human cha ac e is ics, bo h om
he HF/E and OR pe spec i es. These goals a e exp essed h ough he
ollowing esea ch ques ions add essed h oughou bo h he cu en
pape and he second pa o he wo k (P une e al.,2024):
•Which HFs a e s udied in he ela ed li e a u e, and in which
logis ics and manu ac u ing a eas a e hey p esen ?
•How a e HFs modeled in he ela ed li e a u e, and which HAMs
a e a ailable o ake hem in o accoun ?
•How a e HAMs in eg a ed in o op imiza ion models, and how
does hei in eg a ion impac he models om a ma hema ical
p og amming pe spec i e?
The wo pa s o he wo k a e in ended as a ‘‘guidance- ype’’ p esen-
a ion o human-awa e op imiza ion, o help esea che s a iden i ying
ele an human ac o s o hei p oblems, app op ia e quan i a i e
models o hese ac o s, and e e ences o u he heo e ical and
applied wo ks on he opic.
3. Me hodology
3.1. Ma e ial collec ion me hodology
This sec ion p esen s he me hodology ollowed o collec he ma-
e ial o his e iew. The mos common ypes o me hodological ap-
p oaches used o pe o m a li e a u e e iew a e: sys ema ic, semi-
sys ema ic, na a i e, in eg a i e, and me a-analysis (Snyde ,2019).
Mo i a ed by he la ge numbe o pape s i ing he scope de ined in
Sec ion 2.3, we op o a semi-sys ema ic me hodology. In sho , he
sea ch me hodology is sys ema ic, o ensu e ha all ele an opics
a e co e ed by he e iew. Howe e , he amoun o ma e ial does
no ealis ically allow a sys ema ic analysis o e e y in-scope wo k,
and hus he analysis and w i ing a e na a i e. This means ha o
some opics (speci ically, pape s dealing wi h skills and/o lea ning),
he decision o include hem in scope is made a he disc e ion o he
au ho s. The eade in e es ed in sys ema ic e iews on speci ic opics is
e e ed o Sec ion 2.2. Ne e heless, one should no e ha , e en hough
Table 1
Lis o keywo ds in he sea ch s ing.
Logis ics and
manu ac u ing
Human-awa e modeling
wa ehous*, s o age,
pick*, ba ch*, assembly,
p oduc ion, ‘‘lo sizing’’,
manu ac u *, planning,
ou ing, logis ic*,
schedul*, ma i ime
AND musculoskele al, wel a e, pos u *, li *,
‘‘human ac o *’’, e gonomic*, ‘‘human
e o *’’, ‘‘lea ning cu e’’, ‘‘lea ning
e ec ’’, ‘‘ o ge ing’’, ‘‘human lea ning’’,
pain, noise, bo ed*, a igue, b eak, es ,
cogni i e, equi y, sa e, discom o ,
u no e , absen eeism, ib a ion,
epe i *, inju y, ‘‘social bene i *’’, skill*,
psychosocial, ‘‘wo kload smoo hing’’
a numbe o pape s a e excluded, he sys ema ic sea ch me hodology
ensu es ha all ele an opics and modeling app oaches a e co e ed
in his pape . Wi h he same philosophy, we highligh ha he leng h o
a gi en sec ion does no necessa ily e lec he bibliome ic impo ance
o he co e ed opic.
The ma e ial is collec ed using Scopus and Web o Science
da abases, by applying he ollowing se o ules and selec ion c i e ia
(SC):
•Acco ding o he scope o his e iew, wo se s o keywo ds a e
c ea ed, one o logis ics and manu ac u ing, and one o human-
awa e modeling. A sea ch que y is c ea ed o he da abases wi h
one wo d om each o he wo se s, p esen ed in Table 1. Follow-
ing a p epa a o y scan o he li e a u e, he lis o keywo ds has
been c ea ed a he disc e ion o he au ho s wi h he objec i e
o co e ing as many scien i ic wo ks as possible dealing wi h
Human-Awa e Logis ics and Manu ac u ing Sys ems.
•Only pape s published in English a e conside ed.
•All publica ion da es a e conside ed up o 2022.
•(SC1) A ca ego y il e is applied on bo h da abases o a ge he
ele an pape s. On Scopus, he sea ch is limi ed o Compu e
Science and Decision Science ca ego ies. On Web o Science, he
ollowing ca ego ies a e conside ed: Compu e Science (Theo y
me hods, a i icial in elligence & in e disciplina y applica ions),Op-
e a ions esea ch and managemen science,Enginee ing Indus ial,
Managemen ,T anspo a ion,T anspo a ion science echnology &
Ma hema ics in e disciplina y applica ions.
•(SC2) Only a icles published in jou nals and book chap e s a e
conside ed. Con e ence p oceedings a e excluded om his s udy
in o de o keep he size o he s udied co pus manageable.
•(SC3) F om he collec ed lis o pape s, duplica es a e emo ed,
and we only e ain he wo ks ha sa is y he ollowing h ee con-
di ions based on hei i le, keywo ds, and abs ac , in acco dance
wi h wha is p esen ed in Sec ion 2.3:
1. The pape p o ides an op imiza ion ma hema ical model,
o an op imiza ion solu ion me hod.
2. The pape explici ly conside s Human-Awa e Modeling in
i s i le, abs ac , o keywo ds. The men ion o HF o social
wel a e is explici . Mo eo e , hese aspec s a e explici ly
modeled.
3. The pape deals wi h logis ics o manu ac u ing.
As a esul , a o al o 844 pape s a e e ie ed a e he applica ion
o (SC3).
•(SC4) A e a ho ough eading, only ele an pape s a e included.
S udies dealing wi h skills and lea ning include a lo o pape s, and
we decided o exclude some o hem. E en i he exclusion p ocess
is no ep oducible, he ollowing guidelines a e applied o he
pape selec ion:
1. Pape s alling in he scope o he e iew and ocusing hei
con ibu ion on Human-Awa e Modeling a e sys ema ically
included. This ensu es ha e e y ele an assessmen o
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
6
T. P une e al.
Fig. 1. P isma low diag am o he li e a u e sea ch and inclusion p ocess.
modeling app oach ound in he li e a u e is add essed by
he p esen e iew.
2. Pape s ocusing hei esea ch con ibu ions on he algo-
i hmic side, and using a basic ad hoc HAM a e mo e
likely o be excluded. Fo example, when ackling he
opic ela ed o skills, he majo i y o pape s use a simple
cons ain o impose he compa ibili y be ween asks o
schedule and wo ke s ha a e allowed o pe o m hem.
Despi e i s as use, his simple HAM p esen s a limi ed
in e es in he scope o his e iew, hus mos o he pape s
p oposing i a e no included.
A e applying (SC4), 504 pape s a e excluded. The inal co pus
hus includes 340 pape s, which a e s udied and discussed in he
cu en wo k.
Fig. 1 displays he low o he selec ion p ocess. The alue o
𝑛 ep esen s he numbe o pape s esul ing om he co esponding
sea ch.
S uc u e o he pape . Sec ions 4–6p o ide a p esen a ion o he col-
lec ed ma e ial, decomposed by ield o applica ion wi hin logis ics and
manu ac u ing. Th ee main a eas o applica ion a e iden i ied: logis ics
sys ems,manu ac u ing sys ems, and wo k o ce- ela ed p oblems ha could
be applied in a ious con ex s. Each sec ion is hen decomposed in o
speci ic subsec ions. No e ha his s uc u e aims a easing he ead-
ing, by decomposing he p esen a ion o he collec ed ma e ial in o
sec ions o simila leng h. No e ha we do no aim a in oducing a
consis en classi ica ion o he ield. An induc i e app oach has been
used o he classi ica ion: s udies a e clus e ed based on he simila i y
o he p oblem s udied in e ms o ield o applica ion, ime ho izon,
main decision a iables, and p oblem s uc u e. The e o e an o e lap
is p esen among some sec ions, and some wo ks could be classi ied
in o se e al ca ego ies. Howe e , we decided o include each pape
in a single sec ion, whe e i seemed he mos ele an . The axonomy
applied in di e en sec ions elies on p e ious li e a u e s udies when
a ailable, o example, he wo k o Lod ee e al. (2009) o wo k o ce
scheduling p oblems.
3.2. Summa y ables: Fo ma and con en ion
The collec ed ma e ial is p esen ed h ough summa y ables in he
nex sec ions. To acili a e na iga ion, he ables’ en ies e e o he
sec ions o bo h his pape and he second pa (P une e al.,2024).
The ables con ain he ollowing columns:
•Re e ence: The e e ence o he pape in he bibliog aphy.
•Case s udy: This column indica es i he wo k is applied o a
eal-li e case s udy.
•Modeling: This column speci ies how he HAM used in he pape
is in eg a ed in o he op imiza ion model. The di e en op ions
co espond o he subsec ions o Sec ion 7, and include MO
o ue mul i-objec i e, MO-He o a he e ogeneous agg ega-
ion o se e al objec i e unc ions, MO-Ho o a homogeneous
agg ega ion o se e al objec i es, and SO o a single objec i e
unc ion. Conce ning he cons ain s, CC is used o compa i-
bili y cons ain s, TC o h eshold cons ain s, TWC o ime
window cons ain s, FSC o o bidden sequence cons ain s, and
FBC o o wa d–backwa d cons ain s. Conce ning he da a, VTT
designa es a a iable ask ime, and VQ a a iable quali y.
•He e ogeneous wo k o ce (HW): This column is dedica ed o
he modeling o he wo k o ce. Mo e p ecisely, we check i he
wo k o ce is modeled he e ogeneously o no , i.e., i di e en
employees a e modeled wi h di e en cha ac e is ics, o i hey
a e all in e changeable. I he modeling is he e ogeneous, his
column speci ies which pa ame e is a de ining cha ac e is ic o
an employee. The ange o op ions includes he skills S, he lea n-
ing a e LR, physical p ope ies PP, cogni i e and psychosocial
p ope ies CPP, and p e e ences P.
•HAM: This column indica es he quan i a i e model used in he
pape o ep esen he conside ed human ac o . The iden i ied
op ions co espond o he sec ions o he second pa o his
e iew (P une e al.,2024).
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
7
T. P une e al.
Table 2
Wa ehousing e e ences (see Sec ion 3.2 o he able legend).
Re e ence Case Modeling HW HAM
s udy Obj. Cons . Pa am.
Ma el e al. (2001)✓CC Handling
Hwang e al. (2003) SO Biomechanical
G osse e al. (2013) VTT LR Lea ning
G osse and Glock (2015) VTT LR Lea ning
Ba ini e al. (2016b) MO EE, RA
Calza a a e al. (2017) MO EE
O o e al. (2017) SO Handling
Ba ini e al. (2017b)✓S Pos u e, RA
Al-A aidah e al. (2017) Visual, an h opome ic
La co e al. (2017)✓MO Expe collabo a ion
Ma usiak e al. (2017)✓VTT S Skills
Hong (2018) VTT S Skills
Die enbach and Glock (2019) MO EE
Glock e al. (2019a)✓MO Biomechanical
Calza a a e al. (2019a)✓MO EE, pos u e
Zhang e al. (2019a)✓VTT PP, LR Skills, lea ning
Kudelska and Pawłowski (2020) MO Implici
Gajsek e al. (2021)✓MO-He EE, pos u e
Feng and Hu (2021)✓VTT S Skills, a igue e ec
Gajšek e al. (2021) MO EE, pos u e
Kapou e al. (2022)✓MO-He CC Handling
Mou (2022) MO VTT S Skills, lea ning, equi y
4. Logis ics sys ems
4.1. Wa ehousing
Wa ehousing ac i i ies, and especially o de picking, s ill hea ily
ely on manual labo (de Kos e e al.,2007), and he wo k can
be physically demanding. The in eg a ion o HFs in o ma hema ical
models has he e o e gained a en ion om OR esea che s on his
opic, as p esen ed in Table 2. In his con ex he mos common HFs a e
he isk o WMSDs, employees lea ning, and a igue. An in e es ed eade
is e e ed o G osse e al. (2015b) and De Lombae e al. (2022) o
dedica ed e iews on he opic.
WMSD isk assessmen . An o de picke is subjec o a ious isk ac o s
o WMSDs linked o he ma e ial handling ac i i y, especially he
equen li ing o p oduc s (O o e al.,2017;Kapou e al.,2022),
and he awkwa d pos u es equi ed o pe o m he picks, bo h o
he hand/w is and he en i e body (Al-A aidah e al.,2017;Glock
e al.,2019a;Calza a a e al.,2019a). S udies aim a mi iga ing his
isk a di e en le els o decisions. Fi s , ega ding he design o he
picking a ea layou , he e is he possibili y o o a ing he palle s o
enable easie access o he s o ed i ems (Glock e al.,2019a;Calza a a
e al.,2019a) use he OWAS me hod o e alua e di e en si ua ions.
The op imiza ion o he s o age decisions also plays an impo an
ole in ER o he ope a o s. O o e al. (2017) in eg a e s o age and
zoning decisions o balance he ER among employees, and Kudelska
and Pawłowski (2020) s udy he impac o s o age decisions using
simula ion. The picke ou ing decisions a e s udied, o example, by Al-
A aidah e al. (2017) wi h he goal o clus e ing he i ems so ha he
picke (on a ehicle) can sa ely pick se e al p oduc s.
Lea ning. G osse e al. (2013) and G osse and Glock (2015) s udy he
impac o employee lea ning and o ge ing on s o age assignmen and
eassignmen decisions in he wa ehouse. The opic has been applied o
omni-channel supe ma ke s, whe e he o de picking is in eg a ed wi h
ba ching o deli e y decisions (Zhang e al.,2019a;Mou,2022).
Fa igue wi h ene gy expendi u e. Due o he a ie y o elemen a y asks
pe o med by a picke (e.g., walking, picking, pushing a olley),
a holis ic HAM such as ene gy expendi u e is sui ed o he con-
ex . Calza a a e al. (2017) and Calza a a e al. (2019a) use he ene gy
expendi u e in hei models o in eg a e he choice o picking om a
ull palle , o om hal palle s on he loo and uppe shel es, balancing
e iciency and ER. This HAM is o en conside ed when op imizing
he s o age decisions, whe e an e gonomic objec i e is used on op
o he classical picking e iciency op imiza ion (Ba ini e al.,2016b;
Die enbach and Glock,2019;Gajsek e al.,2021;Gajšek e al.,2021).
O he HFs. La co e al. (2017) use a eg ession om a wo ke opinion
su ey on he discom o o he picking ope a ions in di e en si ua-
ions as an inpu o hei model, and Ma usiak e al. (2017) conduc a
eg ession analysis om his o ical da a o de e mine he di e en skills
and skill le els o he employees. Wo kload equi y among picke s is also
conside ed (Mou,2022).
4.2. Vehicle ou ing
The Vehicle Rou ing P oblem (VRP) and i s ex ensions ep esen an
impo an class o p oblems s udied in he OR li e a u e, whe e he aim
is o assign a se o cus ome isi s o ehicles while op imizing he
o al a eled dis ance. Table 3 p esen s he wo ks on he VRP in he
scope o his e iew. Two main HFs a e s udied in VRP: he a igue o
d i e s, and he equi y be ween hem. The opic o d i e s’ a igue is
ackled ei he om a egula ion pe spec i e, wi h he Hou s o Se ice
(HOS) egula ions, o wi h an app oach ocused on es b eaks.
Fa igue wi h HOS egula ions. Recen egula ions en o ce he explici
scheduling o es b eaks in ou ing plans o logis ics p o ide s (see
P une e al. (2024), Regula o y b eaks). These HOS egula ions lead
o he de elopmen o VRP wo ks on he opic. These wo ks aim a
op imizing a ou ing plan ha is alid o classical VRP cons ain s
(e.g., capaci y, ime windows), while espec ing he HOS egula ions
on d i e s wo king and d i ing imes. The s a ing ime and du a ion
o he es b eaks a e in oduced as new decisions o be scheduled in he
model. This makes he p oblem mo e challenging (see Sec ion 7.2 on
o wa d–backwa d cons ain s). HOS egula ions ha e been igo ously
in oduced o he VRP by he seminal wo k o Goel (2009), al hough
some ea lie wo ks deal wi h he b eaks scheduling aspec ia less
sophis ica ed me hods (B andao and Me ce ,1997). Recen wo ks on
his p oblem may include addi ional conside a ions, such as d i e
well-being (Rancou and Paque e,2014;Khai an e al.,2022), g een
objec i es (Za ouk e al.,2022;Dukkanci e al.,2022) o eal-li e side
cons ain s (Goel e al.,2021).
Fa igue wi h es b eaks. The scheduling o d i e b eaks has also been
conside ed in he VRP wi hou elying on ac ual egula ion amewo ks.
A common conside a ion is he scheduling o a meal b eak in he ou ing
plan, o enable d i e s o ge a ull b eak du ing hei shi . This HAM
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
8
T. P une e al.
Table 9
Wo k- es scheduling e e ences (see Sec ion 3.2 o he able legend).
Re e ence Case Modeling HW HAM
s udy Obj. Cons . Pa am.
Bech old (1979) Fa igue e ec
Bech old e al. (1984) VTT Fa igue e ec
Thompson (1990) TWC Meal b eaks
Bech old (1991) VTT PP Fa igue e ec
Bech old and Thompson (1993) VTT PP Fa igue e ec
B usco and Jacobs (1993) TWC Meal b eaks
Aykin (1996) TWC Meal b eaks
Aykin (1998) TWC Meal b eaks
Meh o a e al. (2000) TWC Meal b eaks
B usco and Jacobs (2000)✓TWC Meal b eaks
Gä ne e al. (2001) FBC Wo k-s e ch du a ion
Topaloglu and Ozka ahan (2003) TWC Meal b eaks
Topaloglu and Ozka ahan (2004) MO-He TWC P Meal b eaks, sa is ac ion
Janiak and Ko alyo (2006) FSC Wo k-s e ch du a ion, haza d exposu e
Ba d and Wan (2006)✓TWC, FBC Meal b eaks
Thompson and Pullman (2007) Wo k-s e ch du a ion
Janiak and Ko alyo (2008) FSC Haza d exposu e, wo k-s e ch du a ion
Ba d and Wan (2008)✓TWC Meal b eaks
Rekik e al. (2008) TWC, FBC Meal b eaks, wo k-s e ch du a ion
Sawik (2010) FSC S Haza d exposu e, wo k-s e ch du a ion
Rekik e al. (2010)✓FBC Wo k-s e ch du a ion
Quimpe and Rousseau (2010) FBC Wo k-s e ch du a ion
Res epo e al. (2012)✓FBC Wo k-s e ch du a ion
Chen e al. (2013) TWC Meal b eaks
Widl and Musliu (2014) FBC Wo k-s e ch du a ion
Jamshidi and Seyyed Es ahani (2014) MO-Ho VQ Human e o s, a igue e ec s
Yi and Chan (2015)✓PP, CPP Hea ole ance
Gé a d e al. (2016) TWC Meal b eaks
Res epo e al. (2016) TWC Meal b eaks
Yi and Wang (2017) TC PP Hea ole ance
Bonu i e al. (2017) TWC Meal b eaks
Sungu e al. (2017) MO TWC Meal b eaks
Zhao e al. (2019) MO TC VTT EE
Calza a a e al. (2019b) EE, RA
Akke mans e al. (2019) FBC Wo k-s e ch du a ion
Li e al. (2020) VTT Fa igue e ec
Ál a ez e al. (2020)✓Meal b eaks
Fang e al. (2021) MO-Ho FBC VTT S Skills, wo k-s e ch du a ion
possess (Pa a and Mou a,2018;Hochdö e e al.,2018). Skills can
also be modeled wi h di e en le els o p o iciency, whe e he ask
du a ion will depend on he skill le el o he employee i is assigned
o Mossa e al. (2016) and Ba ini e al. (2022), o bo h he employee
skill and bo edom le els (Nan ha anij e al.,2010;Azizi e al.,2010).
O he HFs. On op o he classical WMSD isk assessmen me hods,
pe cep ual isk ac o s a e also conside ed o be mi iga ed by job
o a ion. These ac o s a e, o example, he noise exposu e o he
ope a o s (Tha mmapho nphilas and No man,2004;A yanezhad e al.,
2009b), o he ib a ion exposu e (Ba ini e al.,2022). Ad hoc isk as-
sessmen me hods based on biomechanical c i e ia a e also commonly
used (Moussa i e al.,2019).
6.1.2. Wo k- es scheduling
In wo k- es scheduling, he main decision is o de e mine when
and how o place es b eaks in an employee’s schedule. Acco ding
o Lod ee e al. (2009), he p oblem consis s in ‘‘de e mining he
numbe , placemen , and du a ion o es imes du ing a wo k pe iod’’.
This p oblem is e y ele an o indus ial applica ions. Many em-
pi ical s udies ha e shown he link be ween he a igue le el and:
indi idual pe o mances (Yung e al.,2020), diminished quali y o
wo k (Ca uso,2015), and inc eased sa e y p oblems (Lomba di e al.,
2010). Unsu p isingly, he s udies on wo k- es scheduling, p esen ed
in Table 9, ocus on b eak- ela ed HAMs, i.e., meal b eaks,wo k-s e ch
du a ion, a igue, and he compa ison be ween b eak scheduling me hods.
An in e es ed eade is e e ed o Xu and Hall (2021) o a mo e
de ailed e iew o empi ical wo k in wo k- es scheduling.
Fa igue ia meal b eaks. One o he mos basic app oaches o in eg a ing
es b eaks in o wo k o ce scheduling is o conside meal b eaks. This
HAM is qui e common, as i eme ges om a e y clea and s aigh o -
wa d ope a ional cons ain when es ablishing employees’ schedules. In
mos jobs, a b eak is planned o he employees o ge lunch. Hence,
ei he he b eak is ixed in ime, o a deg ee o lexibili y is allowed
o i s scheduling. The easies way o in eg a e lexibili y o a gi en
ex en in o scheduling models is o ha e a ime window, in which he
meal b eak should be scheduled. Fo hese easons, his app oach has
been used in he ea ly li e a u e on wo k o ce scheduling (Thompson,
1990;B usco and Jacobs,2000), bu also in mo e ecen wo ks on he
opic (Chen e al.,2013;B usco,2008). The use o ime windows is
no es ic ed o meal b eaks and can be used o add some lexibili y
when scheduling se e al b eaks in a wo k shi (Meh o a e al.,2000;
Topaloglu and Ozka ahan,2003;Bonu i e al.,2017).
Fa igue ia wo k-s e ch du a ion. Ano he common HAM o es b eak
scheduling is o use wo k-s e ch du a ion modeled ia o wa d–
backwa d cons ain s (see Sec ion 7.2). This app oach is mo e sophis-
ica ed han using ime windows, in he sense ha i p o ides mo e
possibili ies. The schedules should abide by a se o ‘‘ ules’’ abou b eak
placemen . Fo example, a maximal wo k du a ion wi hou a b eak, a
minimal b eak ime o be scheduled du ing a shi , a minimal du a ion
o a b eak, a minimal du a ion be ween he shi beginning and he
i s b eak. Gä ne e al. (2001) is he i s wo k, o ou bes knowledge,
o p opose his modeling amewo k, and o he pape s ollowed in
his di ec ion (Quimpe and Rousseau,2010;Res epo e al.,2012;
Rekik e al.,2010). An in e es ed eade is e e ed o Widl and Musliu
(2014) o a mo e heo e ical analysis o he impac o wo k-s e ch
du a ion on scheduling models. In some cases, he ules de e mining
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15

T. P une e al.
Table 10
Shi scheduling e e ences (see Sec ion 3.2 o he able legend).
Re e ence Case Modeling HW HAM
s udy Obj. Cons . Pa am.
Cai and Li (2000) TC Skills
Gans and Zhou (2002) MO-Ho S, LR Skills, lea ning
Ei zen e al. (2004)✓TC S Skills, equi y
Pan e al. (2010) MO CC, FSC S, P Skills, sa is ac ion
Knus and Schumache (2011) FBC Sa is ac ion
Akba i e al. (2013) SO CC VTT S, P Skills, a igue e ec , sa is ac ion
Lapegue e al. (2013) MO FBC Equi y
Nishi e al. (2014) MO-He FBC Equi y
P o e al. (2015)✓MO FBC Equi y
Dewi and Sep iana (2015)✓MO-He EE, cogni i e
Cheng and Kuo (2016) MO-He FSC S, P Skills, sa is ac ion, equi y
Shuib and Kama udin (2019)✓SO CC S, P Skills, sa is ac ion
S eenweg e al. (2020) MO-He CC S Skills, equi y
Caballini and Paolucci (2020)✓MO-He CC, TC, FSC S Skills, equi y, handling, epe i i e mo emen s
So iano e al. (2020) MO-He P Sa is ac ion
he placemen o b eaks a e modeled wi h sequence cons ain s. Fo
example, Janiak and Ko alyo (2006), Janiak and Ko alyo (2008)
and Sawik (2010) s udy he wo k o ce scheduling p oblem in con ami-
na ed a eas. In hei modeling, inspi ed by he ela ed wo k egula ions,
each wo k pe iod mus be ollowed by a es pe iod, whose leng h
depends (exponen ially) on he du a ion o he wo k pe iod.
Fa igue ia o he HAMs. To educe he a igue le el, he bene i s o a
es b eak ha e also been s udied. In he ela ed wo ks, he p oduc i i y
o a gi en employee educes o e ime, as his/he a igue inc eases.
B eaks a e no necessa ily included in a solu ion, bu hey enable
ull o pa ial eco e y o he ope a o , ese ing his/he p oduc i i y
o i s ini ial le el. This app oach is especially sui ed o applica ions
ocusing on he a igue modeling aspec since he b eak placemen and
du a ion a e linked o HF/E obse a ional me hods, ins ead o being an
exogenous inpu da a o he p oblem. This app oach has been used in
ea ly wo ks wi h he basic HAM de eloped by Bech old (1979), hen
applied in ea ly wo ks on he opic (Bech old e al.,1984;Bech old,
1991), as well as mo e ecen s udies (Jamshidi and Seyyed Es ahani,
2014;Li e al.,2020).
Compa ison be ween b eak scheduling me hods. Finally, he e a e wo ks
ha s udy di e en b eak placemen me hods, and analyze he impac s
on he p oduced schedules. Fo example, Bech old (1979) p oposes
se e al models o a igue and es pe iods in he con ex o wo k o ce
scheduling. Rekik e al. (2008) s udy he modeling o bo h ime win-
dows and wo k-s e ch du a ion. Thompson and Pullman (2007) s udy
he compa ison be ween scheduling b eaks in ad ance and doing so in
eal ime.
O he HFs. The de ailed modeling o he b eak scheduling ules may
come om he ac o s ela ed o he indus ial applica ion a hand,
o example, hea exposu e (Yi and Chan,2015;Sadeghi-Das aki and
A azeh,2018) o haza d exposu e in con amina ed a eas (Janiak and
Ko alyo ,2006,2008;Sawik,2010).
6.1.3. Shi scheduling
In he Shi Scheduling P oblem (SSP), he main decision is o
alloca e he employees o a gi en wo k o ce o di e en wo k shi s
o e a ce ain ime ho izon, usually weeks o mon hs (Lod ee e al.,
2009). Each shi is cha ac e ized by a s a equi emen , gi en by a
numbe o employees o be scheduled in he shi , o en wi h he asso-
cia ed skill se equi ed o pe o m he ope a ions ha a e planned. The
objec i e is o en o minimize he cos o he plan wi h penal ies, ha
can be applied o a o aspec s desi ed in he solu ion: equi y be ween
employees, wo ke s’ p e e ences, o e ime (Lod ee e al.,2009). The
co esponding s udies a e p esen ed in Table 10, and ocus on skills,
wo king hou s egula ions and psychosocial ac o s.
Skills. Conside ing he na u e o he SSP and he scope o he p esen
e iew, i is no su p ising ha skills a e one o he main HFs conside ed
in SSP (Akba i e al.,2013;Cai and Li,2000;Ei zen e al.,2004).
Skills a e seen as di e en quali ica ions o he employees: he man-
u ac u ing sys em canno un du ing a shi i ce ain jobs a e absen
om he p oduc ion si e, o a e no in a su icien numbe . This is well
illus a ed in indus ial case s udies. Fo example, Shuib and Kama udin
(2019) s udy an SSP in a powe s a ion, and he skills co espond
o he di e en posi ions: chie cha ge enginee , senio /junio block
ope a o s, i s /second pa olmen, con ol ope a o s, e c.
Wo king hou s egula ions. The manu ac u ing sys ems modeled wi h
an SSP a e o en unning on a 24 h schedule, hus including he
shi scheduling aspec . And as such, wo king hou s egula ions a e o
o emos impo ance in he indus ial con ex . In op imiza ion models,
his can be modeled wi h o wa d/backwa d cons ain s (Nishi e al.,
2014;Knus and Schumache ,2011;P o e al.,2015) o o bidden
sequence cons ain s (Cheng and Kuo,2016;Pan e al.,2010).
Psychosocial ac o s. Ano he impo an class o HFs conside ed in
SSP ela es o he psychosocial isk ac o s, he mos popula being
employee p e e ences and equi y. In he 24 h shi wo k, some shi s a e
mo e d aining han o he s (i.e., nigh shi s). I applicable, some wo k
pe iods a e less p e e ed han o he s (i.e., weekends, public holidays).
Fo a manage , i is c ucial o accoun o hese aspec s o ensu e
ai ness be ween employees and keep he wo k o ce sa is ied. In he
ma hema ical models, se e al wo ks in eg a e he shi p e e ences o
he employees. In his case, each employee gi es i s p e e ed shi s,
ei he wi h sco es o ia a anking sys em (Cheng and Kuo,2016;
Knus and Schumache ,2011;Pan e al.,2010). In some models, he
maximiza ion o p e e ences is he sole objec i e (Akba i e al.,2013;
Shuib and Kama udin,2019). The equi y conce ns in he SSP li e a u e
a e no limi ed o p e e ences, o he measu es a e used o balance
he wo kload be ween employees, like he o al wo king ime, o he
assigned shi s on public holidays (P o e al.,2015;Lapegue e al.,
2013;Cheng and Kuo,2016).
O he HFs. Fa igue may be conside ed in SSP (Akba i e al.,2013;Dewi
and Sep iana,2015). I is indeed pa icula ly ele an o indus ial
cases unning on a 24 h schedule.
6.2. Wo k o ce managemen
6.2.1. Wo k o ce planning
In his sec ion, we ocus on wo k o ce planning p oblems. In his
class o p oblems, he main decision is, o each pe iod, o assign
he employees o a se o asks o be pe o med. The di e ence wi h
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
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T. P une e al.
Table 11
Wo k o ce planning e e ences (see Sec ion 3.2 o he able legend).
Re e ence Case Modeling HW HAM
s udy Obj. Cons . Pa am.
S ewa e al. (1994) SO CC S Skills
Bo doloi and Ma suo (2001)✓MO-Ho S Skills
Wi ojanagud e al. (2007) TC S, LR Skills
Fowle e al. (2008)✓TC S, LR Skills
A yanezhad e al. (2009a) MO-Ho CC S Skills
McDonald e al. (2009)✓MO-Ho CC VQ S Skills
O hman e al. (2012) MO-He S, PP, CPP Skills, mo i a ion
Kim e al. (2013) MO-Ho To al cos unc ion
A ia e al. (2014) MO-He CC VTT S, LR Skills, lea ning
Nembha d and Ben e oue (2014) VTT S, LR Skills, lea ning
Hewi e al. (2015) VTT Lea ning
Mehdizadeh e al. (2016) MO-Ho CC S Skills
Vale a e al. (2017) VTT LR Lea ning
Wang e al. (2018) VTT S Skills
Shahbazi e al. (2019) MO-He VTT S Skills, psychosocial
Ca agnini e al. (2020) VTT Lea ning
Ru e al. (2022) MO-Ho CC S Skills
Jaille e al. (2022) MO-Ho S Skills
Ra iee e al. (2022) MO-Ho CC S Skills, lea ning
Doan e al. (2022)✓MO-He CC S, P Skills, sa is ac ion, equi y
he wo k o ce scheduling p oblem lies in he planning ho izon. The
wo k o ce planning p ocess is loca ed a he ac ical le el, he e o e
he planning ho izon is a he long (e.g., weeks o mon hs). This la ge
ho izon means ha ac ical decisions (e.g., he size o he wo k o ce)
become a ailable o he planne (De B uecke e al.,2015). The hi ing
and i ing p ocesses a e usually conside ed in hese models, bo h com-
ing a a mone a y cos . In mos p oblems, he skill pool o he wo k o ce
should emain su icien o ul ill he equi emen o each wo k pe iod.
Table 11 p esen s he wo ks on he opic, wi h a ocus on skills, and
skill de elopmen . The in e es ed eade is di ec ed o De B uecke e al.
(2015) o a de ailed li e a u e e iew on wo k o ce planning wi h skill
conside a ions.
Skills. The mos common HF in his class o p oblems is skills. They
can be modeled wi h compa ibili y cons ain s, whe e only quali ied
employees can be assigned o ce ain asks when indi idual assignmen s
a e modeled (McDonald e al.,2009). Ano he modeling app oach is
wi h esou ce cons ain s: In his case, employees a e no modeled in-
di idually, he numbe o employees a a gi en skill le el is conside ed
as a esou ce, ha needs o be abo e a ce ain equi emen in each
planning pe iod (Fowle e al.,2008). Skills can also be modeled wi h
a ying ask imes (see Sec ion 7.3.1), whe e he p oduc i i y o an
employee imp o es wi h his/he skill le el (Wang e al.,2018). Mo e
han p oduc i i y, he ou pu quali y le el can a y wi h he skill o an
employee (McDonald e al.,2009).
Skill de elopmen . Wi h he ac ical planning ho izon, hi ing and i ing
a e no he only ways o adap he skill se o he wo k o ce. Some
wo ks explici ly conside human esou ce planning, whe e aining can
be scheduled o imp o e he skill le el o an employee. In his case,
he aining comes a a mone a y cos , ei he di ec ly, o indi ec ly
when an employee is una ailable due o aining (O hman e al.,2012;
Mehdizadeh e al.,2016).Fowle e al. (2008) and Wi ojanagud e al.
(2007) a e in e es ed in he Gene al cogni i e abili y o employees,
a ec ing he equi ed aining ime o pass om one skill le el o
ano he . The skill de elopmen does no necessa ily come wi h aining,
bu also ia indi idual (i.e., au onomous) lea ning. Mo e de ails on he
di e ence be ween induced and au onomous lea ning a e p o ided in
P une e al. (2024), Lea ning. This is also conside ed in se e al wo ks
on wo k o ce planning, whe e he epe i ion o asks equi ing a gi en
skill inc eases he p o iciency o an employee a his skill. Lea ning
cu es a e used o model his au onomous lea ning (Nembha d and
Ben e oue ,2014;Ca agnini e al.,2020).
6.2.2. Wo k o ce assignmen
In his sec ion, we s udy he wo k o ce assignmen . This is no a
uni ied class o p oblems, bu we chose o eg oup p oblems ha assign
a se o ope a o s o a se o jobs o wo ks a ions. Gene ally speaking,
his assignmen decision is no he ocus o hese wo ks pe se, bu one
aspec o he decision-making p ocess. Table 12 p esen s he wo ks on
he opic, wi h a ocus on skills and psychosocial ac o s.
Skills. An impo an class o p oblems e e s o he cellula manu ac u -
ing o ganiza ion, whe e machines and p ocesses a e g ouped in o cells
ha p oduce a speci ic ou pu . In his con ex , one s ep o he p ocess
is he assignmen o wo ke s o he se o cells. As usual, when dealing
wi h p oblems ela ed o wo k o ce op imiza ion, skills a e a e y
common HF (Wu e al.,2018;Lian e al.,2018). No man e al. (2002)
ocus hei modeling o he skill aspec on bo h echnical and human
skills. Skill imp o emen can be conside ed h ough aining (No man
e al.,2002), which is unusual wi h p oblems wi hou a ime ho izon.
In his example, a maximum aining ime is allowed o ge he desi ed
skill se in he wo k o ce. Fo a pe son/job assignmen p oblem, Sayin
and Ka aba i (2007) use a 2-s age model o assign wo ke s be ween
se e al depa men s. The model is mul i-pe iod wi h he i s s age
consis ing o assigning employees o depa men s by minimizing labo
sho age, and he second s age aiming a maximizing skill de elopmen
in he wo k o ce.
Psychosocial ac o s. Se e al pape s ha s udy a gene al pe son/job
assignmen wi h a secu i y conce n (Zhang e al.,2019b), while ac-
coun ing wo ke s’ sensi i i y o isk (Lazze ini and Pis olesi,2018), o
accoun ing o cogni i e aspec s o he job and a ious human esou ces
s a egies (Fini e al.,2017). B usco (2015) ocuses on employees’
p e e ences as a second objec i e unc ion o de e mine he op imal
pe son/job assignmen .
O he HFs. Lea ning is also conside ed in his class o p oblems, o
ins ance (Nembha d,2001) s udies a basic model o wo jobs and
wo ke s wi h di e en lea ning a es. The au ho pe o ms simula ions
unde di e en expe imen al condi ions o design a heu is ic policy
ha maximizes o al p oduc i i y. Equi y is ano he common conce n,
whe e he aim is o balance he wo kload among employees (Lian e al.,
2018;Wu e al.,2018).
7. Ma hema ical p og amming conside a ions
This sec ion p esen s an analysis o he e iewed ma e ial om
a ma hema ical p og amming pe spec i e. The in eg a ion o HFs is
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
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T. P une e al.
Table 12
Wo k o ce assignmen e e ences (see Sec ion 3.2 o he able legend).
Re e ence Case Modeling HW HAM
s udy Obj. Cons . Pa am.
Nembha d (2001) VTT LR Lea ning
No man e al. (2002) MO-Ho CC VQ S, CPP Skills
Sayin and Ka aba i (2007) MO S Skills, lea ning
Suee and Tummalu i (2008) VTT S Skills, lea ning
Egilmez e al. (2014) VTT S Skills
B usco (2015) MO-He S, P Skills, sa is ac ion
Niakan e al. (2016) CC, TC S Skills, noise
Fe jani e al. (2017)✓MO-He CC S Skills, a igue e ec
Azadeh e al. (2017) MO CPP Psychosocial
Fini e al. (2017)✓SO S, PP, CPP Cogni i e, psychosocial
Lazze ini and Pis olesi (2018)✓MO CPP, P Sa is ac ion, expe collabo a ion
Wu e al. (2018) MO-He VTT S Skills
Lian e al. (2018) MO VTT S Skills, equi y
Zhang e al. (2019b)✓MO-Ho TC Expe collabo a ion
Yilmaz (2020) MO VTT S Equi y
Lee e al. (2022)✓CC Haza d exposu e
Daş e al. (2022) MO-He S, CPP Skills, psychosocial
Fig. 2. Popula i y o exis ing modeling echniques.
mainly pe o med h ough he objec i e unc ion (see Sec ion 7.1),
cons ain s (see Sec ion 7.2), o pa ame e s (see Sec ion 7.3). This
sec ion highligh s he impac o HAMs on he s uc u e and complexi y
o ma hema ical models and solu ion me hods. Modeling app oaches
a e linked o he mos classical HAMs hey a e applied wi h. Examples
a e p o ided o illus a ion pu poses, bu mos ly links a e made owa d
opics discussed in P une e al. (2024). Fig. 2 shows he popula i y o
he modeling echniques ound in he collec ed ma e ial. No e ha he
di e en echniques wi hin he same ca ego y (e.g., cons ain s) a e no
mu ually exclusi e, as one pape may use se e al o hose.
7.1. Objec i e unc ion
In his sec ion, we s udy models whe e HFs a e in eg a ed ia
an objec i e unc ion. This is achie ed ei he wi h a single human-
awa e objec i e unc ion (see Sec ion 7.1.1), o wi h he in eg a ion o
a human- ela ed objec i e unc ion alongside o he economic- ela ed
objec i es (see Sec ion 7.1.2).
7.1.1. Single objec i e op imiza ion
A single e gonomic objec i e is e y s aigh o wa d o handle om
a ma hema ical poin o iew. Howe e , conside ing only a HF- ela ed
objec i e is a ely su icien while neglec ing exis ing (economic) ob-
jec i es. Since cos s a e no included in he objec i e, his modeling
app oach e e s mainly o p oblems whe e he cos is ixed o bounded,
and o he quan i ies a e balanced be ween employees/zones. This class
o p oblems is mainly ela ed o job o a ion (O o and Scholl,2013),
assembly line balancing (Bau is a e al.,2016a), o s o age assignmen
in wa ehousing (O o e al.,2017). I can also be encoun e ed in d i e
scheduling models (O o e al.,2017). Ma hema ical models in ol ing
a single HF- ela ed objec i e a e used mainly in wo cases: (i) o
special-pu pose models, o (ii) when dealing wi h equi y conce ns.
Special-pu pose models. Special-pu pose models hea ily depend on he
p oblem unde s udy. One can ci e (S ewa e al.,1994), whe e di -
e en aining s a egies lead o di e en objec i e unc ions ela ed
o he wo k o ce (e.g., minimizing he aining cos , maximizing he
wo k o ce lexibili y). Some imes he objec i e is o minimize he o al
ER o a plan wi hou compu ing i o indi idual employees (Die enbach
e al.,2020).
Single equi y objec i e. The main applica ion ound in he li e a u e
is o deal wi h equi y be ween employees o lines in an assembly
con ex . This is ele an o p oblems whe e he numbe o esou ces is
al eady decided, and he goal is o balance hese esou ces e icien ly.
I is in assembly line balancing o job o a ion o ind an assignmen
o ope a ions ha balances he wo kload e enly be ween he pa k o
wo ks a ions. In he case o HAMs, his wo kload is, howe e , de ined
in e ms o physical wo kload o he employees, o wo k- ela ed inju y
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
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T. P une e al.
isk. The HAMs used o es ima e he ER o balance in a single op i-
miza ion objec i e, include mos obse a ional me hods (Bau is a e al.,
2016a), especially wi h li ing ac i i ies ia he NIOSH-equa ion (O o
e al.,2017) o he JSI (Ca nahan e al.,2000), bu also wi h noise
exposu e (Tha mmapho nphilas and No man,2004).
Ka su and Mo on (2015) p o ide a e iew o he di e en equi y
me ics ound in he OR li e a u e, i.e., he di e en unc ions ha
can be used o assess i a quan i y is well balanced be ween a se o
en i ies. In he wo ks su eyed in he cu en e iew, whe e a single
equi y objec i e is used, he basic min–max balancing c i e ion is he
mos common. He e, a se o asks , each wi h an associa ed ER
𝑝𝑖,𝑖∈has o be balanced be ween a se o employees o e
a planning ho izon . The assignmen decision a iable 𝑥𝑖𝑤𝑡 is equal
o 1 i ask 𝑖∈is assigned o employee 𝑤∈du ing pe iod
𝑡∈. Then he objec i e is o minimize he maximal accumula ed isk
o e he planning ho izon o he employees, as shown in he ollowing
equa ion:
min max
𝑤∈∑
𝑡∈
∑
𝑖∈
𝑝𝑖𝑥𝑖𝑤𝑡
Fo u he analysis, he in e es ed eade is di ec ed o Ka su and
Mo on (2015).
7.1.2. Mul i-objec i e op imiza ion
Mul i-c i e ia op imiza ion is commonly used o include human
well-being and sa e y in ma hema ical op imiza ion because i allows
aking in o accoun bo h economic and e gonomic objec i es a he
same ime. Despi e he ease o modeling, he challenge o mul i-c i e ia
op imiza ion esides in he handling o mul iple (and o en con lic ing)
objec i es. We dis inguish h ee possibili ies o simul aneously handle
mul iple c i e ia:
•Mul iple objec i es: In his case all objec i e unc ions a e ully
conside ed in he model, and he aim is o compu e, o es ima e,
he so-called Pa e o- on . This app oach is he mos sound in
e ms o modeling, as no assump ion is made on he p e e ences
o he decision-make .
•Agg ega ion o he e ogeneous objec i es: Ano he possibili y is o
wo k wi h objec i e unc ions exp essed by he e ogeneous quan-
i ies (like money and wo k- ela ed ER), and o agg ega e hem
none heless. The decision-make hen has o posi ion he objec i e
unc ion o in e es in ela ion o i s ela i e impo ance. In
his case, he conside ed objec i es a e agg ega ed wi h weigh s,
which need o be uned o look a he desi ed a ea o he Pa e o
on . This is, howe e , non- i ial and o en equi es a ai
amoun o wo k (B anke e al.,2008).
•Agg ega ion o homogeneous objec i es: In his case, objec i es a e
exp essed as homogeneous quan i ies (like money) ha a e com-
pa able. The e o e, mul iple c i e ia can be added o he objec i e
wi hou equi ing addi ional in o ma ion.
Mul iple objec i es. Mul i-objec i e op imiza ion is a a he common
way o accoun o HFs. I is used in a ious a eas o logis ics: as-
sembly (Ba ini e al.,2016a), job o a ion (Bo i e al.,2020), wa e-
housing (La co e al.,2017), ou ing (Liu,2016), scheduling (Lu e al.,
2019), o lo sizing (And iolo e al.,2016). In e ms o HFs accoun ed
o , he lis is also di e se bu mos ly ocused on ER assessmen me hods
ha a e educed o balanced. I includes ene gy expendi u e, man-
ual handling isk assessmen me hods, pos u e assessmen , o gene al
wo kload equi y conce ns.
Dealing wi h mul iple c i e ia decision-making has an impac bo h
in e ms o complexi y and choice o solu ion app oaches (B anke e al.,
2008). The mul i-c i e ia aspec leads o se e al non-domina ed op imal
solu ions wi h espec o he conside ed c i e ia, he so-called Pa e o
on . This app oach is especially ele an o indus ial applica ions,
as he Pa e o on p o ides a con enien isual ep esen a ion o
he decision-make . To compu e an es ima e o his on o some
o i s pa icula poin s, wo me hods a e mainly used: popula ion-
based me aheu is ics, and non-in e ac i e ma hema ical p og amming
app oaches.
Popula ion-based me aheu is ics a e well-s udied and ha e p o en
hei e iciency in dealing wi h la ge-scale mul i-c i e ia op imiza ion
p oblems (B anke e al.,2008). Se e al solu ions a e kep in memo y a
all imes, wi h conside a ion o he popula ion di e si y, hus enabling
he gene a ion o a se o non-domina ed solu ions a he end. The mos
common me aheu is ics ound a e a ian s o gene ic algo i hms (Sana
e al.,2019;Al-Zuhe i e al.,2016), as he NSGA-II algo i hm (Rabbani
e al.,2020;Azadeh e al.,2017). Mo e seldom, o he me aheu is ics
amewo ks ha e been used, like he a i icial bee colony (Li e al.,
2018a), he pa icle swa m op imiza ion (Cui e al.,2020), o he G ey
Wol op imiza ion (Lu e al.,2019).
As a as ma hema ical p og amming is conce ned, se e al com-
mon mul i-c i e ia me hods ha e been used in he con ex o his
li e a u e e iew. The mos common one is he 𝜖-cons ain me hod,
which enables he compu a ion o se e al poin s o he Pa e o on
by op imizing o e one objec i e, while he o he s a e cons ained by
a gi en alue (Bo olini e al.,2017;Finco e al.,2020a). A mo e
basic e sion o his me hod is he op imiza ion using lexicog aphic
objec i es, whe e one objec i e is i s op imized and hen cons ained
o i s op imal alue, hen ano he objec i e is op imized, and so on.
Compa ed o 𝜖-cons ain , his app oach is mo e s aigh o wa d in i s
design and implemen a ion bu p o ides less in o ma ion on he Pa e o
on (Lehuédé e al.,2020;Liu,2016).
Agg ega ion o he e ogeneous objec i es. This modeling app oach is ai ly
common since i is he mos basic way o deal wi h economic and
e gonomic objec i es. In he e ie ed co pus, i has been used in
a ious a eas o logis ics and manu ac u ing. These include assembly
line balancing (O o and Scholl,2011;Bau is a e al.,2016b), job
o a ion (Diego-Mas e al.,2009), wo k o ce scheduling (Yoon e al.,
2016;O hman e al.,2012), and ehicle ou ing (Kim e al.,2006).
A nai e me hod o cons uc an agg ega e objec i e is, o a easible
solu ion 𝑥∈𝑋, o associa e wi h each objec i e unc ion 𝑔𝑖(𝑥)a weigh
0< 𝑤𝑖<1, 𝑖 ∈ {1,2,…, 𝑛}. These weigh s a e usually no malized so
ha hei sum is equal o one. The agg ega e objec i e unc ion is hen
he weigh ed sum o 𝑛objec i es:
𝑔(𝑥) =
𝑛
∑
𝑖=1
𝑤𝑖𝑔𝑖(𝑥)
This is analy ically and compu a ionally a he easy o do. Howe e ,
he ine- uning o hese weigh s is a om i ial o ansla e well he
p e e ences o he decision make in o quan i ied ade-o s. I s ease o
use has made he weigh ed sum me hod ai ly popula in he p esen
co pus. One can ci e (Bhadu y and Rado ilsky,2006;Bau is a e al.,
2016b) o Diego-Mas e al. (2009) wi h up o 45 c i e ia agg ega ed
in a weigh ed sum. Goal p og amming is ano he widely used me hod
o agg ega e he e ogeneous objec i es. The idea is, o each objec i e
unc ion 𝑔𝑖 o se a a ge alue 𝑔∗
𝑖, which co esponds o he desi ed
alue o he decision make . Then he agg ega e objec i e unc ion is
o minimize he weigh ed sum o he de ia ions om he a ge alues.
The ad an age o goal p og amming o e he weigh ed sum app oach
is ha he a ge alues ha e a conc e e in e p e a ion o he decision
make , and a e he e o e easie o compu e. Fu he mo e, hese a ge s
p o ide na u al alues o he weigh s in he sum, by no malizing he
de ia ion o he a ge alue as a pe cen age. The objec i e unc ion
would hen be:
𝑔(𝑥) =
𝑛
∑
𝑖=1
𝑤𝑖|𝑔∗
𝑖−𝑔𝑖(𝑥)|
𝑔∗
𝑖
An al e na i e exis s, whe e he Chebyshe dis ance me ic is used
ins ead, i.e., he objec i e is o minimize he maximum (weigh ed)
de ia ion om he a ge alues. Goal p og amming has been used, o
ins ance, in Choi (2009), Sungu e al. (2017) and Mokh a i and Hasani
(2017).
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T. P une e al.
O e all, a he e ogeneous agg ega ion o se e al objec i es is much
easie o handle han ue mul i-objec i e op imiza ion, bo h analy i-
cally and compu a ionally. Howe e , in o ma ion is los since a single
solu ion is ound ins ead o a se o non-domina ed ones. This can make
an ac ual implemen a ion ickie o ine- une o he decision make ’s
p e e ences.
Agg ega ion o homogeneous objec i es. Ano he way o deal wi h mul-
iple objec i es is o con e hem in o homogeneous quan i ies. This
way hey a e compa able, and he ade-o is easie o ind. The big
ad an age o his kind o modeling is ha he model has only one
objec i e, hus being analy ically and compu a ionally easie , wi hou
equi ing he decision make o quan i y his/he needs o ine- une an
implemen a ion o he model. Howe e , o be easible, he conside ed
objec i es should be exp essed in homogeneous quan i ies, which, de-
pending on he con ex , can be a he di icul . Usually, he agg ega ed
objec i e is exp essed as a o al cos since i is a a he common conce n
o companies o quan i y he di ec and indi ec economic impac s
o hei decisions. No e ha , o use his modeling, a HAM should be
con e ible o a mone a y cos , which limi s he applicabili y.
This modeling app oach is in e es ing o use since i combines bo h
accu acy in he decision-making p ocess and ease o compu a ion. How-
e e , he p oblem a hand should be compa ible wi h such modeling,
which is no always he case. I is used in di e en a eas o logis ics
and manu ac u ing, such as lo sizing (Ba ini e al.,2017c), assembly
line balancing (Ka a e al.,2014), wo k o ce scheduling (Jamshidi and
Seyyed Es ahani,2014) o wa ehousing (Ba ini e al.,2017b).
A common app oach is based on a igue wi h ene gy expendi u e
and es allowance. The gene al idea is ha when he a igue le el
o an employee inc eases, he ER inc eases acco dingly. The e o e, o
each wo k pe iod, one can compu e a es allowance co esponding o
a b eak pe iod enabling he eco e y, which can easily be ansla ed
in o a cos using he hou ly wage o he employee since i co esponds
o paid unp oduc i e ime (Ba ini e al.,2017c;Condeixa e al.,2020;
Glock e al.,2019c). A high le el o a igue also leads o a ise in he
e o a e, which can easily be quan i ied as a cos and in eg a ed in o
he objec i e unc ion (Kuo e al.,2014).
Ano he common modeling e e s o he WMSD isk. A high le el
o wo k- ela ed inju ies migh impac indi ec ly se e al cos i ems
o a company. Sobhani e al. (2017), Sobhani and Wahab (2017)
and Sobhani e al. (2019) y o quan i y his mone a y impac . The
objec i e unc ion hey use is exp essed in e ms o a mone a y cos
and includes se e al e ms ha co espond o di e en cos i ems,
po en ially impac ed by poo wo king condi ions: pe o mance loss,
inju y lea e cos s, inc eased insu ance cos s, hi ing and i ing cos s o
eplacemen sho - e m employees o o compensa e a high u no e
a e, e c. Ano he in e es ing app oach is o accoun o he cos o isk-
mi iga ion measu es when aced wi h a haza dous wo k en i onmen .
Fo example, Raza i e al. (2014) accoun o noise con ol in a man-
u ac u ing en i onmen , and hei objec i e unc ion includes pe sonal
and machine equipmen o keep he noise- ela ed isks a an accep able
le el.
Finally, when conside ing employees’ skills, i is possible o link he
skill le el o p oduc i i y a e (No man e al.,2002), e o a e (McDon-
ald e al.,2009) and, o cou se, wages (Moon e al.,2009). All hese
aspec s impac he cos s uc u e.
7.2. Cons ain s
Ano he widesp ead way o conside human- ela ed ac o s in op-
imiza ion models is ac oss cons ain s. Se e al ypes o human-awa e
cons ain s can be iden i ied wi hin he collec ed ma e ial: compa ibil-
i y cons ain s, h eshold cons ain s, o bidden sequence cons ain s, and
wo k-s e ch du a ion cons ain s.
Compa ibili y cons ain s. Compa ibili y cons ain s a e one o he mos
basic ways o in eg a e human conside a ions in o ma hema ical mod-
els. They a e mos ly used when he aim is o assign a se o human
esou ces (o en employees) o a se o asks in acco dance wi h he
skills o employees. Compa ibili y cons ain s a e mos ly used o model
he skills and quali ica ions o wo k o ce scheduling (Pan e al.,2010)
o assembly line balancing (Mana izadeh e al.,2013), mo e seldom,
o ehicle ou ing when d i e s scheduling is in eg a ed (Anoshkina
and Meisel,2020).
To be e e lec p ac ical conside a ions, addi ional modeling laye s
can be added o he p oblem. The idea behind his is always ha a
subse o assignmen s is o bidden. Fo example, each wo ke 𝑤∈
is associa ed wi h a se (𝑤)o asks he/she is able o pe o m, o
wi h he opposi e iew each ask 𝑖∈is associa ed wi h a se (𝑖)
o wo ke s ha can pe o m i . When skills a e explici ly accoun ed
o , he p oblem de ini ion con ains a se o skills ha can ep esen
ei he di e en compe encies o quali ica ions, a single compe ence
wi h di e en p o iciency le els, o bo h a he same ime. In his case,
each employee 𝑤is associa ed wi h a se (𝑤)o skills he/she is able o
pe o m, and each ask 𝑖 equi es a se (𝑖)o compe ences o be ope -
a ed (e.g., Pan e al. (2010)). Then an assignmen is possible when he
employee possesses he skills equi ed o he ask. Some imes se e al
employees can be assigned o a ask, and he skill equi emen applies
o he g oup as a whole, and no o each indi idual. This is ound in
p oblems dealing wi h cell o ma ion in manu ac u ing (A yanezhad
e al.,2009a), o echnician ou ing p oblems (Ma hlou hi e al.,2018),
whe e he eams should ha e a b oad skill se .
F om a complexi y pe spec i e, hese cons ain s do no signi ican ly
change he p oblem. Wi h ewe easible assignmen s, he p oblem be-
comes mo e cons ained. I could he e o e be mo e complica ed o ind
easible solu ions, depending on he numbe o cons ain s applied o a
speci ic ins ance. Howe e , in he e iewed pape s, he main challenge
is he op imiza ion o he p oblem since he skill esou ces a e no
sca ce enough o make easibili y challenging. This modeling equi es
conside ing a he e ogeneous wo k o ce, sligh ly inc easing he p oblem
size depending on he g anula i y o he skill se (i.e., he numbe
o skills conside ed). This modeling is, apa om ea ly wo k, used
mos ly in addi ion o o he human aspec s ha a e compu a ionally
mo e challenging o conside in ma hema ical models.
Th eshold cons ain s. Th eshold cons ain s a e mos ly used when as-
signing o scheduling asks. The idea is ha each ask is associa ed
wi h an ER. Then he e is a h eshold alue o accumula ed ER o
an employee o e a shi . This modeling is mos ly used when dealing
wi h isk es ima ion me hods, like WMSD isk assessmen me hods
o ene gy expendi u e. Some imes i is also applied o en i onmen al
ac o s ha a e also linked o occupa ional isks, like noise o hea
exposu e. I is applied o a ious kinds o logis ics and manu ac u ing
p oblems ela ed o assembly line balancing (O o and Scholl,2011),
job o a ion (Mossa e al.,2016), wa ehousing (Zhao e al.,2019),
scheduling (Yi and Wang,2017) o ehicle ou ing (Ra anamanee
e al.,2015).
Th eshold cons ain s a e e y classical in he OR li e a u e, o en
called ‘‘capaci y cons ain s’’. A se o asks has o be assigned o a
se o wo ke s. Each ask 𝑖∈has a isk sco e 𝑝𝑖>0. An assignmen
a iable 𝑥𝑖𝑤 ∈ {0,1} is equal o 1, i ask 𝑖∈is assigned o wo ke
𝑤∈du ing he shi . A h eshold alue 𝑝𝑚𝑎𝑥 is gi en and should no
be iola ed by he accumula ed ER o asks assigned o each employee,
ensu ing he isk emains a an accep able le el. Then, he h eshold
cons ain is o he o m:
∑
𝑖∈
𝑝𝑖𝑥𝑖𝑤 ≤𝑝𝑚𝑎𝑥 ∀𝑤∈.(1)
The modeling o he assignmen ask/employee can be mo e so-
phis ica ed, especially when conside ing he scheduling o he asks,
whe e ime is accoun ed o . Usually, in his amily o cons ain s, he
sequencing o asks does no ma e . O en he isk le el o a ask
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20

T. P une e al.
is weigh ed by he ime spen pe o ming his ask, acco ding o a
gi en ER assessmen me hod. Ne e heless, his amily o cons ain s
is ul ima ely equi alen o he basic o m o Inequali ies (1). The alue
o he h eshold 𝑝𝑚𝑎𝑥 is o en de i ed om he isk assessmen me hod
and is se o an accep able isk le el o he shi . The isk assessmen
me hod can be: he OCRA index (Mossa e al.,2016), he NIOSH-
equa ion (Ma eo e al.,2020), he ene gy expendi u e (Zhao e al.,
2019), o he DND (Raza i e al.,2014). Some imes he h eshold
alue is se wi h espec o legal cons ain s abou employees’ exposu e,
e.g., ela ed o oxic subs ance exposu e (Villeda and Dean,1990).
In Ruiz-To es e al. (2015), he opposi e cons ain is used, whe e each
employee sa is ac ion le el should be abo e a gi en h eshold.
Fo bidden sequence cons ain s. Fo bidden sequence cons ain s a e
used o a oid some successions o asks/shi s when designing a sched-
ule. Indeed, when pe o ming a physical ask o high in ensi y, he
ER inc eases wi h he du a ion o he ask. In obse a ional isk
es ima ion me hods, he exe ion du a ion is o en a key pa ame e .
A o bidden sequence cons ain ensu es a maximum numbe (o en
one) o successi e s enuous asks scheduled o a gi en ope a o . The
h eshold cons ain s a e a sui able me hod o limi he isk exposu e o
an employee on his/he shi . Howe e , hey miss a key componen o
he isk es ima ion, namely, he du a ion o he unin e up ed exe ion.
Fo bidden sequence cons ain s a e a way o b ing mo e e inemen and
accu acy o models ha al eady ha e mechanisms o limi /balance he
ER o e a shi (see e.g., Moussa i e al. (2019)). Na u ally, o bidden
sequence cons ain s a e mos ly ound in job o a ion models (Moussa i
e al.,2019;Yoon e al.,2016), o wo k- es scheduling p oblems, since
hey accoun o he sequence o assigned asks o a wo ks a ion o an
employee (Gä ne e al.,2001;A a indk ishna e al.,2009).
In e ms o complexi y, hese cons ain s a e a he s aigh o wa d
o handle om a modeling poin o iew. Howe e , he p oblem
becomes signi ican ly mo e cons ained. Mo eo e , he inse ion o
hese cons ain s in an exis ing model migh lead o in easibili y, and
o e all easibili y becomes mo e complica ed o ensu e. One should
no e ha he educ ion o successi e high wo kloads is also commonly
ound as an objec i e unc ion ins ead o a cons ain (Asensio-Cues a
e al.,2012b;Bo i e al.,2020). Modeling his aspec as an objec i e
add esses he a o emen ioned d awbacks o he o bidden sequence
cons ain s, howe e , o he di icul ies migh a ise wi h he handling
o se e al objec i es (see Sec ion 7.1).
Time window cons ain s. The ime window cons ain is a widely used
modeling echnique in OR (see Fig. 2). I applies mos ly when schedul-
ing a se o ac i i ies, ha need o be pe o med in a gi en ime ame.
In hese p oblems, decisions ha e o be made ela ed o he s a ime
o a gi en se o ope a ions. Fo a gi en ac i i y 𝑖∈, a ime
window cons ain ensu es ha he ac i i y 𝑖s a s du ing a gi en ime
window [𝑎𝑖, 𝑏𝑖]. Wi h 𝑡𝑖 he decision a iable de ining he s a ime o
he ac i i y, he ma hema ical cons ain is hen 𝑎𝑖≤𝑡𝑖≤𝑏𝑖.
In he scope o his e iew, he ac i i ies conce ned wi h ime
window cons ain s a e wo k b eaks. The e a e indeed se e al ways
o model he in eg a ion o b eaks in a wo k o ce schedule, he mos
basic one being h ough ime windows. I co esponds o mos ly ea ly
wo k on he opic, whe e a meal b eak has o be scheduled in he wo k
shi wi h a lexible s a ime (Thompson,1990). Howe e , he meal
b eak should happen oughly a meal ime, hus he ime window. In he
e ie ed co pus, his modeling is mos ly ound in wo k o ce scheduling
p oblems (B usco,2008;B usco and Jacobs,2000), bu also wi hin he
ou ing li e a u e when d i e meal b eaks need o be scheduled (Kim
e al.,2006;Coelho e al.,2016).
In e ms o complexi y, ime window cons ain s a e o en easy o
in eg a e. This modeling adds a numbe o ac i i ies o schedule ( he
b eaks) on op o he p oduc i e asks, which cons ain sligh ly he
p oblem. Al hough he conside a ion o ime window cons ain s was
challenging in he ea ly li e a u e on he opic (Solomon and Des osie s,
1988), hey a e nowadays common and well-s udied ea u es in a ious
classes o p oblems. In mos applica ions, hey do no p o ide a signi -
ican algo i hmic challenge compa ed o he o he cons ain s in he
model.
Fo wa d-backwa d cons ain s. Fo wa d-backwa d cons ain s a e com-
monly used when dealing wi h b eak scheduling. Wi h his app oach,
a se o ules is de ined o ensu e ha he quan i y, du a ion, and/o
placemen o b eaks comply wi h ei he wo k egula ions o company
policies. The objec i e is o gi e he ma hema ical models mo e lexi-
bili y han using ime windows while p ohibi ing schedules ha a e o
poo in e es in p ac ice. Fo example, scheduling a b eak jus a e he
shi s a s, o scheduling all he b eaks one a e ano he . In p ac ice,
es b eaks should be sp ead o e he wo k shi o enable eco e y. The
ules de ining he o wa d–backwa d cons ain s a y om one pape o
ano he , mo e de ails can be ound in P une e al. (2024), Regula o y
b eaks and Wo k-s e ch du a ions, bu some examples would be: ‘‘The
i s b eak should be scheduled a leas 2 h a e he shi s a s’’, ‘‘A
shi should con ain one lunch b eak o a minimum du a ion o 1h’’ o
‘‘The wo k du a ion be ween wo consecu i e b eaks mus be a leas
2 h, and a mos 4h’’.
A mo e heo e ical and gene al desc ip ion o o wa d–backwa d
cons ain s can be ound in Widl and Musliu (2014) and Rekik e al.
(2008). The e a e wo main a eas o applica ion o o wa d–backwa d
cons ain s: shi scheduling and ehicle ou ing. In ehicle ou ing,
hese ules a e usually aligned wi h o icial egula ions. Se e al coun-
ies ha e legisla ed on uck d i e s’ wo king imes. The ules depend
on he coun y, bu ollow he same amewo k (Goel,2009;P esco -
Gagnon e al.,2010;Rancou e al.,2013). Wo k- es scheduling is he
o he main applica ion a ea o o wa d–backwa d cons ain s. The e
is no egula ion o comply wi h in his case, howe e , he lexibili y
allowed by his modeling is s ill wo h conside ing (Gä ne e al.,2001;
Res epo e al.,2012;Quimpe and Rousseau,2010).
The lexibili y gained om his modeling app oach o en leads o
be e quali y solu ions compa ed o ime window cons ain s (Rekik
e al.,2008,2010). Howe e , his lexibili y comes a a cos in e ms o
complexi y. One common echnique o deal wi h his complexi y is o
conside b eak placemen implici ly. This can be done di ec ly in he
o mula ion (Rekik e al.,2010), o hese aspec s can be con exi ied in
he subp oblem wi hin a decomposi ion amewo k (P esco -Gagnon
e al.,2010;Ceselli e al.,2009).
7.3. Pa ame e s
The HFs migh be in eg a ed di ec ly in he pa ame e s o he
p oblem, ei he wi h a ying ask ime, a ying quali y, o a he e ogeneous
wo k o ce. The impac on op imiza ion models a ies depending on
which aspec is conside ed and how i is in eg a ed. Ne e heless, one
can assume ha conside ing he e ogeneous cha ac e is ics will o en
inc ease he compu a ional bu den ha ollows by he need o conside
ex a dimensions in he ma hema ical models. These ex a dimensions
may be he ime needed o cap u e he change o a pa ame e o e he
shi . I can also be he explici conside a ion o employees o ake in o
accoun hei di e en skills. Finally, he model can be s ochas ic in
na u e.
7.3.1. Va ying ask ime
Va ying ask ime is one o he main ways o model HFs in op imiza-
ion models (see Fig. 2). I is indeed a e y lexible ool, which can be
adap ed o a wide ange o HFs, and i s well in he OR amewo ks.
The idea is a he simple: ins ead o being a ixed pa ame e , he
p ocessing ime o a ask pe o med by a human ope a o a ies. This
a ia ion can be pu ely s ochas ic o model he in insic a iabili y
o he pe o mance o a human ope a o (Chiang and U ban,2006;
Egilmez e al.,2014;So sko e al.,2015). Howe e , mos o he ime,
p ocessing imes a e de e minis ic and depend on he cha ac e is ics o
he ope a o (e.g., skill, a igue le el). In e ms o applica ion ields, his
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T. P une e al.
modeling is mos ly used in ope a ional p oblems, whe e he g anula i y
o he modeling allows us o conside indi idual execu ion imes.
Scheduling p oblems a e a na u al applica ion, o example, p oduc-
ion scheduling (Lee and Wu,2009;Gong e al.,2020b), wo k o ce
scheduling (Yilmaz,2020), o assembly lines (Os e meie ,2020). O he
applica ions a e ound in logis ics, such as wa ehousing (Ma usiak
e al.,2017), o ehicle ou ing (Yan e al.,2019). Fu he mo e, his
opic has also been s udied om a mo e heo e ical pe spec i e wi h
complexi y s udies (Janiak and Rudek,2010;Lee and Wu,2009;Wu
and Lee,2008).
Se e al ac o s a e ound o a ec he p ocessing ime o asks, as
he wo k o ce skills (Akyol and Baykasoğlu,2019;Azizi e al.,2010),
o he posi ion o he ask in he shi schedule (Biskup,1999;O o
and O o,2014). The ask ime depending on i s posi ion in a shi is
a e y common modeling app oach o p oblems ela ed o employee
lea ning since he epe i ion o a simila ask imp o es he e iciency
o he ope a o . Howe e , i is also used o model a a igue e ec ,
whe e he p oduc i i y o an ope a o dec eases as a igue inc eases
owa d he end o a wo k shi (Sanchez-He e a e al.,2019;Wang
e al.,2020). Ano he e y simila modeling echnique is o adjus he
p ocessing ime o a ask depending on he sum o he p ocessing imes
o asks scheduled be o e. This is a e y common modeling app oach o
he lea ning p ocess (Zhang and Gen,2011;Wang e al.,2019). I is also
used when dealing wi h a igue, whe e he p oduc i i y o an employee
dec eases acco ding o a gi en unc ion ha depends on he ime passed
since he las es b eak. In some pape s, lea ning and a igue e ec s a e
conside ed oge he , wi h hei opposi e e ec s on p ocessing ime (Gi i
e al.,2015b;Os e meie ,2020).
The impac on op imiza ion models a ies depending on which
aspec is conside ed, and how i is in eg a ed. Ne e heless, one can
assume ha conside ing a ying ask imes will o en inc ease he
compu a ional bu den o he models. I is howe e in e es ing ha o
scheduling models he p oblems seem o s ay polynomial in se e al
cases (Wu and Lee,2008;Lee and Wu,2009).
7.3.2. Va ying quali y
Some esea che s conside ha he quali y le el o he p oduc ion
is no a ixed exogenous pa ame e , bu depends on he cha ac e is ics
o he employee ope a ing he gi en ask. Se e al ac o s can be used
o measu e he quali y le el o p oduc ion. The mos common one
is he skill le el o he employee pe o ming he ask (De B uecke
e al.,2015). A a ying quali y le el is mainly used when dealing wi h
wo k o ce planning when i depends on he skill le el (No man e al.,
2002;McDonald e al.,2009). Lea ning, as he de e mining ac o o he
quali y le el, has also been used in he li e a u e. One can especially
ci e (Gi i e al.,2015b) and Gi i e al. (2015a), who ex end he lea ning
o ge ing a igue eco e y model o accoun o a quali y imp o e-
men in he lea ning p ocess. Ano he possibili y is o model he e o
a e h ough a p obabilis ic amewo k. Khan e al. (2012) and Khan
e al. (2014) s udy he human e o axonomy and classi ica ion wi h
associa ed p obabili ies. In some o he applica ions, e o s a e modeled
using andom a iables wi h known densi y unc ions (Shin e al.,2018;
Gilo a e al.,2020).
Employee a igue is ano he majo sou ce o e o s, and se e al
empi ical s udies show a clea link be ween human a igue and p oduc-
ion quali y (Yung e al.,2020). This sou ce o e o s is, o ins ance,
a common conside a ion in wa ehousing s udies ocusing on o de
picking (Zhao e al.,2019;G osse e al.,2017b).
7.3.3. He e ogeneous wo k o ce
O he models wo h men ioning a e hose conside ing a he e oge-
neous wo k o ce. His o ically, he homogenei y o he wo k o ce is a
common assump ion in OR models. I is indeed gene ally e y con e-
nien om a ma hema ical poin o iew, and he hypo hesis is alid o
a numbe o applica ions. Howe e , in some cases, a ine g anula i y in
he modeling o he wo k o ce is necessa y o conside ce ain e ec s.
This modeling is no a ached o a pa icula HAM and is mos o he
ime used wi hin ano he modeling app oach (e.g., objec i e unc ion,
o cons ain s). An in e es ed eade is e e ed o Ka i aee e al. (2021)
o a dedica ed e iew on he opic.
A he e ogeneous wo k o ce is na u ally encoun e ed when dealing
wi h skills, o he wise all employees would ha e he same skill se . This
case is deno ed Sin he esul ables. The unde lying idea o in eg a ing
skills in an op imiza ion model can be wo old: (i) Employees as a
he e ogeneous se o esou ces. In his case, he skills ep esen di e en
jobs o quali ica ions, en o ced ia compa ibili y cons ain s (Calza a a
e al.,2019a;Hochdö e e al.,2018;Joo and Kim,2013), o (ii) Em-
ployees as a single se o esou ces, ye wi h di e en h oughpu /cos .
In his case, he skills ep esen he p o iciency o he employee on a
se o asks, en o ced wi h a ying ask imes, o a ying quali y (Hong,
2018;Khan e al.,2012;Ma usiak e al.,2017). No e ha nei he case
is exclusi e o each o he .
Rela ed o skills, lea ning a es can also a y be ween employees.
This case is deno ed LR in he esul ables. When wo king wi h lea ning
cu es, i is easonable o suppose he e is some disc epancy be ween
employees in e ms o lea ning a es. This modeling is mos ly used in
s udies ha ocus on modeling wi h mo e accu acy he pe o mances
and hei a ia ions in a wo k o ce. I has mainly seen use in wo k o ce-
ocused p oblems, like wo k o ce planning (Fowle e al.,2008;A ia
e al.,2014), bu also wa ehousing (G osse and Glock,2015;G osse
e al.,2013), machine scheduling (Ma ichel am e al.,2020), shi
scheduling (Gans and Zhou,2002), and job o a ion (Ayough e al.,
2020).
The he e ogenei y o he wo k o ce can also be based on o he in-
insic p ope ies o i s componen s. In he esul ables, his is e e ed
o as PP o Physical P ope ies, and CPP o Cogni i e and Psychosocial
P ope ies. These pa ame e s a e mos ly isk ac o s o de eloping
WMSD and a e accoun ed o when compu ing he es ima ion o he
ER o a wo k si ua ion. I is indeed known ha indi iduals ha e
di e en chances o de eloping occupa ional diseases when acing a
gi en wo k si ua ion (B idge ,2018). This can depend, o example,
on he age o he pe son (Yi and Wang,2017), his/he li ing capa-
bili ies (Ca nahan e al.,2000), he physical a igue he/she can sa ely
sus ain (Ra anamanee e al.,2015) o e en i s smoking and d inking
habi s (Yi and Chan,2015). Howe e , mos o he ime he indi idual
physical pa ame e s can be seen as skills in he sense ha hey a ec
he p ocessing imes o asks o lea ning cha ac e is ics. Conce ning
psychosocial cha ac e is ics, hey can be used o c ea e eams wi h
compa ible decision-making s yles (Azadeh e al.,2017), o accoun
o he ca e ulness o he employees in a con ex o haza dous wo k
en i onmen (Lazze ini and Pis olesi,2018), o o conside he mo i a-
ion and pe sonali y o indi iduals when assigning asks (O hman e al.,
2012).
The las pa ame e ha can di e be ween employees is hei indi-
idual wo k p e e ences, no ed Pin he esul ables. This pa ame e
is used when accoun ing o he sa is ac ion o he wo k o ce, ei he as
an objec i e unc ion, o h eshold cons ain s ha en o ce a minimal
sa is ac ion le el o each employee, in a sea ch o equi y (Ruiz-To es
e al.,2015,2019;Akba i e al.,2013).
Modeling he wo k o ce as he e ogeneous migh ha e a subs an ial
impac on decision models. The mos ob ious one is ha a pa ame e
ha was cons an o e he wo k o ce now depends on he conce ned in-
di idual. This means ha he decision a iables should be able o model
he indi idual assignmen /schedule o he employees. No e ha his is
mos o he ime al eady he case in he p oblems s udied in his e-
iew, especially he wo k o ce- ela ed ones. This indi idual-dependen
pa ame e can add some complexi y o an exis ing model. Howe e ,
i also b eaks symme ies, which is o en bene icial o he pe o -
mance o solu ion app oaches, especially when using ma hema ical
p og amming-based me hods (Ma go ,2010).
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T. P une e al.
8. Discussion and u u e esea ch
The analysis o he collec ed ma e ial b ings ou a la ge a ie y
o wo ks on he opic o in eg a ing HFs in o decision models o
logis ics and manu ac u ing sys ems, bo h in e ms o s udied p oblems,
modeling app oaches, and conside ed HFs. Howe e , in ligh o he
insigh s gained om he c oss-analysis pe o med in bo h pa s o he
e iew, some gaps s ill emain o be illed. In his sec ion, we discuss
he collec ed ma e ial and p opose some esea ch di ec ions ha ap-
pea p omising o u u e wo ks, namely he ela ionship be ween HFs
and ields o applica ion (Sec ion 8.1), he unde - ep esen ed decision
p oblems (Sec ion 8.2), and he modeling o mode n indus ial p ac ices
(Sec ion 8.3). The eade in e es ed in a s udy o he di e en modeling
o a HF depending on he ield o applica ion is e e ed o he second
pa o his wo k (P une e al.,2024).
8.1. Discussion on he ela ionship be ween HFs and ields o applica ion
Upon e iewing he collec ed ma e ial, i becomes e iden ha
he p eponde ance o he s udied HFs a ies be ween logis ics and
manu ac u ing p oblems, as illus a ed by Table 13 ha highligh s he
ela ionships be ween decision p oblems and HFs. This obse a ion
can be mainly explained by wo ac o s: ei he he combina ion is
i ele an o he s udy, o he opic has no been ho oughly exploi ed
and p esen s a di ec ion o u u e esea ch.
Lea ning. While ex ensi ely s udied in some applica ions (e.g., p o-
duc ion planning and scheduling), lea ning is seldom in eg a ed in o
o he scheduling p oblems whe e i could be ele an . This is espe-
cially ue o applica ions whe e employees pe o m di e en kind
o asks, and hus whe e lea ning and o ge ing occu when he ask
assignmen changes: assembly line balancing, job o a ion, and wo k-
es scheduling. Accoun ing o lea ning in job o a ion would allow o
disc imina e equi alen solu ions in e ms o pain ulness (Ayough e al.,
2020). Vehicle ou ing is ano he ield whe e lea ning is la gely absen ,
despi e he ele ance o he lea ning p ocess o d i e s. Few wo ks in
he li e a u e accoun o d i e lea ning (Ba ini e al.,2015;Ulme
e al.,2020;Bakke e al.,2021), ye d i e amilia wi h he oads
may an icipa e a ic jams and use al e na i e ou es, he eby educing
he a el ime. The lea ning p ocess also applies o he cus ome
loca ions, whe e se ice imes migh be educed when d i e s become
mo e amilia wi h he loca ions.
Fa igue. The opic is s udied in almos all ields, o en using di -
e en HAMs. Al hough se e al ields ex ensi ely s udy he a igue
phenomenon ia di e en HAMs, he explici conside a ion o when o
schedule es b eaks is mainly p e alen in ehicle ou ing and wo k-
es scheduling, despi e being a powe ul ool o educe a igue. The
use o wo k-s e ch du a ion o explici ly schedule es b eaks is a
p omising esea ch di ec ion in a eas like wa ehousing o job o a ion.
The ou ing li e a u e is ano he esea ch di ec ion o a igue con-
side a ions. Al hough his in e ac ion is p o usely s udied, i is almos
exclusi ely using hou s o se ice egula ions. The wo k o Fu e al.
(2022) is he only excep ion, as i accoun s o biological indica o s
o schedule d i e s’ b eaks. Fu he s udies on ehicle ou ing wi h
al e na i e a igue HAMs would en ich he li e a u e on he opic.
Finally, ene gy expendi u e is only used wice in he job o a ion
co pus, despi e p o iding a con enien indica o o es ima e he ER o
balance be ween employees.
WMSD isk assessmen me hods. A la ge class o p oblems in eg a es
WMSD isk. Shi scheduling and wo k o ce planning a e he wo
no able excep ions, which is unsu p ising as hey conside longe ime
ho izons. Despi e being challenging o model, he balancing o he
WMSD ER is, howe e , s ill ele an as he isk builds o e ime, and
mo e wo ks should pu sue his di ec ion (Caballini and Paolucci,2020).
Cogni i e, psychosocial and pe cep ual ac o s. These ac o s a e o e all
unde s udied, despi e being ele an o a la ge class o p oblems. Noise
and ib a ion exposu e, o example, would be wo h in es iga ing in
all he applica ions in ol ing hea y machine y, such as assembly line
balancing, p oduc ion scheduling and planning, wo k- es scheduling,
o job o a ion. Al hough employee sa is ac ion is a ely conside ed in
he wo k o ce planning li e a u e, i plays an impo an ole in he a e
o u no e ha is o en conside ed in his class o p oblems (Doan
e al.,2022). Finally, bo edom would be an in e es ing esea ch di-
ec ion o he li e a u e on wa ehousing and assembly line balancing,
gi en he highly epe i i e na u e o he co esponding jobs.
8.2. Unde - ep esen ed decision p oblems
Cases whe e he s udy is no ele an . Some HFs may no be ele an o
be s udied wi hin speci ic applica ions. The i s example ha comes o
mind is pe cep ual and en i onmen al ac o s: i does no make sense
o s udy hea o noise exposu e when hey do no pose a isk ac o in a
pa icula applica ion, simila ly isual conside a ions a e only ele an
in some applica ions (e.g., when an ope a o spends an impo an
po ion o he ime isually sea ching o i ems o pick). O he examples
include he HFs ha a e ele an o he applica ion a hand, bu
whe e he op imiza ion p oblem canno modi y he associa ed isk. This
is o ins ance he case o pos u e- ela ed isk in he ou ing li e a-
u e: while he p olonged si ing posi ion may be ha m ul o d i e s,
hei exposu e le el is only ma ginally a ec ed by he ou ing plan.
Finally, he e a e some cases whe e he HF is simply no applicable
o some classes o p oblems. The mos s aigh o wa d example is
when s udying p oblems ha do no in eg a e ime aspec s, such as
layou design o in en o y managemen , whe e he scheduling o es
b eaks is no applicable. To ci e ano he example, skills cons i u e he
mos s udied opic, bu a e no ele an when applied o p oduc ion
planning, as indi idual employees a e no modeled. Hou s o se ice
egula ions a e almos exclusi ely s udied in he ou ing li e a u e,
because o he complex legisla ion en o cing hem (P une e al.,2024).
When applied o o he p oblems, simila a igue conside a ions a e
modeled ia wo k-s e ch du a ion. Finally, a igue can be conside ed
as a ‘‘sho - e m’’ e ec , and hus i is unde s andably unde -s udied
in p oblems wi h long ime ho izons ha do no decompose a shi
(shi scheduling, wo k o ce planning and assignmen ). The in e es ed
eade is e e ed o P une e al. (2024) o addi ional discussions on
he c oss-dependency be ween HF and applica ion.
Layou design p oblems. The design o wo ks a ion layou is one o he
main s udy di ec ions in HF/E and is an impo an le e o e gonomic
imp o emen in a wo k si ua ion (B idge ,2018). Howe e , he OR
li e a u e on he opic is sca ce. Mo e han designing wo ks a ions pe
se, o ins ance, s udied in An onio Diego-Mas e al. (2017), op imiza-
ion algo i hms enable a cos –bene i analysis o di e en solu ions.
Fo example, Raza i e al. (2014) and Finco e al. (2020a) s udy
he economic and e gonomic impac s o using p o ec i e equipmen
o , espec i ely, noise and ib a ion exposu e. Wi hin wa ehousing
logis ics, Glock e al. (2019a) s udy he possibili y o o a ing palle s
o enable easie access o he s o ed i ems, and Calza a a e al. (2017)
o (Calza a a e al.,2019a) s udy he design o he s o age acks and
assess he economic and e gonomic impac s o di e en echnological
solu ions. O e all, mo e wo ks should s udy he impac o di e en
echnological solu ions by he means o op imiza ion p oblems.
Ma i ime logis ics p oblems. Despi e being an impo an class o p ob-
lems wi hin logis ics (Ch is iansen e al.,2007), he in eg a ion o HFs
emains lacking wi h hese p oblems. Ac ually, a single wo k has been
e ie ed by ou sea ch me hodology (Caballini and Paolucci,2020).
Fu u e esea ch should de e mine which HFs a e impo an o his
esea ch s eam, and in eg a e hem in models. Fo ins ance, a igue
and skills may be ele an conside a ions o ships sailing a ound he
clock.
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
23
T. P une e al.
Table 13
C oss-analysis o he human ac o s s udied by ield o applica ion.
Skills
Lea ning
Ene gy expendi u e
Res allowance
Fa igue e ec
Lea n o ge a igue eco e y
Meal b eaks
Hou s o se ice egula ions
Wo k-s e ch du a ion
Whole body
Manual handling
Wo king pos u e
Repe i i e mo emen s
Mo i a ion and bo edom
Sa is ac ion
Psychosocial ac o s
Cogni i e ac o s
Noise exposu e
Visual conside a ions
Hea exposu e
Vib a ion exposu e
Wa ehousing 4 3 6 2 1 3 4 1
Vehicle ou ing 4 3 2 1 8 23 1 1 3 1
P oduc ion scheduling 14 31 2 1 6 1 2 1 1 3 2 2 2 4 1 1
Assembly line balancing 20 2 8 2 1 1 1 4 6 6 1
P oduc ion planning 18 4 4 1 2 1 1
Design o sys ems 5 1 4 1 1 1 1 2 1 1 1 1 1
Job o a ion 10 1 2 1 3 6 2 4 4 5 1
Wo k- es scheduling 1 2 1 6 16 1 12 1 2
Shi scheduling 9 1 1 1 1 1 5 1
Wo k o ce planning 16 6 1 1 1
Wo k o ce assignmen 10 3 1 2 3 1 1
To al 93 69 31 11 18 4 26 24 12 5 19 16 15 8 12 8 3 12 4 3 3
EURO Jou nal on T anspo a ion and Logis ics 13 (2024) 100136
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