Alp, Enes; Pi ola, Fabiana; Sala, Robe o; Pezzo a, Giudi a; Kuhlenkö e , Be nd
A icle — Published Ve sion
Ope a i e se ice deli e y planning and scheduling in
P oduc -Se ice Sys ems
Se ice Business
P o ided in Coope a ion wi h:
Sp inge Na u e
Sugges ed Ci a ion: Alp, Enes; Pi ola, Fabiana; Sala, Robe o; Pezzo a, Giudi a; Kuhlenkö e , Be nd
(2024) : Ope a i e se ice deli e y planning and scheduling in P oduc -Se ice Sys ems, Se ice
Business, ISSN 1862-8508, Sp inge , Be lin, Heidelbe g, Vol. 18, Iss. 2, pp. 161-192,
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1 3
REVIEW ARTICLE
Ope a i e se ice deli e y planning andscheduling
inP oduc ‑Se ice Sys ems
A sys ema ic li e a u e e iew
EnesAlp1 · FabianaPi ola2· Robe oSala2· Giudi aPezzo a2·
Be ndKuhlenkö e 1,3
Recei ed: 23 Oc obe 2023 / Accep ed: 20 Ma ch 2024 / Published online: 21 Ap il 2024
© The Au ho (s) 2024
Abs ac
To na iga e compe i ion and c ea e highe alue o cus ome s, manu ac u ing
companies a e mo e and mo e adop ing he s a egy o Se i iza ion by en iching
hei p oduc o e ing wi h se ices in solu ions known as P oduc -Se ice Sys ems
(PSS). While he p o ision o PSS p esen s nume ous ad an ages o cus ome s and
p o ide s, hey also pose signi ican challenges, pa icula ly in he ope a i e se -
ice deli e y planning and scheduling. This s udy aims o iden i y decision-suppo
wi hin his con ex by conduc ing a sys ema ic li e a u e e iew. The analysis unco -
e s limi a ions in exis ing app oaches and unde sco es unadd essed esea ch gaps
emphasizing he need o u he de elopmen o decision-suppo sys ems o PSS
ope a ion.
Keywo ds P oduc -Se ice Sys ems· Ope a i e se ice deli e y planning·
Scheduling
1 In oduc ion
O e he pas ew decades, manu ac u ing companies ha e aced signi ican ma -
ke ola ili y (Pombo and F anco 2023). In esponse, and o di e en ia e hem-
sel es om compe i o s, companies ha e inc easingly adop ed he s a egy o
* Enes Alp
[email p o ec ed]ub.de
1 Chai o P oduc ion Sys ems (LPS), Ruh -Uni e si ä Bochum, Indus ies aße 38C,
44894Bochum, Ge many
2 Depa men o Managemen , In o ma ion andP oduc ion Enginee ing, Uni e si y o Be gamo,
Viale Ma coni 5, Dalmine, BG, I aly
3 Cen e o heEnginee ing o Sma P oduc -Se ice Sys ems (ZESS), Ruh -Uni e si ä
Bochum, Hans-Dobbe in-S aße 8, 44803Bochum, Ge many
162
E.Alp e al.
1 3
Se i iza ion (Le-Dain e al. 2023). Se i iza ion e e s o he end o compa-
nies ex ending hei alue p oposi ions by o e ing se ices associa ed wi h hei
p oduc s (Khan a e al. 2021), ma ke ed as P oduc -Se ice Sys ems (PSS). PSS
a e designed o con inuously mee cus ome needs while educing en i onmen-
al impac , making hem highly dynamic and complex sys ems (Gaia delli e al.
2021). Usually, PSS a e p o ided wi hin inno a i e business models ha di -
e om adi ional ones, solely ocused on high sales olumes, as he PSS ones
emphasize he use, a ailabili y, o bene i s o he p oduc (Mo o e al. 2022).
While PSS o e bene i s such as isk a oidance o cus ome s and con inuous
e enues o p o ide s (Reim e al. 2015), hei widesp ead adop ion has no been
as apid as ini ially an icipa ed (B issaud e al. 2022). In ac , he e ha e been
ins ances o dese i iza ion, whe e companies ha e scaled back o discon inued
hei PSS o e ings. This is due o nume ous challenges associa ed wi h p o iding
PSS, equi ing companies o unde go a pa adigm shi in hei hinking o achie e
success. Se ice ac i i ies should no longe be ea ed as eac i e addi ional asks
bu a he as key ac i i ies o alue c ea ion (Kowalkowski e al. 2017). Fo many
manu ac u ing companies, c ea ing alue h ough se ices ep esen s a isk, as
ine ec i e o ine icien se ice deli e y can esul in high penal ies o e ode us
be ween he cus ome and p o ide (Reim e al. 2016). Thus, achie ing e ec i e,
and e icien se ice deli e y is c ucial in PSS business models.
The e ec i eness and e iciency o se ice deli e y a e mainly de e mined in
he ope a i e se ice deli e y planning. Howe e , his ask is highly complex
and equi es sui able decision-suppo sys ems o make op imal decisions ha
enhance cus ome sa is ac ion and minimize cos s (Sala e al. 2019). Acco dingly,
se e al app oaches ha e been p oposed in he li e a u e o suppo ope a i e se -
ice deli e y planning and scheduling. This s udy aims o explo e he s a e o he
a in hese app oaches de eloped speci ically o he ope a i e se ice deli e y
planning and scheduling in he con ex o PSS. The esea ch ques ions (RQ) o
his s udy we e o mula ed as ollows:
RQ1:Wha is he cu en s a e o he a o ope a i e se ice deli e y plan-
ning and scheduling app oaches in he con ex o PSS?
RQ2:Wha a e he limi a ions o exis ing app oaches, and wha a e he
equi emen s o new app oaches?
RQ3:Wha is he sui able esea ch agenda o u he ad ance he ield?
To answe hese esea ch ques ions, a sys ema ic li e a u e e iew ollow-
ing he me hodology ou line by om B ocke e al. (2009) was conduc ed. The
e iew’s scope is de ined using he axonomy o Coope (1988). Hence, ou li -
e a u e e iew ocuses on me hods and applica ions wi h he goal o iden i ying
he cen al issues ela ed o ope a i e se ice deli e y planning and scheduling
app oaches in he con ex o PSS. While we aim o achie e exhaus i e co e age,
his a icle is di ec ed o gene al schola s, he eby adop ing a comp ehensi e
app oach.
The emainde o he a icle is o ganized acco ding o he amewo k o om
B ocke e al. (2009) Sec . 2 se es as he concep ualiza ion o he opic and
163
1 3
Ope a i e se ice deli e y planning andscheduling in…
includes he heo e ical backg ound o PSS business models, ope a i e se ice
deli e y planning and scheduling, and he ela ed challenges. Sec ion3 desc ibes
he li e a u e sea ch p ocess in de ail. Sec ion4 p esen s he key indings de i ed
om he li e a u e analysis and syn hesis phase. A e discussing he esul s in
he esea ch con ex , a u u e esea ch agenda is gi en in Sec .6.
2 Theo e ical backg ound
2.1 P oduc ‑Se ice Sys ems
P oduc -Se ice Sys ems (PSS) can be de ined as “a ma ke able se o p oduc s and
se ices capable o join ly ul illing a use ’s need” (Goedkopp e al. 1999). Manu-
ac u ing companies adop PSS as a s a egic app oach o achie e a ious objec i es,
including e enue g ow h, cus ome ela ionship de elopmen , and en i onmen al
sus ainabili y imp o emen (Li e al. 2020). PSS business models a e gea ed owa d
he long e m and can be ca ego ized based on he balance be ween angible p od-
uc s and in angible se ices in he alue p oposi ion (Mon 2002). Tukke (2004)
ou lines h ee dis inc business models, as depic ed in Fig.1. P oduc -o ien ed busi-
ness models in ol e selling he echnical p oduc while o e ing ela ed se ices like
main enance o end-o -li e se ices. A ailabili y-o ien ed business models ocus on
selling he a ailabili y o he p oduc , wi h he p o ide assuming esponsibili y o
ensu ing gua an eed a ailabili y and acing penal ies i he p oduc is no a ailable.
Resul -o ien ed business models shi he ocus om he p oduc i sel o he desi ed
ou pu such as he pay-pe -p in concep used by copie manu ac u e s (Tukke
2004). Cus ome s who engage wi h PSS can bene i by ans e ing ac i i ies o he
p o ide , allowing hem o ocus on co e compe encies, minimizing high- isk in es -
men s, a oiding capi al lock-up, and gaining access o new echnologies (Meie e al.
2011b).
The adop ion o se ice-in ensi e PSS business models, which in ol e he
ans e o ac i i ies and esponsibili ies om cus ome s o p o ide s, in oduces
Fig. 1 PSS Business Models (Meie e al. 2011b; Tukke 2004)
164
E.Alp e al.
1 3
inc eased isks – encompassing echnical, beha io al, and deli e y compe ence
aspec s – o PSS p o ide s (He zog e al. 2014). Technical isks a ise om unex-
pec ed b eakdowns o he echnical p oduc , while beha io al isks ela e o he pos-
sibili y o cus ome s ea ing he p oduc less ca e ully since hey do no own i .
Deli e y compe ence isks e lec he p o ide ’s abili y and capaci y o ul ill he
alue p oposi ion e ec i ely. Inadequa e se ice deli e y no only incu s high pen-
al y cos s o he p o ide bu also endange s cus ome us and sa is ac ion (Reim
e al. 2016). Se ice deli e y p ocesses encompass all necessa y ac i i ies o eal-
ize he alue p oposi ion such as “main enance p ocedu es, echnological upg ades,
spa e pa deli e ies” o simila (Meie e al. 2013a). As a consequence o hese
inno a i e business models, e ec i e and e icien se ice deli e y ac oss all cus-
ome s assumes pa amoun impo ance o PSS p o ide s.
2.2 Se ice planning inP oduc ‑Se ice Sys ems
Se ice planning in PSS can be ca ego ized in o s a egic, ac ical, and ope a i e
planning (see Fig.2). S a egic planning in ol es long- e m decisions made du ing
he design and de elopmen phase o a PSS. Tac ical planning ocuses on mid- e m
decisions whe eas ope a i e planning deals wi h sho - e m decisions made in he
ope a ions o use phase and addi ionally, includes he ask o scheduling (Do ka
e al. 2014). In his con ex , he e m planning pe ains o de e mining “wha and
how” while scheduling pe ains o “who and when” (Baldwin and Bo doli 2014).
The ollowing subsec ions explain he di e en planning dimensions in de ail.
2.2.1 S a egic se ice planning
The main ask o s a egic planning is o de e mine and build up all necessa y
esou ces o se ice deli e y in he igh quan i y and quali y. These esou ces
encompass echnicians, spa e pa s, o ools, wi h echnicians being he mos c i ical
esou ces. By employing o aining echnicians, PSS p o ide s ha e o ensu e he
a ailabili y o quali ied echnicians in he long e m o achie e e ec i e and e icien
se ice deli e y (Meie e al. 2012). In cases whe e in e nal esou ces a e insu i-
cien , PSS p o ide s may op o es ablish deli e y ne wo ks (Lagemann e al. 2015),
which in ol e collabo a ion among di e en companies o deli e he PSS alue
p oposi ion o he espec i e se ices. To ensu e he a ailabili y o he equi ed
Fig. 2 Se ice Planning in PSS (Meie e al. 2012)
165
1 3
Ope a i e se ice deli e y planning andscheduling in…
esou ce capaci ies, he PSS p o ide can en e pa ne ships wi h e.g., componen o
se ice supplie s (Meie e al. 2010).
Fo ecas ing he equi ed esou ces p esen s a signi ican challenge in s a egic
planning (Meie e al. 2012). To add ess his, Lagemann and Meie (2014) in oduce
a simula ion-based capaci y planning app oach as a decision-suppo sys em o
s a egic planning. This app oach enables o de e mine capaci y equi emen s unde
he e ec s o di e en scena ios. Simila ly, Zheng e al. (2017) p esen an app oach
based on uzzy mul iple linea eg ession o an e icien build-up o capaci ies
despi e he ola ili y o se ice eques s.
2.2.2 Tac ical se ice planning
The goal o ac ical se ice planning is o ensu e he a ailabili y o he esou ces
needed in he ope a ion phase. Mid- e m decisions wi hin his ealm include he
managemen o aca ions as well as he o ganiza ion o aining o he echnicians.
Addi ionally, app op ia e s ocks o spa e pa s need o be planned o each main e-
nance cen e (Lagemann 2015).
An exempla y app oach o ac ical se ice planning was published by Agniho h i
and Mish a (2004). The au ho s ocused on he misma ch be ween he exis ing skills
o he echnicians and he equi ed skills o each o de . Using a simula ion model,
he au ho s analyzed he ques ions abou how many echnicians should be ained
and when aining amo ized (Agniho h i and Mish a 2004). A simila app oach was
in oduced by Gu sche (2015). She concen a ed on he human ac o when mis-
ma ches be ween equi ed and exis ing compe encies occu and analyzed he impac
on employee sa is ac ion (Gu sche 2015).
2.2.3 Ope a i e se ice deli e y planning andscheduling
The objec i e o he ope a i e se ice deli e y planning and scheduling in he con-
ex o PSS is o “p o ide he esou ces which a e needed o he deli e y o se ices
du ing he ope a ion [phase] in he igh quali y and quan i y a he co ec ime and
place” (Meie e al. 2012). Fo his, a dispa che ma ches and assigns app op ia e
esou ces o he p esen deli e y p ocesses whe he hey a ise om planned asks
o unexpec ed machine b eakdowns (see Fig.3). He o she can ei he choose om
he in e nal esou ces o he esou ces o he ne wo k o subcon ac he deli e y
p ocess o o he pa ne s (Meie e al. 2011b). As a esul , he dispa che gene a es
plans ha a e going o be execu ed in he sho e m.
Fo he assessmen o plans, Meie e al. (2013c) in oduce a lis o key pe o -
mance indica o s (KPIs). Acco ding o he au ho s, he mos impo an KPIs o he
assessmen o he deli e y planning and scheduling pe o mance a e Mean ime o
p oblem solu ion, cos s, e enue, a el ime p opo ion, esou ce u iliza ion, and
escheduling quo a ollowed by he a es o Fi s ime ix and On ime deli e y
(Meie e al. 2013c).
The e a e huge simila i ies and in e sec ions be ween Field Se ice Managemen
(FSM) and especially he ield se ice planning and he ope a i e se ice deli e y
planning and scheduling in he con ex o PSS. Vössing (2017) desc ibes he ield
166
E.Alp e al.
1 3
se ice planning p oblem as “Spa ially dis ibu ed cus ome eques s need o be
alloca ed o spa ially dis ibu ed echnicians,” whe eby eques s can be u gen o
less c i ical asks. The au ho classi ies he p oblem as a “unique a ian o he ehi-
cle ou ing p oblem” (Vössing 2017), which is a p oblem p esen ed in he la e 1950s
cha ac e ized by inding he op imal ou es o a numbe o ehicles ha ha e o
se e cus ome s ha a e geog aphically sca e ed (Dan zig and Ramse 1959).
Despi e he simila i ies in he basic s uc u e o p oblem unde s anding, he e a e
also some di e ences and peculia i ies in he ope a i e se ice deli e y planning
and scheduling in he con ex o PSS due o he inno a i e business model logic. In
he a ea o ield se ice planning, i is no clea , whe he he company unde con-
side a ion is an o iginal equipmen manu ac u e (OEM) p o iding addi ional se -
ices o a pu e se ice p o ide ha independen ly om any asse de elopmen and
owne ship o e s se ices. Besides, in o ma ion on business models and emune a-
ion o se ices (e.g., emune a ion o indi idual se ice acco ding o ime spen o
lump-sum emune a ion acco ding o main enance con ac s) as well as po en ial
penal ies o a dy se ice deli e y is mos ly no speci ied (Vössing e al. 2018). In
con as , PSS a e cha ac e ized by he mu ual and in eg al design and de elopmen
o a alue p oposi ion wi h he conside a ion o he use phase. Hence, al eady in he
ea ly s ages o PSS de elopmen , s a egic decisions abou se ice deli e y a e made
(Hazée e al. 2020). The echnical knowledge abou own p oduc s in PSS, esul -
ing om he de elopmen phases as well as he high cus ome in e ac ion in he
use phase, has a high impac on he e ec i eness and e iciency o se ice deli e y.
In gene al, he goal behind p o iding PSS is o es ablish a ela ionship based on
a pa ne ship be ween he p o ide and he cus ome (Meie e al. 2011b). The e
a e also app oaches o in eg a e he cus ome s al eady in he design and enginee -
ing p ocesses o PSS (Pezzo a e al. 2017). Since he p o ision o PSS is mo i-
a ed by di e en ia ing om he compe i o s and ex ending he ela ionship o he
cus ome o e yea s o decades (Li e al. 2020), he non- ul illmen o he alue
p oposi ion has a comple ely di e en signi icance. While o companies p o iding
solely se ices, ield se ice se es as a e enue sou ce, PSS p o ide s, especially
Fig. 3 Ope a i e Se ice Deli e y Planning and Scheduling (Do ka e al. 2014)
167
1 3
Ope a i e se ice deli e y planning andscheduling in…
in a ailabili y-o ien ed business models, conduc se ice deli e ies o ealize he
p omised alue p oposi ion and do no ge paid o he indi idual se ice p ocesses.
Whe eas a dy se ice deli e y can cause addi ional cos s due o penal ies, losing
he us o he cus ome s is a highe isk ha can dis u b he long- e m ela ionship
(Reim e al. 2016).
Besides he inc eased numbe and c i icali y o cons ain s, he e a e also pecu-
lia i ies o PSS business models ha can ha e a posi i e e ec on he ope a i e
se ice deli e y planning and scheduling. Due o he high cus ome p oximi y and
knowledge abou hei own p oduc s, unce ain ies ega ding he amoun and ime
o eques s can be educed (Wan e al. 2014). Wi h app op ia e knowledge manage-
men sys ems and aining, he e iciency in aul diagnosis and oubleshoo ing can
be inc eased, hus esul ing in highe p edic able se ice deli e y du a ions. Ano he
cha ac e is ic o PSS business models is ha no only cus ome s can eques deli -
e y p ocesses bu he PSS p o ide is also able o ini ia e deli e y p ocesses, ena-
bling o lexibly conduc p e en i e measu es (Meie e al. 2011b). Since he alue
p oposi ion in PSS is no speci ied on ce ain se ices o p oduc s bu ocuses on
ul illing cus ome needs h ough a a iable combina ion o p oduc s and se ices,
he p o ide ge s a highe lexibili y in decision-making while planning he se ice
deli e y. To le e age his lexibili y and op imize se ice deli e y plans, Meie e al.
(2011a) in oduced a se o a iance op ions, which a e summa ized in Table 1.
These a iance op ions enla ge he solu ion space o plans and p o ide oppo uni-
ies o gene a ing op imized plans.
In summa y, despi e he e a e g ea simila i ies and in e sec ions be ween ield
se ice planning and ope a i e se ice deli e y planning and scheduling in he con-
ex o PSS, he business model logic in PSS gi es addi ional cons ain s and lex-
ibili ies when making decisions. Ope a i e se ice deli e y in he con ex o PSS
can be unde s ood as a specialized o m o ield se ice planning whe e no only
economic logic in luences he decisions bu also social alues ega ding he ela ion-
ship wi h cus ome s and pa ne s.
Thus, ma ching he app op ia e esou ces wi h he igh deli e y p ocesses o
gene a e an ope a i e plan is a complex p ocess. The a iance op ion inhe en in PSS
Table 1 Va iance op ions in deli e y planning and scheduling
168
E.Alp e al.
1 3
makes his ask a la ge-scale op imiza ion p oblem (Meie e al. 2011b). To s uc u e
he decision-making p ocess, Sala e al. (2021a) in oduced he D3M amewo k o
imp o e he decision-making based on eal- ime and his o ical da a. Acco ding o
he D3M amewo k da a ha a ises om he se ices, esou ces, cus ome s, and
p oduc s can be as a basis o make deli e y decisions. Addi ionally, da a ha eme ge
om deli e ing he p ocess i sel can help o imp o e decision-making (Sala e al.
2021a). Because o he la ge solu ion space as well as he huge da a basis ha could
be conside ed, gene a ing op imal plans emains a huge challenge. Wi h he aim o
minimizing cos s and maximizing p o i s as well as cus ome sa is ac ion, PSS p o-
ide s a e in need o sui able decision-suppo sys ems o ope a i e se ice deli e y
(Sala e al. 2019).
2.3 Sol ing ope a i e planning andscheduling p oblems
The huge solu ion space makes he ope a i e planning and scheduling an NP-ha d
p oblem. This means ha using exac me hods o sol e he p oblem is only possi-
ble o small p oblem sizes. The e o e, heu is ic me hods a e ypically used o sol e
such p oblems (Vössing 2017). Heu is ics esemble “ ules o humb” o a pa icula
domain applica ion and can ind good (nea -op imal) solu ions wi hin a sho com-
pu a ional ime. While heu is ics do no gua an ee op imali y and may con e ge o
local op ima (Bu ke and Kendall 2014), hey align wi h he ac ha “Real-wo ld
scheduling o en does no equi e op imal solu ions, bu easonable good solu-
ions in easonable ime” (Vössing 2017). A u he de elopmen o heu is ics is
me aheu is ics which ope a e on a highe le el and can ind op imal solu ions
e en o la ge p oblem se s (Doke oglu e al. 2019), by employing sea ch s a e-
gies based on phenomena in he na u e, physical laws, o human beha io (Abua-
ligah e al. 2022). Typical me aheu is ics a e he Gene ic Algo i hm (Ga cía-Ma -
ínez e al. 2018), Simula ed Annealing (Aa s e al. 2014), o Tabu Sea ch (Laguna
2018). Recen ad ancemen s in he ield o heu is ics led o he de elopmen o
hype heu is ics. Hype heu is ics o e a highe -le el sea ch me hodology ha does
no ope a e on he p oblem domain i sel bu on heu is ics ha sol e he p oblem
which inc eases he gene ali y o he algo i hms (D ake e al. 2020).
When sol ing ope a i e planning and scheduling p oblems, wo ca ego ies
(online o o line) o p oblem se ings can be dis inguished. In o line p oblems,
all he ele an in o ma ion ega ding eques s, equi emen s, p ocess imes, e c. is
gi en be o e he planning and scheduling. Thus, he en i e plan can be gene a ed
a ime ze o. In con as , in an online se ing, no all in o ma ion is known in he
beginning bu becomes a ailable du ing he execu ion. The decision-make does no
know how many deli e y p ocesses will be eques ed and wha hei a ibu es will
be (Pinedo 2022).
In he pas , esea ch has ocused on sol ing ope a i e planning and scheduling
p oblems in gene al. This s udy aims o conduc a sys ema ic li e a u e e iew o
iden i y and analyze he exis ing app oaches o gene a ing op imized ope a i e se -
ice deli e y plans in he con ex o PSS. The objec i e is o gain insigh s in o he
me hodologies, main cha ac e is ics, and limi a ions o hese app oaches.
175
1 3
Ope a i e se ice deli e y planning andscheduling in…
is consis en h oughou he subsequen sec ions. No ably, he li e a u e exhib-
i s conside able he e ogenei y in e ms o e minology and in o ma ion a aila-
bili y. The au ho s do no cla i y all aspec s o hei app oach, lea ing oom o
assump ions.
The majo i y o publica ions desc ibe p oduc -o ien ed PSS business models,
whe ein he PSS p o ide is esponsible o scheduling a e -sales se ices such as
main enance o epai s. In (Pe akis e al. 2012), (An unes e al. 2018), (Yumbe e al.
2019), and (Sala e al. 2021b), he p o ide is obliga ed o deli e se ices wi hin
a speci ic poin in ime o ace penal ies, indica ing an a ailabili y-based business
model. Only in he wo k by Ding e al. (2017), a esul -o ien ed business model is
discussed. Fo he emaining publica ions, he a ailable in o ma ion is insu icien o
de ini i ely assign a speci ic business model ype.
Rega ding he classi ica ion o he ope a i e se ice deli e y planning and sched-
uling p oblem, signi ican a ia ions can be obse ed ac oss publica ions. Each
au ho ends o p esen hei own classi ica ion scheme o he unde lying p oblem,
esul ing in a lack o consensus o s anda dized ca ego iza ion.
The majo i y o he app oaches in he analyzed li e a u e can be ca ego ized as
o line planning app oaches, whe e schedules a e gene a ed in ad ance based on
a ailable in o ma ion. Only Pe akis e al. (2012), Cas ane e al. (2019), and Yumbe
e al. (2019) add ess he aspec o online planning, whe e plans a e gene a ed in
eal- ime when a new se ice eques is added.
The objec i es pu sued by he a ious app oaches a e la gely aligned and e ol e
a ound common hemes. The p ima y objec i es commonly obse ed in he ana-
lyzed publica ions a e minimizing cos s and maximizing he numbe o in- ime se -
ice deli e ies in o de o achie e high cus ome sa is ac ion and a oid penal ies.
Addi ionally, some publica ions conside wo ke u iliza ion as an addi ional objec-
i e o be balanced.
Table 3 Main cha ac e is ics o he app oaches
Ma u i y
S age
Objec i es
Online s.
O line
P oblem Classi ica ionPSS-Type
2MAX (Punc uali y), MIN (Cos s), E en (Wo kload)O lineMul idimensional and mul iobjec i e op imiza ion p oblem
-Meie e Funke (2010)
5MAX (Punc uali y), MIN (Cos s), E en (Wo kload)O line
T a eling Salesman P oblem wi h Time Windows (TSPTW)
as basis
-Do ka e al. (2015)
4
MIN (T anspo a ion Cos s, Deadline Penal ies,
O e ime Cos s)
Online & O lineField Se ice Scheduling wi h P io i ies (FSSP)
A ailabili y -basedPe akis e al. (2012)
3
MIN (Mileage Cos s, Deploy Cos s, In alid ime Cos s,
Sa is ac ion loss Cos s)
O lineMul iple T a eling Salesman P oblem
P oduc -o ien edZhao e al. (2014)
3
MIN (To al Cos o Se ice --> Mileage Cos s, Time Cha ge,
Down ime Loss, Wai ing Cos s)
O lineTechnician Scheduling P oblem
P oduc -o ien edLi e al. (2015)
4MIN (To al Cos o Main enance Se ice Deli e y)O lineMain enance ield se ice deli e y p oblem
P oduc -o ien edZhou e al. (2016)
4
MIN (Time, Cos s)O lineP oduc ion and Ins alla ion Planning
P oduc -o ien edAlexopoulos e al. (2017)
4
MAX (Cus ome Sa is ac ion Deg ee, Resou ce U ili y
E iciency), MIN (P oduc -Se ice Cos s)
O line
En i onmen al and Economic sus ainabili y-awa e esou ce
se ice scheduling p oblem (RSSP)
Resul -o ien edDing e al. (2017)
3MAX (Re enue)O lineIn eg a ed O de Accep ance and Scheduling (OAS)
P oduc -o ien edDan e al. (2018)
3MIN (S o age Cos , Ta dinessCos s)O line
PSS o de scheduling P oblem wi h Time Windows
(PSS-OSPTW)
P oduc -o ien edZhang e al. (2019)
4MIN (Ea liness, La eness, Penal y Cos s)O lineMobile Wo k o ce Scheduling P oblem
A ailabili y -basedAn unes e al. (2018)
4
MIN (T a el ime, Idle Time Cos s), MAX (Pe cen age o on -
ime Task Comple ion by Task P io i y)
OnlineField Se ice P oblem
P oduc -o ien edCas ane e al. (2019)
5
MIN(To al Labo Cos s) by equalizing he wo k amoun o
each da e and wo ke
OnlineField Se ice Technicians Scheduling P oblem
A ailabili y -basedYumbe e al. (2019)
3MIN (To al Ta diness)O linePa allel Machine Scheduling P oblem
-Sala e al. (2020)
4MIN (Numbe o Ta dy In e en ions)O line-
A ailabili y -basedSala e al. (2021)
1MAX (Punc uali y), MIN (Cos s), E en (Wo kload)O lineVehicle Rou ing P oblem wi h Time Windows (VRPTW)
-Alp e al. (2022)
3MIN (Cos s, Ene gy, Risk Le el, o Time)O lineModi ied T a el Salespe son P oblem
A ailabili y -basedYi e al. (2023)
Li e a u e
PSS
FSM
Li e a u e
176
E.Alp e al.
1 3
To assess he le el o comp ehensi eness o he app oaches p esen ed in each
publica ion, a 5-s age ma u i y model was de eloped ollowing Poeppelbuss and
Roeglinge (2011). The model, isualized in Fig. 10, ca ego izes he app oaches
based on hei le el o de elopmen .
S age1 ep esen s app oaches ha in oduce desc ip i e and/o isual concep s
o an ope a i e planning and scheduling app oach. In S age 2, he ma hema ical
o mula ions o objec i e unc ions and u he o malized cons ain s ega ding
he p oblem a e p o ided. S age 3 encompasses app oaches ha we e es ed and
e i ied using syn he ic da a in concep ual scena ios. Typically, publica ions in his
s age ocus on showing he gene al applicabili y o hei de eloped algo i hms and
me hodologies in he con ex o PSS ope a i e planning and scheduling. S age4 p e-
sen s app oaches ha unde wen alida ion in eal-wo ld use cases using company
da a and hus p o e hei sui abili y o eali y. S age5 desc ibes app oaches ha a e
applied in eal-wo ld use cases and whose pe o mances a e compa ed o he exis -
ing me hods and app oaches in he espec i e use cases, e.g., manual planne s. The
analysis e eals ha he majo i y o he publica ions p esen a leas a e i ica ion o
hei app oaches wi hin concep ual scena ios. Fu he mo e, nine o he 17 publica-
ions e alua ed hei app oaches using eal-wo ld da a wi hin speci ic use cases. In
wo o hese publica ions, Do ka e al. (2015) and Yumbe e al. (2019) compa ed he
pe o mance o hei app oaches o exis ing me hods used in he espec i e com-
panies. In bo h cases, he p oposed app oaches ou pe o med he exis ing me hods,
highligh ing hei e ec i eness in p ac ical se ings.
4.2.2 Conside ed da a
As discussed in Sec .2.2.3, ope a i e se ice deli e y planning and scheduling can
d aw upon da a om ou sou ces (Sala e al. 2021a). Table4 p o ides an o e -
iew o he publica ions and hei conside a ion o da a du ing he planning p o-
cess. All publica ions conside da a and in o ma ion abou he deli e y p ocesses
Compa ed o
S age
5
E alua edApp oach
Applied on eal -wo ld use case
Valida ed App oach
S age
4
Applied on concep ual scena ios
Ve i ied App oach
S age
3
Fo malized App oach
S age
2
Concep ual App oach
S age
1
Fig. 10 Ma u i y model o ope a i e planning and scheduling app oaches
177
1 3
Ope a i e se ice deli e y planning andscheduling in…
and esou ces. A de ailed analysis o he conside ed a ibu es o deli e y p ocesses
and esou ces can be ound in he subsequen sec ion. Only wo ou o he 17 pub-
lica ions conside da a om he ins alled machines. Li e al. (2015) and Sala e al.
(2021b) u ilize machine condi ion da a o de e mine he emaining li espan be o e
po en ial b eakdowns, which suppo s scheduling he p ocesses imely. I is no ewo -
hy ha none o he app oaches inco po a e addi ional in o ma ion abou he cus-
ome , such as hei his o y o hei signi icance o he PSS p o ide . Simila ly, da a
ha could be collec ed om he se ice execu ion p ocesses, such as eedback o
pe o mance me ics, a e no u ilized in any o he analyzed app oaches.
4.2.3 Deli e y p ocess and esou ce a ibu es
As men ioned ea lie in he ounda ional sec ion, he main logic behind he
app oaches o ope a i e se ice deli e y planning and scheduling is o ma ch deli -
e y p ocesses wi h app op ia e esou ces. In he analyzed publica ions, he e m
“deli e y p ocess” is e e ed o as e.g., Cus ome s’ equi emen in (Ding e al.
2017), o de s in (Dan e al. 2018), and asks in (Yumbe e al. 2019). While he
majo i y o publica ions p ima ily ocus on planning main enance- ela ed deli e y
Table 4 Conside ed da a
Da a om o abou
Cus ome
Machine/
Equipmen
Resou ce
Deli e y
P ocess
○○●●
Meie e Funke (2010)
○○●●
Do ka e al. (2015)
○○●●
Pe akis e al. (2012)
○○●●
Zhao e al. (2014)
○●●●
Li e al. (2015)
○○●●
Zhou e al. (2016)
○○●●
Alexopoulos e al. (2017)
○○●●
Ding e al. (2017)
○○●●
Dan e al. (2018)
○○●●
Zhang e al. (2019)
○○●●
An unes e al. (2018)
○○●●
Cas ane e al. (2019)
○○●●
Yumbee al. (2019)
○○●●
Sala e al. (2020)
○●●●
Sala e al. (2021)
○○●●
Alp e al.(2022)
○○●●
Yi e al. (2023)
●
= inco po a ed
○
= no inco po a ed
PSS Li e a u e
FSM Li e a u e
178
E.Alp e al.
1 3
p ocesses, he e a e a ew excep ions. Fo ins ance, Alexopoulos e al. (2017), Dan
e al. (2018), and Zhang e al. (2019) speci ically add ess he scheduling o ins al-
la ion se ices o echnical p oduc s wi hin a PSS, including he coo dina ion o
hei p eceding p oduc ion. Th oughou hese di e se app oaches, he key esou ce
consis en ly conside ed is he human elemen esponsible o execu ing he deli e y
p ocesses, e e ed o as e.g., echnicians in Meie and Funke (2010), enginee s (Pe -
akis e al. 2012), o ope a o s in Sala e al. (2020).
An o e iew o he conside ed a ibu es o deli e y p ocesses and esou ces
ac oss he in es iga ed publica ions is ound in Table5. Analyzing he a ibu es o
he deli e y p ocesses, i becomes e iden ha in nea ly e e y app oach, he loca ion
o a deli e y p ocess is used o calcula e a eling ime and cos s. A special case is
p esen ed in he app oach o Li e al. (2015), whe e i is men ioned ha loca ions
a e changing dynamically. No ably, Ding e al. (2017), Dan e al. (2018), Zhang
e al. (2019), and Yi e al. (2023) do no inco po a e loca ion-based in o ma ion in
Table 5 Conside ed a ibu es o deli e y p ocesses and esou ces
179
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Ope a i e se ice deli e y planning andscheduling in…
hei espec i e app oaches. Time windows, speci ying allowable deli e y imes, a e
included in he majo i y o publica ions, whe eby h ee publica ions solely ocus on
deadlines, e.g., esul ing om he emaining li e ime o he equipmen like in (Li
e al. 2015), (Yumbe e al. 2019), o (Sala e al. 2020). In mos cases, penal ies a e
associa ed wi h delayed deli e y o he p ocesses. Skill equi emen s o e ec i e
p ocess deli e y a e aken in o accoun in en ou o he 17 publica ions and p o-
cess du a ion is ypically ep esen ed as a ixed alue o calcula ed de e minis ically.
Only Pe akis e al. (2012) and Cas ane e al. (2019) inco po a e s ochas ic imes,
conside ing he inhe en unce ain ies in se ice deli e y du a ions. Addi ionally,
Pe akis e al. (2012), Zhou e al. (2016), and Cas ane e al. (2019) in oduce p io i-
ies o deli e y p ocesses. In gene al, in he in es iga ed app oaches, only one ech-
nician is equi ed o deli e he p ocesses. Howe e , Meie and Funke (2010), Do ka
e al. (2015), and Ding e al. (2017) in oduce he possibili y o ha ing he equi e-
men o mul iple echnicians ope a ing as a eam o deli e ce ain se ice p ocesses.
In e ms o he a ibu es ela ed o esou ces, he e a e bigge di e ences be ween
he app oaches. The wo king imes o echnicians a e aken in o accoun in se en ou
o he 17 app oaches, es ic ing hei a ailabili y o alloca ion. Six app oaches con-
side he op ion o echnicians wo king o e ime o mee inc eased se ice demands.
Skills and quali ica ions o echnicians a e conside ed in en app oaches, while wo
o hem in oduce he aspec o amilia i y. He e, amilia i y e e s o he expe i-
ences and nea ness o echnicians o indi idual cus ome s and si es. Meie and
Funke (2010) men ion he amilia i y o he echnicians wi h he eques ed p ocess
as a du a ion-e ec ing ac o , while An unes e al. (2018) assume ha amilia i y
enhances deli e y quali y. The au ho s use he aspec o amilia i y as a decision
c i e ion o echnician alloca ion and aim o always send he same echnician o
he cus ome s. The echnical, allocable esou ces ecei e less conside a ion. Spa e
pa s a e aken in o accoun in ou app oaches, ools a e inco po a ed o men ioned
in wo. In cases whe e in e nal esou ces a e insu icien , Meie and Funke (2010)
and Alexopoulos e al. (2017) add ess he op ion o con ac ing addi ional se ice o
esou ce supplie s in o de o mee se ice demands e ec i ely.
I is no ewo hy ha h oughou he analyzed publica ions, he p oblem desc ip-
ions o en include de ailed discussions o mul iple a ibu es ela ed o deli e y p o-
cesses and esou ces. Howe e , when i comes o p esen ing he ac ual app oach,
au ho s end o make simpli ica ions and do no inco po a e all o he men ioned
a ibu es. To gi e an example, Do ka e al. (2015) explain he ele ance o ech-
nician p e e ences ega ding a el imes and du a ions. A echnician could p e e
o come home daily o o s ay nea he cus ome o e he weekend. The inco po-
a ion o his a ibu e, howe e , is nei he shown no men ioned in he app oach
desc ip ion. This could be due o he inhe en challenges associa ed wi h de eloping
comp ehensi e solu ions ha encompass all impo an aspec s. As wi h all models,
he challenge lies in s iking he balance be ween he complexi y and easibili y o
implemen ing p ac ical solu ions. These simpli ica ions, while necessa y, also open
up oppo uni ies o u he esea ch and he de elopmen o mo e sophis ica ed
app oaches ha can e ec i ely add ess he complexi ies and nuances o ope a i e
se ice deli e y planning and scheduling. In Sec .4.2.7, he limi a ions and u he
esea ch oppo uni ies s a ed by he au ho s a e explained.
180
E.Alp e al.
1 3
4.2.4 Sol ing me hodologies
To sol e he op imiza ion p oblem o ma ching deli e y p ocesses wi h app op ia e
esou ces, a ious me hods a e employed ac oss he analyzed app oaches. Table6
p o ides an o e iew o he ma hema ical o mula ions, sol ing me hods, and so -
wa e u ilized in each app oach. The majo i y o publica ions include a ma hema ical
objec i e unc ion o e alua e plans, wi h he excep ion o Do ka e al. (2015) and
Alexopoulos e al. (2017) who men ion i s use wi hou explici ly p o iding a unc-
ion. Besides, Cas ane e al. (2019) u ilize a simula ion model o plan e alua ion.
Alp e al. (2022) do no in oduce an objec i e unc ion in hei app oach, as hey a e
in he ea ly s ages o de elopmen . Fu he mo e, mos app oaches wi h an objec i e
unc ion also include ma hema ically o malized cons ain s ha ensu e he easibil-
i y o he gene a ed plans.
The sol ing me hod in each app oach esembles he co es o ope a i e se ice
deli e y planning and scheduling. Analyzing he 17 publica ions, i becomes e iden
ha he majo i y o he app oaches employ (me a-) heu is ic op imiza ion me hods
o gene a e and op imize plans. Modi ied e sions o he Gene ic Algo i hm and
Table 6 Sol ing me hods
181
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Ope a i e se ice deli e y planning andscheduling in…
Simula ed Annealing a e pa icula ly common. Meie and Funke (2010), o exam-
ple, use a sequen ial combina ion o E olu iona y Algo i hms, Simula ed Anneal-
ing, and B u e Fo ce Sea ch me hods o gene a e op imized schedules by le e aging
he a iance op ions (see Sec .2.2.3) in se ice deli e y. Pe akis e al. (2012) and
Zhang e al. (2019) compa e he esul s and pe o mance o di e en (me a-)heu is-
ics. Some au ho s in oduce hei own heu is ics speci ic o hei app oach, such as
Dan e al. (2018) o Yumbe e al. (2019). Besides he heu is ic app oaches, ma he-
ma ical p og amming (Sala e al. 2021b) and quan um annealing (Yi e al. 2023) a e
also used. Al hough ew au ho s indica e he so wa e used in hei app oach, he e
is e idence o inc eased use o he Cplex so wa e in he analyzed li e a u e.
4.2.5 Func ionali ies
Subsequen o he analysis o solu ion me hods, he indi idual mechanisms inco po-
a ed in each app oach a e examined in his subsec ion, as summa ized in Table7.
While mos app oaches assume ha echnicians do no ha e p eassigned asks o
appoin men s, Alexopoulos e al. (2017), An unes e al. (2018), and Sala e al.
(2021b) de ia e om his by planning wi h echnicians who al eady ha e a pa ly
Table 7 Func ionali ies
182
E.Alp e al.
1 3
illed schedule wi h appoin men s, such as deli e y p ocesses, aining, o holidays.
In ou o he app oaches, p eassigned deli e y p ocesses a e pa ly conside ed, as
hese app oaches conduc escheduling when unexpec ed asks occu o assigned
asks canno be execu ed as planned. Some app oaches make skill equi emen
ma ching a p e equisi e o alloca ion. In (Pe akis e al. 2012; Do ka e al. 2015;
Sala e al. 2021b), echnicians mus ha e he necessa y skills in o de o be assigned
o he espec i e deli e y p ocess. In o he app oaches, such as in (Cas ane e al.
2019; Li e al. 2015; Sala e al. 2020), all echnicians ha e he skills o deli e all
asks; howe e , he du a ion o he deli e y p ocess is adjus ed based on hei skills,
and quali ica ions.
Rega ding deli e y p ocess du a ions, Do ka e al. (2015) s and ou since hei
app oach inco po a es longe -du a ion deli e y p ocesses, e.g., 16h, by dis ibu ing
he p ocess ac oss consecu i e days. In e ms o decision-making lexibili y, h ee
app oaches s and ou . Meie and Funke (2010), Sala e al. (2021b), and Yi e al.
(2023) p o ide he PSS p o ide wi h he abili y o choose al e na i e deli e y p o-
cesses. Fo ins ance, Meie and Funke (2010) explo e he e ec s o eplacemen s
ins ead o epai s on he o e all plan, while Sala e al. (2021b) conside op ions such
as emo e suppo o sending spa e pa s ins ead o deploying a echnician. Ano he
pa icula i y o he app oach p esen ed by Meie and Funke (2010) is he inclusion
o planning wi h di e en ehicles, such as ca s o ains, which allows o po en ial
cos educ ion o ime-sa ing measu es in se ice deli e y.
Table 8 Applica ion scena ios
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Ope a i e se ice deli e y planning andscheduling in…
4.2.6 Applica ion scena ios
This subsec ion ocuses on he e i ica ions, alida ions, and e alua ions conduc ed
in he analyzed publica ions, as summa ized in Table8. The able gi es an o e -
iew o he main cha ac e is ics ela ed o he applica ion scena ios. Among he 17
app oaches, 15 p esen hei applica ion scena ios, wi h en u ilizing concep ual use
cases and eigh employing eal-wo ld use cases o alida ion. No ably, Do ka e al.
(2015) and Yumbe e al. (2019) conduc ed compa isons wi h exis ing me hods in
hei espec i e companies. Wi h hei app oach, Do ka e al. (2015) gene a ed a
plan, ha demons a ed app oxima ely 35% less echnician u iliza ion and 30% less
a el imes compa ed o he plan gene a ed by he ope a i e planne o he com-
pany. Howe e , i is wo h no ing ha hese esul s we e ob ained a e nea ly wo
days o compu ing ime. Simila ly, Yumbe e al. (2019) compa ed hei app oach o
con en ional planning me hods used in a Japanese IT company. They we e able o
educe he numbe o equi ed echnicians by 25% and a el dis ance by app oxi-
ma ely 22% when gene a ing an ini ial plan. Fu he mo e, wi h dynamic eschedul-
ing, hei app oach achie ed a educ ion o app oxima ely 13% in equi ed echni-
cians and app oxima ely 21% in a el dis ance, while also signi ican ly educing he
numbe o a dy asks. No ably, hei app oach deli e ed he bes esul s in less han
10s.
The analyzed app oaches we e p ima ily applied in he ield o he mechanical
enginee ing indus y. The use cases a ied signi ican ly in scale and complexi y.
Fo example, Sala e al. (2021b) conside ed scena ios wi h a ange o se en, en, o
wel e deli e y p ocesses o be scheduled wi h i e echnicians in a single depo se -
ing. On he o he hand, Pe akis e al. (2012) employed a la ge use case in ol ing
177 echnicians, wi h each echnician ha ing ou o i e deli e y p ocesses assigned
o hem. This use case was designed based on eal-wo ld da a and implemen ed in
a mul i-depo scena io. Yi e al. (2023) es hei app oach on la ge-scale p oblems
wi h 1281 al e na i e se ice p ocesses. I is wo h no ing ha he majo i y o he
app oaches u ilized single depo se ing as he s anda d scena io, while only a ew
explo ed mul i-depo scena ios.
5 Discussion
The li e a u e sea ch conduc ed in his s udy yielded a o al o 17 publica ions el-
e an o he opic o ope a i e se ice deli e y planning and scheduling in he con-
ex o PSS. Th ough analysis o hese publica ions, se e al key indings eme ged.
Fi s ly, a limi ed co ela ion was obse ed ac oss di e en au ho g oups. Se e al
au ho g oups published hei app oaches in se e al s ages and p esen ed adjus -
men s, modi ica ions, o e alua ions o he con en s in he p e ious publica ion.
Thus, ele en dis inguishable clus e s ac oss he 17 publica ions could be iden i ied.
Mos o he app oaches we e s ill in he ea ly s ages o de elopmen , lacking eal-
wo ld es ing and compa ison wi h exis ing me hods employed by companies. Fu -
he mo e, he majo i y o he app oaches we e o line in na u e, wo king wi h s a ic
da a despi e he dynamic na u e o se ice deli e y. The e is a lack o a decen alized
184
E.Alp e al.
1 3
iew o he p oblem as p oposed by A aham e al. (2017). Da a u iliza ion in he
app oaches also showed oom o imp o emen . Machine da a, despi e i s po en ial
in p ognos ics and condi ion moni o ing (Teixei a e al. 2013), was a ely conside ed
bu could help schedule necessa y se ices mo e e icien ly. Cus ome o p ocess
execu ion da a, which could p o ide aluable insigh s o decision-making, we e
no inco po a ed in o any o he app oaches. None o he app oaches encompassed
all analyzed a ibu es, leading o an o e ly simpli ied conside a ion o he p ob-
lem. Addi ionally, mos app oaches assumed de e minis ic a el and se ice du a-
ions, which may no e lec he unce ain ies p esen in p ac ices. In gene al, se ice
deli e y is seen as a cen alized p oblem, in which he OEM o e sees he execu ion
o all se ice p ocesses. Rega ding sol ing me hodologies, heu is ic and me aheu is-
ic algo i hms we e commonly employed. Howe e , he lack o benchma k ins ances
p e en ed meaning ul pe o mance compa isons among hese algo i hms. Al hough
some app oaches we e applied o di e en use cases and demons a ed supe io pe -
o mance compa ed o exis ing me hods, none o he au ho s conduc ed compa i-
sons be ween he gene a ed plans and hei ac ual execu ion, o calcula ed cos s and
ac ual expenses. This highligh s a need o u he e alua ion and alida ion o he
app oaches in eal-wo ld se ings.
6 Conclusion and esea ch agenda
Ope a i e se ice deli e y planning and scheduling in he con ex o PSS is no
me ely an academic p oblem. I holds p ac ical signi icance o business, as e ec i e
se ice deli e y is essen ial o cus ome sa is ac ion and ope a ional success. To
add ess he challenges and complexi ies inhe en in se ice deli e y, i is impe a i e
o de elop sui able app oaches. Thus, a comp ehensi e esea ch agenda has been
syn hesized o guide u u e endea o s. Resea ch in he ollowing se en a eas could
lead o he de elopmen o imp o ed decision-suppo sys ems wi hin he con ex
o PSS, he eby acili a ing op imized se ice deli e y planning and scheduling and
ul ima ely enhancing he o e all pe o mance and success o PSS business models.
6.1 Realis ic a ibu es andcons ain s
To gain a mo e comp ehensi e and ealis ic unde s anding o he ac o s in luenc-
ing decision-making in ope a i e se iced planning and scheduling, u he esea ch
could in ol e conduc ing ield s udies and engaging wi h indus y p ac i ione s.
Case s udies and close collabo a ion wi h companies can p o ide aluable insigh s
in o he a ibu es ha a e conside ed in eal-wo ld scena ios. Addi ionally, aking a
human-cen ic app oach, i would be bene icial o in e iew besides he dispa che
also echnicians o ga he hei pe spec i es and inpu , which can con ibu e o he
de elopmen o mo e e ec i e and p ac ical app oaches. By inco po a ing hese
ealis ic a ibu es and cons ain s, u u e esea ch can enhance he applica ion and
accu acy o ope a i e se ice deli e y planning and scheduling me hods.
191
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Publishe ’s No e Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in published maps
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