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GRAĐEVINAR 70 (2018) 7, 593-606
DOI: h ps://doi.o g/10.14256/JCE.1719.2016
Cons uc ion p ocess op imisa ion –
e iew o me hods, ools and applica ions
P imljen / Recei ed: 7.7.2016.
Isp a ljen / Co ec ed: 24.11.2017.
P ih aćen / Accep ed: 12.4.2018.
Dos upno online / A ailable online: 10.8.2018.
Au ho s:
Václa Venk bec, MSc. CE
B no Uni e si y o Technology
Facul y o Ci il Enginee ing
Ins i u e o Technology, Mechaniza ion and
Cons uc ion Managemen
Czech Republic
[email p o ec ed]
Assis .P o . Ma io Galić, PhD. CE
Josip Ju aj S ossmaye Uni e si y o Osijek
Facul y o Ci il Enginee ing Osijek
Depa men o O ganiza ion, Technology and
Managemen
[email p o ec ed]
Assoc. P o .U oš Klanšek, PhD. CE
Uni e si y o Ma ibo , Slo enia
Facul y o Ci il Enginee ing, T anspo a ion
Enginee ing and A chi ec u e
Chai o Cons uc ion Managemen ,
Technology and Economics
[email p o ec ed]
Subjec e iew
Václa Venk bec, Ma io Galić, U oš Klanšek
Cons uc ion p ocess op imisa ion – e iew o me hods, ools and applica ions
A e iew o cu en heu is ic echniques and ma hema ical p og amming me hods is
p esen ed in he pape . Mode n op imisa ion modelling ools a e p esen ed, common
cons uc ion op imisa ion p oblems a e desc ibed, and an o e iew o hei ecen
applica ion is gi en. I is also s essed ha he e is ample oom o u he esea ch on
ac i e BIM-based op imisa ion models, aimed a be e managemen o cons uc ion
p ocesses. Accele a ed de elopmen o decision-making models, which combine
op imisa ion me hods and in o ma ion sys ems, can be clea ly p edic ed o he nea
u u e.
Key wo ds:
BIM, cons uc ion p oduc ion, modelling, op imisa ion me hods, p ocess op imisa ion
P egledni ad
Václa Venk bec, Ma io Galić, U oš Klanšek
Op imizacija g ađe inskih p ocesa – me ode, ala i i p imjena
U adu je dan p egled su emenih heu is ičkih ehnika i me oda ma ema ičkog
p og ami anja. P eds a ljeni su mode ni ala i za op imizacijsko modeli anje, izlažu
se najčešći p oblemi op imizacije u g ađe ina s u i daje p egled njiho ih neda nih
aplikacija. Rad je is aknuo p os o is aži anja na ak i nim modelima op imizacije, koji
su pod žani BIM-om s namje om boljeg up a ljanja g ađe inskim p ocesima. Ub zani
az oj modela odluči anja, koji kombini aju op imizacijske me ode i in o macijske
sus a e, jasno se može p ed idje i za blisku budućnos .
Ključne iječi:
BIM, g ađe inska p oiz odnja, modeli anje, op imizacijske me ode, op imizacija p ocesa
Übe sich sa bei
Václa Venk bec, Ma io Galić, U oš Klanšek
Op imie ung on Baup ozessen – Me hoden, We kzeuge und Anwendung
In de Abhandlung wi d eine Übe sich übe die mode nen heu is ischen Techniken
und Me hoden de ma hema ischen P og ammie ung da geleg . Vo ges ell
we den mode ne We kzeuge ü die op imie e Modellie ung, es we den die
häu igs en P obleme de Op imie ung im Bauwesen da geleg und es wi d eine
Übe sich ih e ak uellen Anwendungen gegeben. Die Abhandlung be on den
Un e suchungsbe eich ak i e Modelle de Op imie ung, die du ch BIM un e s ü z
we den, mi dem Ziel eines besse en Baup ozessmanagemen s. Eine beschleunig e
En wicklung de En scheidungsmodelle, welche die Op imie ungsme hode und die
In o ma ionssys eme kombinie en, kann ü die nahe Zukun kla o ausgesehen
we den.
Schlüsselwö e :
BIM, Baup oduk ion, Modellie ung, Op imie ungsme hode, Op imie ungsp ozess
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1. In oduc ion
Op imisa ion has been p esen o qui e a long ime in indus ial
p oduc ion. Howe e , i can be no ed ha op imisa ion has
once again become he subjec o highly in ensi e wo ldwide
esea ch, ollowing a huge heo e ical g ow h o his ield in
he pe iod be ween 1950s and 1970s. Reasons why his ield
o science is s ill so p opulsi e can be ound in an inc eased
common awa eness abou limi ed a ailabili y o esou ces, wha
happens o be he main igge ha pushed he op imisa ion
o wa d in he lis o public’s c i e ia. Addi ional mo i a o s a e
wide a ailabili y o compu a ional packages and s eadily g owing
ha dwa e capaci ies. Hence, he almos o go en ma hema ical
models a e now being e ised, expanded, modi ied and e i ied
h ough hei use in sol ing ac ual op imisa ion p oblems in
indus ial p oduc ion.
The quan i a i e suppo o decision making p ocesses in
indus y is especially highligh ed as being o c ucial signi icance
in mode n managemen ends. Howe e , he speci ici y o
managing cons uc ion p ojec s is hei s ochas ic and dynamic
en i onmen , despi e common epe i i e ope a ions, in which
op imisa ion cons i u es one o he phases. A his poin , he
low o sui able in o ma ion is highly impo an o pe o ming
op imisa ion in cons uc ion p ocesses and, he e o e, i is
necessa y o iden i y which o he a ailable solu ion echniques
is he mos app op ia e o a pa icula p oblem.
Al hough cons uc ion indus y has always been p ojec -o ien ed
[1], as well as a ec ed by ine iciency and ine ec i eness [2],
limi ed a en ion has been paid in p ac ice o ma hema ically-
based app oaches o p ocesses op imisa ion. The e o e, be e
awa eness abou con empo a y echniques o p ocesses
op imisa ion can be gene ally bene icial o all decision-make s
in cons uc ion indus y, especially o p ojec manage s and
expe s o p oduc ion p epa a ion.
I is wo h men ioning ha imp o emen o business
p ocesses can also be achie ed in indus y h ough en e p ise
esou ce planning (ERP) sys ems. Va ious ERP sys ems o
manu ac u ing and ading companies a e known and some o
hem ha e been suppo ed by a ionalized wo k lows [3] and
schedules [4]. Subjec e iews ha e also been p esen ed in
li e a u e [5] and some au ho s, e.g. [6], in oduced a sys ema ic
app oach o ERP implemen a ion in cons uc ion sec o . I has
o en been no ed ha ERP sys ems can be good ehicles o
business p ocess eenginee ing and in oduc ion o IT sys ems
[7]. Ne e heless, his a icle does no a emp o p o ide a
e iew o in eg a ion and in e ope abili y be ween di e en
in o ma ion sys ems, e.g. ERP and building in o ma ion
modelling (BIM), since li e a u e on ha subjec can be ound in
ele an da abases, and some ecen con ibu ions ha e e en
indica ed ha he e s ill exis s a as sca ci y o knowledge in
he men ioned ield, see e e ence Howe e , BIM is s eadily
being in oduced in cons uc ion indus y, and i is necessa y o
e iew cu en s a e in he sphe e o p ocess op imisa ion, since
bo h concep s can be ope a i ely connec ed in o de o imp o e
business pe o mance. To he bes o ou knowledge, such a
e iew has no as ye been made. Au ho s [8] s a ed ha BIM
has e ol ed and ha The cons uc ion communi y is seeing a shi
om he 3D o isualiza ion aspec o BIM o wo k low-speci ic ools
ha a e being di ec ly applied o sol e eal-wo ld p oblems, such as
ins alla ion e i ica ion, sequencing, and es ima ing. The indus y
dialogue is now mo ing o a gene al ques ioning o how we op imize
he e ec i e cap u e, analysis, and dissemina ion o in o ma ion in
eal ime o make p ojec s mo e success ul. The e o e, his pape
gi es an o e iew o ecen achie emen s in he men ioned
ields, and p o ides some indings ha a e in ended o ill gaps
in li e a u e and open possibili ies o u he esea ch.
Conside ing he g ea a ie y o p oblems encoun e ed, and
in he ligh o p e ious li e a u e e iews conce ning speci ic
solu ion me hods and modelling ools, he au ho s p esen a
e iew o hose conce ning he op imisa ion o cons uc ion
p oduc ion in o de o p o ide he expe and academic
communi ies wi h cu en in o ma ion ela ing o hese a eas.
2. Op imisa ion me hods
Op imisa ion me hods can be used o sol ing a wide ange
o di e en enginee ing p oblems. E en hough op imisa ion
p oblems may come om a ious ields and di e en sys ems,
hey can basically be o mula ed in a su p isingly simila way.
Gene ally, an op imisa ion p oblem (OP) can be exp essed in he
ollowing way:
minimize (x),
subjec o: h(x) = 0 and g(x) ≤ 0
whe e (x) is he objec i e unc ion o be minimized o e he
ec o o decision a iables x, while h(x) = 0 ep esen s equali y
cons ain s and g(x) ≤ 0 deno es inequali y cons ain s.
The objec i e unc ion de ines he c i e ion o selec ing an
op imum solu ion, while he cons ain s de e mine bounda ies
delimi ing he space o all easible solu ions. I should be no ed
he e ha he objec i e unc ion may also be maximized o e
he easible space i necessa y. As o decision a iables, hey
a e usually calcula ed be ween hei lowe and uppe bounds,
xLO ≤ x ≤ xUP, and hey can be con inuous, x R, whe e R is he se
o eal numbe s; o in ege , x Z, whe e Z is he se o in ege s.
In ege a iables can also appea as bina y decision a iables,
i.e. x {0,1}m.
An app op ia e me hod o sol ing a speci ic op imisa ion
p oblem should be selec ed a en i ely in o de o ob ain
aluable esul s. Be o e selec ing a me hod o solu ion, he
op imisa ion p oblem should be analysed om he s andpoin
o i s unc ions, cons ain s, and decision a iables. Solu ion
app oaches o single-c i e ia op imisa ion p oblems can be
oughly di ided in o wo main g oups, i.e. heu is ic me hods and
ma hema ical p og amming me hods.
Heu is ic echniques can be used o sol e a wide a ie y o
op imisa ion asks and hei main ad an age is ha mos o
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Cons uc ion p ocess op imisa ion – e iew o me hods, ools and applica ions
hese echniques con e ge easonably as , and can handle
p oblems ha con ain non-di e en iable unc ions. Howe e ,
a he end o he sea ch, heu is ic algo i hms o en o e only
app oxima ely op imal, i.e. sub-op imal solu ions. Ne e heless,
heu is ic me hods ha e been p o en sui able o sol ing a
a ie y o op imisa ion p oblems in ci il enginee ing, and he
mos equen ly used ones a e: di ec sea ch (DS) [9], e olu ion
s a egies (ES) [10, 11] and gene ic algo i hms (GA) [12,13],
abu sea ch (TS) [14], simula ed annealing (SA) [15], neu al
ne wo ks (NN) [16], an colony op imisa ion (ACO) [17], pa icle
swa m op imisa ion (PSO) [18], di e en ial e olu ion (DE) [19],
and ha mony sea ch (HS) [20]. As ex ensions o main heu is ic
me hods, he me a- and hype -heu is ic s ochas ic echniques
a e mos ly inclined o bio-inspi ed compu ing algo i hms like
bac e ial o aging op imisa ion (BFO) [21], cuckoo sea ch (CS)
[22], a i icial bee colony (ABC) [23], i e ly algo i hm (FA) [24],
ba algo i hm (BA) [25], lowe pollina ion algo i hm (FPA) [26],
a i icial plan op imisa ion (APO) [27], wol sea ch algo i hm
(WSA) [28], e c. I should be no ed he e ha a ious hyb id
combina ions o he a o esaid echniques ha e also been
epo ed in li e a u e.
Ma hema ical p og amming me hods ha e also been widely
ecognized as ad an ageous ools o op imisa ion in ci il
enginee ing (examples suppo ed wi h e e ences will be
gi en in sec ions 4 and 5). The majo bene i o ma hema ical
p og amming me hods is ha hey p o ide an expec ed exac
op imum esul , al hough he sea ch p ocess i sel can, in some
cases, equi e longe amoun o ime. As is commonly known,
slowe con e gence o esul s is ypical in he case o op imisa ion
o highly combina o ial, disc e e, nonlinea and, pa icula ly,
noncon ex p oblems. In gene al, he ield o ma hema ical
p og amming co e s linea p og amming (LP), nonlinea
p og amming (NLP), mixed-in ege linea p og amming (MILP)
and mixed-in ege nonlinea p og amming (MINLP) me hods.
LP is a well de eloped a ea o ma hema ical p og amming.
Solu ion echniques o LP can gene ally be applied o a ious
op imisa ion p oblems, which include linea objec i e unc ions
and cons ain s wi h con inuous a iables. Well known simplex
algo i hm [29] is p esumably he mos commonly used me hod
o sol ing LP p oblems. Howe e , i should be emphasized ha
he in e io poin me hod [30] is o en ound o be mo e e icien
when i comes o la ge-scale LP asks.
When he con inuous op imisa ion p oblem con ains nonlinea
unc ions, NLP app oach is equi ed o each he solu ion. Many
enginee ing asks can be ansla ed in o NLP p oblems. A sui able
me hod o sol ing a pa icula NLP p oblem should be selec ed
by conside ing i s size and cha ac e is ics o nonlinea i y.
Cu en ly, he e is no s anda d me hod ha can equally well
sol e all ypes o NLP p oblems. Howe e , as he e a e many
e icien algo i hms, an app op ia e one can be selec ed o sol e
a pa icula NLP p oblem. A his poin , he gene alized educed
g adien me hod [31], augmen ed Lag angian me hod [32, 33],
and successi e quad a ic p og amming [34], can p obably be
men ioned as he mos equen ly used NLP me hods.
MILP is an ex ension o he LP app oach. While LP algo i hms
can be used o sol e con inuous linea p oblems, MILP me hods
can handle linea op imisa ion asks wi h con inuous and
disc e e decision a iables. In MILP p oblems, disc e e a iables
can be decla ed as in ege a iables o as bina y 0-1 a iables.
S anda d me hod o MILP op imisa ion is he b anch and bound
me hod [35] al hough he cu ing plane me hod [36], as well as
he b anch and cu me hod [37], can also be e icien ly applied
in a numbe o cases.
MINLP me hods a e necessa y o disc e e op imisa ion
p oblems ha include nonlinea e ms in hei o mula ion.
Al hough high quali y exac solu ions can be expec ed om
he s a e-o - he-a MINLP me hods, i should be no ed
he e ha he ield o nonlinea (disc e e) op imisa ion is higly
complex and ha i has no ye eached he le el o ma u i y
a ained by linea (con inuous) op imisa ion. Ne e heless, a
numbe o e icien MINLP me hods a e a ailable o sol ing
nonlinea disc e e op imisa ion p oblems in ci il enginee ing,
such as he gene alized Bende s decomposi ion [38], nonlinea
b anch and bound me hod [39], easibili y echnique [40],
sequen ial linea disc e e p og amming [41], ex ended cu ing
plane me hod [42], augmen ed penal y/ou e -app oxima ion/
equali y- elaxa ion algo i hm [43], b anch and educe me hod
[44], mixed-in ege alpha BB algo i hm [45], hyb id algo i hm
[46], and o he s.
3. Op imisa ion modelling ools
A a ie y o comme cial so wa e o compu e -based modelling o
op imisa ion p oblems a e cu en ly a ailable. Syn axes o algeb aic
modelling languages a e o en lexible and allow o mula ion o
la ge-scale compac op imisa ion models ia indexing. In his
way, algeb aic modelling languages, such as AIMMS [47], AMPL
[48], CAMPS [49], GAMS [50], LINGO [51], LPL [52], MPL [53],
OPL [54] and UIMP [55], may be applied o complex and unique
op imisa ion p oblems, which may need se e al e isions be o e
an accu a e model is es ablished. They a e pa icula ly usable
in case o conside able numbe o cons ain s o he same ype
ha ollow a simila pa e n. In pa icula , he algeb aic modelling
language can simul aneously o mula e all cons ain s o he same
ype by simul aneously handling decision a iables o each ype.
The applica ion o he algeb aic modelling language accele a es
nume ous model managemen ac i i ies, such as ans o ming he
da a in o model pa ame e s, modi ying he model, accessing he
da a, and analysing model esul s.
Sp eadshee s a e also e y popula ools o modelling
op imisa ion p oblems. Mic oso ’s compu e package Excel
(Mic oso Visual Basic), wi h add-ins like E ol e [56], Sol e
[57], o Wha ’sBes [58], as well as WinQSB [59], can be de ined
as sp eadshee based compu e p og ams ha a e o en used o
modelling op imisa ion p oblems. Sp eadshee s a e use - iendly
ools and enable op imisa ion model o mula ion wi hin a amilia
so wa e en i onmen . On he o he hand, op imisa ion models
gene a ed by sp eadshee so wa e a e less anspa en han
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Václa Venk bec, Ma io Galić, U oš Klanšek
he ones o mula ed using he algeb aic modelling languages.
Tabula en y o model en i ies is also mo e ime consuming, and
so he sp eadshee -o ien ed modelling so wa e is no mally used
o c ea ing small- and medium-sized models wi h a easonable
numbe o pa ame e s. The e a e also specialized s and-alone
so wa e p og ams including in e alia Gu obi [60], TORA [61] and
LIONsol e [62].
In e ac i e compu e languages o nume ic and symbolic
calcula ions can also be applied o op imisa ion modelling.
So wa e packages such as Ma hema ica [63] and Ma lab [64]
can also be men ioned as in e ac i e compu e languages ha
a e widely used o op imisa ion modelling, al hough ha is no
hei main pu pose. Bo h o hese so wa e packages p o ide an
in e ac i e en i onmen o da a s uc u ing, and o modelling and
sol ing op imisa ion p oblems, while also enabling p esen a ion o
esul s in he o m o compu e p in ou s and cha s.
Clea signs ha e ecen ly eme ged abou he g owing in e es
among esea che s in he use o in e ne applica ions o
op imisa ion. One o he ways o pe o ming op imisa ion
o e he in e ne is o use open p og ams loca ed a he NEOS
se e [65]. A his se e , i is also possible o ind links o many
comme cial op imisa ion p og ams, manuals o hei use, es
esul s, and publica ions/jou nals wi h ecen op imisa ion
esea ch da a, e c.
4. Use o op imisa ion in cons uc ion p ocesses
4.1. Gene al o ms o op imisa ion p oblems
common o cons uc ion p ocesses
This sec ion p o ides a b ie o e iew o o igins and de ini ions
o some well-known op imisa ion p oblems ha can be
ecognized as mos common in cons uc ion p ocesses. Thei
o iginal name, basic desc ip ion and objec i es, as well as hei
o igin, a e shown in alphabe ical o de in Table 1. Op imisa ion
p oblems a e g ouped by hei main op imisa ion objec i es, and
hey o m wo basic domains o asks common o cons uc ion
p ocesses:
a) op imisa ion o esou ces
b) op imisa ion o layou and ou e.
Domain Op imisa ion p oblem P oblem desc ip ion O igin
Resou ce o ien ed op imisa ion p oblems
Assignmen p oblem (AP).
AP deals wi h he ques ion o how o assign a gi en numbe o asks o a gi en
numbe o agen s, whe e each agen can pe o m any asks and, a ha , his
agen c ea es a cos ha changes depending on he ask, and all asks mus be
comple ed, p o ided ha only one agen can be assigned a single ask, and he
o al cos s mus be minimised.
[66]
P ojec scheduling p oblem (PSP).
PSP ep esen s a wide se o p oblems, which a e commonly summa ized
by one dominan objec i e unc ion o minimiza ion o o al p ojec cos s. I
expanded wi h objec i e unc ions o minimiza ion o p ojec ime and cos s,
hey a e known as ime-cos ade-o p oblems (TCTO), and, i including also
he op imisa ion o o he esou ces, hey a e iden i ied as ime-cos - esou ce
op imisa ion p oblems (TCRO).
[67]
T anspo p oblem (TP).
TP is de ined as a p og am o sol ing anspo o goods om mul iple sou ces
o mul iple des ina ions wi h an objec i e unc ion aimed a minimising
anspo cos s. TP is gene ally a ma e ial ne wo k- low op imisa ion p oblem.
F om he iewpoin o TP o mula ion, he anspo cos s, supply and demand
quan i ies, a e o en inpu pa ame e s while anspo ing lows ep esen
decision a iables.
[68]
Layou and ou e o ien ed op imisa ion p oblems
A c ou ing p oblem (ARP).
ARP is a connec ed g aph cons uc ed o wo se s o poin s (o igins and
des ina ions) o which i is necessa y o ind he closed ou e ha isi s each
des ina ion poin a leas once, o o de e mine ha such a ou e does no exis .
[69, 70]
Capaci a ed a c ou ing p oblem
(CARP).
CARP has he objec i e o ind a numbe o ou es such ha each a c wi h
posi i e demand is se iced by exac ly one ehicle and, a ha , he sum o
demand o hose a cs, se iced by each ehicle, should no exceed gi en
capaci y and he o al cos o he ou es is minimized.
[71]
Chinese pos man p oblem (CPP).
CPP is de ined by he connec ed undi ec ed g aph wi h he known dis ance
ma ix, and he p oblem is o ind a ou e ha passes h ough each poin o he
g aph a leas once in he sho es possible way.
[72]
T a eling salesman p oblem (TSP).
The TSP is de ined as an op imisa ion p oblem o he salesman who needs
o a el om home loca ion o each loca ion speci ied on he lis and, a e
execu ing all isi s, o e u n o home loca ion while aking in o accoun he
objec i e o he sho es o al ou e o he minimum o al a el ime.
[73]
Vehicle ou ing p oblem (VRP).
VRP is de ined as a p oblem o how o op imally ou e a lee o iden ical
ehicles om a cen al poin o supply des ina ions wi h known demands
subjec o ehicle capaci y cons ain s.
[74]
Table 1. Op imisa ion p oblems common o cons uc ion p ocesses
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Table 2. Resou ce o ien ed op imisa ion p oblems applied in cons uc ion
Pe iod Desc ip ion o applied p oblem Me hods, models and ools Main indings and conclusions O igin
1997 - 2000
TCTO p oblem o an eigh een-
ac i i y cons uc ion p ojec .
Au ho s de eloped a new algo i hm
by combining gene ic algo i hm (GA)
and Pa e o on app oach.
The p esen ed new algo i hm has p o en o be e icien and accu a e
in sol ing he add essed p oblem by sea ching only a small ac ion o
he o al sea ch space. Fu he mo e, he au ho s de eloped a compu e
p og am TCGA ha uses he MS Excel p og am, and au oma es he
execu ion o he p oposed algo i hm.
[75]
TCTO p oblem in ol ing
gene a ion o six een
cons uc ion p ojec s each
consis ing o ele en ac i i ies.
Machine lea ning gene ic algo i hm
sys em (MLGAS).
P e ious analy ical echniques we e known o limi he usage o GAs as i
was necessa y o manually en e da a o he ime-cos cu es and his
in linea o m only. MLGAS o e comes hese limi a ions by inco po a ing
machine lea ning ia GAs.
[76]
Mul i-objec i e op imisa ion
o esou ce alloca ion and
le elling based on a case s udy
in ol ing wen y ac i i ies and
six esou ces.
Gene ic algo i hm was applied by
means o MS Visual Basic (VBA)
p og amming language.
Main con ibu ions o he men ioned app oach a e: e ec i e
imp o emen o esou ce alloca ion heu is ics using andom ac i i y
p io i ies; p ac ical modi ica ion o esou ce le elling heu is ics using
a double-momen app oach; and mul i-objec i e op imisa ion o bo h
esou ce alloca ion and le elling using GAs.
[77]
Resou ce le elling p oblem on
a nine-ac i i y cons uc ion
p ojec wi h mul iple esou ces,
aimed a minimising esou ce
u iliza ion a ia ion wi hin a
ixed du a ion p ojec .
GA (GARLS) based esou ce le elling
sys em.
GARLS model does no necessa ily need o commi o any speci ic heu is ic
ule; hence, i is mo e lexible o sol ing complex le elling and scheduling
p oblems. GARLS p o ides se e al easible o nea -op imal solu ions ha
may assis in p ojec decision-making. In his s udy, he au ho s success ully
used a a ie y o so wa e p og ams o GARLS implemen a ion (i.e. MS
P ojec , MS Access, MS Excel, and VBA). As a ecommenda ion o u he
de elopmen , he au ho s sugges modi ica ion o he p oposed model o
include TCTO p oblem sol ing as well.
[78]
2001 – 2010
Repe i i e scheduling p oblem
wi h sha eable esou ce
cons ain s in case o p ecas
p oduc ion.
GA-based esou ce-cons ained
epe i i e scheduling model, using
he VBA p og amming language.
The au ho s poin ou ha hei model inco po a es an e icien
compu a ional echnique o esou ce alloca ion and a mo e sui able way
o modelling he esou ce sha ing in epe i i e scheduling, compa ed
o he linea scheduling echnique. GA-based model does no ha e o
commi o any pa icula heu is ic ules and hus is mo e lexible. In
addi ion, he GA-based epe i i e scheduling model can explo e and use
se e al nea -op imal solu ions, which a e no mally no a ailable using
con en ional epe i i e scheduling echniques.
[79]
Repe i i e scheduling p oblem
o a p ojec consis ing o
ou simila sec ions o uni s,
and each includes epe i i e
ac i i ies wi h inish o s a
connec ions wi hou ime lags.
Au oma ed model based on dynamic
p og amming o mula ion.
I was concluded ha , since he model is au oma ed, i alle ia es
he need o he use o p o ide a se o in e up ion ec o s in an
a bi a y manne p io o scheduling. In addi ion, i signi ican ly educes
he numbe o in e up ion ec o s in a a ional manne , making he
op imisa ion p ocess easible; i also enables gene a ion o an op imum
solu ion.
[80]
Resou ce scheduling and TCTO
p oblem o a wel e-ac i i y
cons uc ion p ojec wi h he
assump ion ha he e a e
no limi a ions on p ecedence
ela ionships be ween
succeeding ac i i ies
Resou ce le elling using he
augmen ed Lag angian GA model.
The sugges ed model allows any linea o nonlinea unc ion o he
p esen a ion o cos -du a ion and esou ce du a ion ela ionships. The
model can also handle a wide ange o p ojec sizes including la ge
cons uc ion p ojec s in ol ing a la ge numbe o ac i i ies. The model was
e icien ly implemen ed in FORTRAN p og amming language in o de o
sol e esou ce scheduling p oblems on se e al cons uc ion p ojec s.
[81]
Mul i-objec i e TCTO p oblem
ha akes in o accoun
adap i e weigh s among
op imisa ion c i e ia on a case
s udy consis ing o se en
ac i i ies.
GA based model wi h a modi ied
adap i e weigh app oach.
Au ho s no e ha his app oach p o ides GAs wi h g ea e eedom
du ing sea ch in he mul i-objec i e space, which o e comes d awbacks
o he single objec i e TCTO. Howe e , because he model uses GAs
in e ms o a sea ch engine, i s andomness could a ec eliabili y o
esul s.
[82]
Resou ce-cons ained PSP
(RCPSP) aimed a minimizing
p ojec du a ion.
Me aheu is ic PSO based me hod.
Based on compu a ional analyses, au ho s concluded ha he
pe mu a ion-based PSO ou pe o ms he p io i y-based PSO, and
ha he PSO-based me hodology has good pe o mance, which is
compa able o o he me aheu is ic me hods such as GA and SA in
sol ing he RCPSP.
[83]
S ochas ic mul i-objec i e
TCRO p oblem wi h he ime
and cos a iables conside ed
as uzzy.
S ochas ic mul i-objec i e
op imisa ion model based on he
use o he non-domina ed so ing
GAs (NSGA-II).
This model imp o es he p e iously de eloped weigh ing app oaches by
p o iding a h ee-dimensional Pa e o on . I also adop s FSs o inco po a e
he unce ain ies in ime and di ec cos s o p ojec ac i i ies. The model can
also conside di e en le els o unce ain y by changing he α-cu le els.
Au ho s sugges ed ha u he de elopmen o he model could be ocused
on p o iding he capabili y o spli ing ac i i ies in he model.
[84]
PSP in case o mul iple shi s
on cons uc ion p ojec s, he
aim being o minimize p ojec
du a ion, cos , and nega i e
impac s o e ening and nigh
shi s.
Op imisa ion model consis s o
h ee modules: i) ini ializa ion
module o scheduling op imisa ion
compu a ions; ii) scheduling module;
and iii) mul i-objec i e GA module
The model was p o en capable o e alua ing and iden i ying op imum shi
sys ems o p ojec s and his al eady in he i s i e a ion. In addi ion, he
model o e s an op imum solu ion wi h minimisa ion o p ojec ime and
cos s, and wi h app op ia e dis ibu ion o wo ke s in o e ening and nigh
shi s, whe e e e y solu ion iden i ies an op imum schedule and mul iple
shi wo k plan o each ac i i y. The model also gene a es op imum plans
o dis ibu ing labou among compe ing shi s o minimize nega i e
impac s o labou cons ain s on p ojec pe o mance.
[85]

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He e i should be no ed ha he e a e many o he a ia ions o
op imisa ion p oblems, wi h a ious objec i es and cons ain s,
ha would be wo hy o conside a ion. Howe e , hey a e no
p esen ed he e due o he limi ed space a ailable o his pape .
4.2. O e iew o ecen op imisa ion applica ions in
cons uc ion p ocesses
Due o complex na u e o cons uc ion p oblems, and cyclic ela ions
o planning and op imisa ion p ocesses, i is o en di icul o
iden i y only one ype o he abo e p esen ed o iginal op imisa ion
p oblems, o e en o comple ely di e en ia e one p oblem om
ano he . Mos op imisa ion p oblems in cons uc ion appea o
be a combina ion o a ious o iginal op imisa ion p oblems. I is
he e o e ha d o summa ize all me hods ha a e used o sol ing
such p oblems. Ne e heless, in his sec ion, he au ho s p o ide
an o e iew o ecen and mos known (in he majo i y o cases
mos ci ed) applica ions, me hods and models o op imisa ion
p oblems in cons uc ion sec o . The o e iew is gi en in o m o
wo ch onologically s uc u ed ables di ided by hei op imisa ion
o ien a ions: esou ce o ien ed cons uc ion op imisa ion p oblems
(Table 2) and layou and ou e op imisa ion p oblems (Table 3). The
layou and ou e o ien ed op imisa ion asks applied in cons uc ion
(gi en in Table 3) a e ch onologically s uc u ed s a ing om 1995.
Since hey a e no as nume ous as he p e ious se o op imisa ion
p oblems, hey a e no di ided by longe pe iods.
5. Op imisa ion and BIM
5.1. Gene al connec ions be ween op imisa ion and
BIM
Op imisa ion can ce ainly be conside ed as po en ially
bene icial o many a eas in cons uc ion indus y. The basic
aim o his sec ion is o p esen connec ions es ablished
be ween op imisa ion and BIM ha ha e been ecen ly
epo ed in epu able scien i ic li e a u e. Namely, o he bes
o ou knowledge, such a e iew has no ye been ca ied ou
no wi hs anding he ac ha i conce ns a apidly de eloping
ield. O e iew o ecen achie emen s in he con ex o
connec ing op imisa ion and BIM is especially needed o iden i y
new pe spec i e opics o esea ch and o ill he li e a u e gap.
2011 – 2016
Non-linea quad a ic AP (QAP)
o owe c ane and ma e ial
supply loca ions.
MILP model.
Based on a nume ical example, au ho s ound ha he esul s gained
by MILP a e be e han he esul s ob ained by GA, wi h almos 7%
less in he o al ma e ial anspo cos . In addi ion, MILP o mula ion
was ound o be mo e lexible in e ms o including addi ional design
cons ain se s o modelling ac ual on-si e condi ions.
[86]
Nonlinea disc e e TCTO
(NDTCTO) p oblem o a
cons uc ion p ojec consis ing
o wen y-nine ac i i ies.
MINLP model.
The p oposed model is mo e complex and equi es g ea e analy ical/
compu a ional e o han he MILP model. Howe e , he au ho s no ed
ha he ad an age o he MINLP-NDTCTO model compa ed o he MILP
model lies in i s modelling capabili ies. In addi ion, he p oposed model
yields he exac op imum NDTCTP solu ion, while heu is ic models
calcula e app oxima e op imum solu ions.
[87]
PSP o he p ojec wi h
modula sca olding.
Mul i-objec i e cons ained
op imisa ion model based on he
disc e e i e ly algo i hm (DFA).
Resul s p o ed c edibili y o he op imisa ion model by p oducing a
be e solu ion o wo k o ce alloca ion enabling p ope ime and cos
balance.
[88]
TCTO p oblem o a
cons uc ion p ojec wi h a
smalle numbe o p ojec -
signi ican ac i i ies.
PSO me hod o op imizing global
c i ical pa h diag ams using he
MATLAB p og amming sys em.
The au ho s success ully applied PSO me hod o op imizing ealiza ion
o cons uc ion p ojec s. The p oposed model p o ides good esul s
and p esen s se e al ad an ages compa ed o me hods based on he
use o simplex algo i hms o linea p og amming, and o he adi ional
ma hema ical p og amming me hods.
[89]
NDTCTO p oblem o a
cons uc ion p ojec wi h a
noncon ex unc ion o cos s.
MINLP model.
MINLP op imisa ion model o handling noncon ex dependencies
was ound o educe he use e o in dealing wi h la ge-size da a
and upda ing he model when ci cums ances unde which he p ojec
scheduling was done ha e changed. The use o nonlinea exp essions
can enable a mo e compac model o mula ion as well as a mo e apid
execu ion o model managemen asks, such as ans o ma ion o da a
in o model pa ame e s and model modi ica ions.
[90]
AP and op imal esou ce
alloca ion p oblem on mul iple
ongoing p ojec s.
Bina y mul i-objec i e scena io
simula ion model.
The model was p o en as a use ul ool o sol ing small and medium
scale p oblems. I is adap able o changes o inpu da a. I also
enables compa ison o op imum and sub-op imum scena ios wi h he
co esponding ou pu da a, and i has hus ul illed he main expec a ions.
[91]
PSP unde es ic ed cos s. MINLP model.
The p oposed MINLP model comp ises o al p ojec cos s, gene alized
p ecedence ela ionship cons ain s, p ojec du a ion es ain s, logical
condi ions, and cos es ic ions.
[92]
TP o a ho asphal mix u e. E olu iona y algo i hm o he
mul iple c i e ia sol e (MCS).
Resul s ha e p o ed ha he e olu iona y algo i hm o he MCS is a use ul ool
o sol ing he p oblem o planning anspo o ho asphal mix when he o al
p ojec ealisa ion ime is no limi ed. The au ho s poin ou ha he model
needs o be modi ied o sol ing big p oblems and o aking in o accoun ime
and echnological ela ionships be ween sub p ocesses in he chain.
[93]
Table 2. Resou ce o ien ed op imisa ion p oblems applied in cons uc ion - ex ension
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Cons uc ion p ocess op imisa ion – e iew o me hods, ools and applica ions
Table 3. Layou and ou e o ien ed op imisa ion p oblems applied in cons uc ion
Desc ip ion o applied p oblem Me hods, models and ools Findings and conclusions O igin
Planning he cons uc ion-si e
access ou es o la ge ehicles.
The ou e-planning sys em was achie ed
using he expe sys em Nexpe Objec
and he geog aphic in o ma ion sys em
(GIS) A c/In o in conjunc ion wi h he
compu e -aided design (CAD) package
Mic os a ion, Excel sp eadshee , and
o he cus om p og ams.
Alongside he "wha -i " analysis, he au ho s unde line ha he use can employ
he p esen ed sys em o selec modi ica ion pa ame e s, modi y selec ed
pa ame e s, and es modi ica ion esul s. In u he wo k, au ho s sugges
de elopmen s in he ollowing sphe es: model unce ain y and abili y o iden i y
a eas o isk on po en ial ou es; quan i ica ion o use p e e ences using a
uzzy-logic app oach; and ep esen a ion o s a egies and knowledge equi ed o
scena io modi ica ion in o de o acili a e access o si e.
[94]
Cons uc ion si e layou
p oblem (CSLP) aimed a
minimising ma e ial anspo
cos s.
GA gene a es an ini ial popula ion o
layou s h ough a sequence o mu a ion
ope a ions, and e ol es popula ion
layou s h ough a sequence o gene ic
ope a ions aimed a inding an op imum
si e layou solu ion.
Au ho s emphasize ha he key ea u e o he p oposed algo i hm is ha i uses
a la ge numbe o di e en GA ope a o s o a y posi ions o objec s a ound he
si e. The GA ope a o s a e p og ammed in such o de ha he chance o inding
a easible posi ion o a selec ed block is maximized wi h a unc ion ha inds
and s o es se s o easible posi ions o a selec ed objec . Ano he key ea u e is
ha i main ains in each gene a ion he ch omosomes ep esen ing pa ial layou
solu ions. These so-called "bad" ch omosomes a e kep o help he e olu ion
p ocess ge ou o local op ima.
[95]
Scheduling, esou ce planning,
and cos op imisa ion p oblem in
he scope o la ge cons uc ion
and main enance p og ams ha
in ol e mul iple dis ibu ed si es
wi h he objec i e o inding an
op imum se o cons uc ion
me hods and an op imum
ou ing o de among si es.
The au ho s p opose a dis ibu ed
scheduling model (DSM) ha uses
GAs o de e mine an op imum se o
cons uc ion me hods and an op imum
ou ing o de among si es.
The DSM has p o en o be an indispensable addi ional ool du ing de e mina ion
o an app op ia e echnology and an eligible and easible lis o acili ies o be
included in municipal cons uc ion/main enance p og ams. I s bene i s include
de e mina ion o he numbe o equi ed c ews and hei de ailed wo k plan,
and conduc o sensi i i y analysis s udies o de e mining he mos p ope
ime o s a execu ion o a cons uc ion p og am; i can also p o ide a dynamic
en i onmen needed o mee cons ain s and decide on p ope co ec i e ac ions
du ing execu ion.
[96]
Logis ic and dispa ching
p oblems occu ing du ing
disas e elie ac i i ies. They
a e cha ac e ized by empo a y
escape ou e p oblems, ehicle
di ec ing p oblems, and he
mul i-commodi y dispa ch
p oblems.
Me a-heu is ic me hod o an colony
op imisa ion (ACO), which decomposes
he o iginal eme gency logis ics p oblem
in o wo sequen ial phases and i e a es
be ween hem.
In compa ison o he CPLEX solu ion, he quali y solu ion gained by ACO is achie ed
wi hin a minu e o un ime, which is especially signi ican in eme gency si ua ions
in ol ing con inuous unce ain y and in o ma ion dynamism. Howe e , he local
sea ch based me a-heu is ics, such as abu sea ch, needs u he s udy o p o e
i s e iciency in sol ing his p oblem. I should be no ed ha in oduc ion o local
sea ch in o he pos -op imisa ion p ocedu e does no enhance he o e all solu ion
e iciency, al hough some p o isional solu ion quali y is imp o ed in he p ocess.
Compu a ional esul s sugges ha his decomposi ion app oach may be e icien
o o he complex combina o ial p oblems wi h in e dependen decision a iables.
[97]
T a ic delays and scheduling
p oblem based on he ou e-
changing beha iou o oad
use s.
Mic o-simula ion model in ol ing eam
ACO (TACO) sea ch o a nea -op imal
scheduling, based on he VISSIM
simula ion so wa e.
Resul s indica e ha he o al a ic delay can signi ican ly be educed by
means o he p oposed model. Compa ed wi h o he ma hema ical me hods, he
mic oscopic simula ion equi es mo e compu a ional ime, bu i is close o he
eal- ime si ua ion, wha makes he p edic ion and es ima ion mo e eliable.
[98]
VRP model o uck mixe s
a elling o e a wo king
day om conc e e plan o
conc e e-demanding cus ome s
and ice e sa.
Combina ion o MILP model wi h gene ic
local sea ch app oach using he CPLEX
p og am.
Gene al MILP sol e is inadequa e o sol ing eal ime la ge-scale p oblems. In
o de o sol e such p oblems, he au ho s combined MILP wi h he local sea ch
app oach. The sugges ed app oach is based on he assump ion ha all inpu da a
a e a ailable o analysis. The e o e, he au ho s ecommend his app oach as a
use ul ool o baseline planning.
[99]
CSLP.
In eg a ed simula ion sys em consis ing
o he in ica e sea ch and GAs, based on
he Symphony pla o m.
The p oposed simula ion sys em can easily be ex ended o accommoda e mo e
disciplines and s a egies in o de o p oduce mo e ad anced ou pu s. The
de eloped modelling sys em enables unnelling wo k expe s o c ea e models, and
o expe imen wi h di e en scena ios wi hou he sys em de elope ’s ins uc ions.
[100]
Dynamic, mul i-objec i e CSLP
wi h unequal-a eas.
Max–Min an sys em (MMAS) and
modi ied Pa e o-based ACO algo i hm.
The in ui ionis ic uzzy
The pe o mance o CSLP decision-making sys em is e i ied on he case s udy o
a esiden ial building. I has p o en o be a use ul ool o p ojec manage s and
planne s in he design o cons uc ion si es cha ac e ized by mul iple con lic ing
o cong uen objec i es. In addi ion, i helps use s o design a cons uc ion si e
layou , including o he quali a i e ac o s, such as ease o supe ision and con ol.
[101]
TOPSIS me hod was used
o he e alua ion and le el
selec ion s ages.
The MTCARPTW was ans o med in o
TSP in o de o apply a heu is ic ACO
algo i hm.
Resul s sugges ha he p oposed app oach e ec i ely wo ks wi h a MTCARPTW
con aining less han i y nodes, yielding a good solu ion wi hin a sho compu ing
ime. Au ho s sugges ha u u e s udies should explo e expanding he p esen
p oblem in o p ac ical-sized ne wo ks in o de o examine whe he he ACO
algo i hm ou pe o ms all known heu is ics on he MTCARPTW, and o de e mine
in wha way he algo i hm's e iciency is a ec ed by an inc ease in he ypes o
se ices.
[102]
CSLP as a owe c ane
layou p oblem wi h he
ma e ial supply and demand
op imisa ion.
Au ho s compa ed ela i e e iciency
o he PBA, PSO, and BA o sol ing he
add essed p oblem.
Resul s show ha PBA pe o ms be e han he o he wo algo i hms. Al hough
he PBA pe o ms well in op imizing loca ion o owe c anes, he algo i hm
is unable o minimize ope a ing cos s o supply in case o mul i-dimensional
p oblems.
[103]
Ready-mixed conc e e
dispa ching and deli e y
p oblem.
The au ho s compa ed obus GA and
Column Gene a ion (CG) o sol ing eal
p oblems o di e en sizes.
Resul s show ha , on an a e age, CG ob ains solu ions a 20% lesse cos .
Howe e , obus GA con e ges 40% as e han CG, while he numbe o
unassigned cus ome s is almos he same o bo h echniques.
[104]
Ready-mixed conc e e deli e y
p oblem.
The au ho s p esen ed a scena io
simula ion model using he En e p ise
Dynamics so wa e.
The p esen ed model has p o en o be a use ul ool o sol ing disc e e small and
medium size p oblems ela ed o he deli e y o eady-mixed conc e e. I p o ides
an op imum solu ion and also he se o sub op imal solu ions, which imp o es he
decision making p ocess o he planne . Howe e , model’s dependence on web
maps is a majo cons ain hinde ing applicabili y o he model.
[105]
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The BIM pa adigm is one o he mos p omising de elopmen s
in he a chi ec u e, enginee ing, and cons uc ion indus ies
[106]. The mos equen ly used de ini ion is: BIM is essen ially
alue c ea ing collabo a ion h ough he en i e li e-cycle o an asse ,
unde pinned by he c ea ion, colla ion and exchange o sha ed 3D
models and in elligen , s uc u ed da a a ached o hem [107].
Building in o ma ion model is da a- ich, objec -o ien ed,
in elligen and pa ame ic ep esen a ion o physical and
unc ional cha ac e is ics ans o med in o a mul i-dimensional
digi al compu e -based model. All cha ac e is ics abou acili ies
ealised h ough BIM-based ools a e a ailable o he use .
BIM applica ions can be ca ego ized acco ding o hei basic
ocus. Mos o BIM-based ools manually ans o m accu a e
syn he ic da a in o i ual eali y and usually con ain 3D
geome ical model wi h o he isualized in o ma ion (i.e.
dimensions). This app oach can be named as passi e BIM
because he analy ical pa o he model is absen .
The analysis o s uc u al da a, such as an op imum ime scheduling
and cons uc ion si e wo kspace planning, isk assignmen con ol,
heal h and sa e y con ol o cons uc abili y e iew, also in ol es
he use o o he ools, which mus be sui ably backed by compu e
skills and expe knowledge o he use .
Viewed in he s ic ma hema ical sense, BIM as a concep is no
an op imisa ion app oach un il a leas one o he op imisa ion
me hods is applied. This pu s in o pe spec i e he di e ences
be ween he simula ed solu ion and he op imized one. The
main pu pose o his sec ion is o place emphasis on ecen ly
published BIM-based app oaches, ope a ionally named as
ac i e BIM app oaches. In hese cases, models a e wo king wi h
analy ical da a h ough algo i hms. The ollowing subsec ions
concen a e on only hose ac i e BIM applica ions ha employ
op imisa ion echniques and a e mos equen ly iden i ied in
cons uc ion managemen li e a u e, i.e. pa icula ly hose o
APs, CSLPs, and PSPs.
5.2. Ac i e BIM applica ions
The aim o he ollowing able is o show ac i e BIM applica ions
(i.e. in eg a ion o op imisa ion echniques and BIM) de eloped
o he pu pose o gene a ing op imum esul s. Mos equen ly
iden i ied applica ions a e namely he ones o APs and CSLPs.
Table 4 shows con ibu ions in a ime-so ed s uc u e.
Ac i e BIM applica ions o PSPs a e shown by ch onological
o de in Table 5.
Yea Desc ip ion o applied
p oblem Me hods, models and ools Main indings and conclusions O igin
2003*
AP o inding op imal
posi ions o empo a y
acili ies in CSLP.
Au ho s de eloped a new algo i hm
by using GA on he CAD-based
pla o m.
The pape concluded ha he GA by i sel does no ensu e an op imum CSLP
solu ion, bu may o e a nea op imum solu ion. By minimizing he objec i e
unc ion, he GA accomplishes he complex ask o assigning empo a y acili-
ies o posi ions consis en wi h hei espec i e p oximi y needs.
[108]
2005*
AP o op imizing
loca ion o c anes and
acili ies.
GA me hod was used o posi ioning
c anes and acili ies.
Signi ican ime sa ings can be made using he GA model o c ane posi ioning
on cons uc ion si es. The se ing o wo ypes o ch omosomes has been
ound use ul o gene a ing he GA model. The i s ch omosome indica es
he c ane posi ion code while he second one indica es he numbe o c anes.
Fu u e wo k can be ex ended o in eg a e and hyb idize he GA-based model
wi h he 3D isualiza ion echnique.
[109]
2010* CSLP o ai po expan-
sion p ojec s.
GA me hod was used o posi ioning
c anes and acili ies.
Signi ican ime sa ings can be made using he GA model o c ane posi ioning
on cons uc ion si es. The se ing o wo ypes o ch omosomes has been
ound use ul o gene a ing he GA model. The i s ch omosome indica es
he c ane posi ion code while he second one indica es he numbe o c anes.
Fu u e wo k can be ex ended o in eg a e and hyb idize he GA-based model
wi h he 3D isualiza ion echnique.
[110]
2014
BIM-based CSLP
ocusing on ac ual a el
pa hs.
GA was employed o gene a e
dynamic CSLP model. Au odesk
Re i was used as he BIM ool and
Mic oso P ojec was employed o
scheduling. The au ho s de eloped
hei own so wa e ha eads excel
sp eadshee s.
Resul s show ha he linea dis ance based op imisa ion o minimize he
anspo dis ance o si e pe sonnel could lead o he gene a ion o sub-
op imal layou s, and hence he ac ual a el dis ance was used. The me hod
gene a ed layou s ha educed o al on-si e a el dis ances by 16.5%. Fu u e
esea ch on his model could be o ien ed owa d in eg a ion o he 4D con-
s uc ion simula ion.
[111]
2015
BIM-based au oma ed
CSLP o conges ed
cons uc ion si es.
GA was applied o gene a e dynamic
CSLP models. Calcula ions we e
based on BIM gene a ed da a.
Resul s show a educ ion o app oxima ely 13.5 % in he ac ual a el dis-
ance on cons uc ion si e compa ed o con en ional me hods. The au ho s
concluded ha he con en ional algo i hm, which uses di ec dis ances in he
op imisa ion p ocess, is no su icien . The model could be ex ended o include
eal ime cons uc ion schedules and ma e ial logis ics models.
[112]
2015
BIM-based au oma ed
CSLP o cons uc ion
si e cos op imisa ion
GA, BIM and RFID a e combined o
op imise gene a ion o cons uc ion
si e layou s in eal ime.
The esul s show ha he p oposed sys em can au oma ically ack empo a y
acili ies, and model a ailable cons uc ion si e spaces, in o de o minimise
cos s.
[113]
Table 4. Ac i e BIM applica ions o APs and CSLPs
G ađe ina 7/2018
601
GRAĐEVINAR 70 (2018) 7, 593-606
Cons uc ion p ocess op imisa ion – e iew o me hods, ools and applica ions
Table 4. Ac i e BIM applica ions o APs and CSLPs - ex ension
Table 5. Ac i e BIM applica ions o PSPs
2014
2015
AP o gene a ing an
op imal owe c ane
layou plan.
A BIM applica ion is used o
au oma ically gene a e ma e-
ial quan i ies needed o on-si e
anspo . FA-based op imisa ion is
employed o de e mine owe -c ane
loca ions, as well as ma e ial sou ce
and des ina ion poin s.
Resul s show ha less ime is needed o c ea e a owe c ane layou scheme
in compa ison wi h adi ional me hods, especially when mo e han one owe
c ane is used. The me hod also gene a es sa ings in o al ma e ial anspo
cos s and possible collision cos s as, due o isualisa ion o he p ocess, he
wo ke s can easily unde s and and implemen he owe c ane layou scheme.
[114]
[115]
2015
AP o op imum owe
c ane posi ioning and
c ane ype selec ion.
The analy ical hie a chy p ocess
(AHP) is u ilized as a mul i-c i e ia
decision making (MCDM) me hod o
c ane ype selec ion. GA is used o
de ining an op imum c ane layou ,
sho es ail leng h, and an op imum
li ing assignmen plan. BIM model
is u ilized o ex ac ing quan i ies
equi ed o GA op imisa ion.
Resul s show ha he use o AHP me hod o c ane ype selec ion is qui e
signi ican and ha i p esen he highes le el o sensi i i y by o e ing he
g ea es numbe o c i ical selec ion c i e ia. The p esen ed example shows
ha he hamme head owe c ane is he mos sensi i e c ane ype since i
has ob ained he highes sensi i i y coe icien s. Au ho s concluded ha he
owe c anes BIM model can addi ionally be de eloped by adding mo e unc-
ionali ies such as enabling ad anced spa ial que y unc ions, o in oducing
mobile c ane modelling capabili ies.
[116]
2016
AP o es ima ing a el
equencies in CSLP.
Objec i e unc ion mini-
mizes a el cos s.
In eg a ion o BIM inpu da a and
a el equency scheduling is p e-
sen ed. The VBA analyse is coded
o linking in o ma ion ga he ed
om bo h BIM and p ojec schedule.
Es ima ed a el equencies a e se
as inpu da a o MILP based CSLP
model.
Resul s show capabili ies o he p oposed model o au oma ing he a el
equency es ima ion p ocess. The au ho s concluded ha a el equency
alues used om o he p ojec s can gene a e some e o s in he op imisa ion
p ocess. I is sugges ed ha u he de elopmen and imp o emen in model
accu acy be made by collec ing own da abase om mo e ypes o p ojec s.
[117]
2016
AP o c ane and ma e ial
supply poin s posi ioning,
aking in o accoun ope -
a ing and en al cos s.
MILP model o c ane loca ion op i-
misa ion ha minimizes o al cos s.
A case s udy model is linea ized and
sol ed using CPLEX sol e .
The owe c ane loca ion ob ained by he p oposed model e eals signi ican
di e ences when he c ane capaci y is no aken in o accoun . In his case, ap-
p oxima ely 30% educ ion in o al cos s is achie ed using he p oposed MILP
model. The esul s show ha o e looking he c ane capaci y equi emen s in
he c ane loca ion p oblem may lead o e o s as high as 20% when compa ed
o he op imum o al cos s.
[118]
* Con ibu ions ep esen CAD-based p edecesso s o he ac i e BIM-based applica ions. Thei ele ance is in he connec ion be ween op imisa ion me hods and p e ious
gene a ion o compu e -aided ools o AEC indus y.
Godina Opis p oblema Me ode, modeli i ala i Dop inosi i zaključci Iz o
2008* PSP o ime-cos in e-
g a ed schedule.
The objec sequencing ma ix (OSM)
and GA a e used. Mul i-dimensional
CAD is used as model ex ension.
The me hodology o using GA o op imise cons uc ion sequences and de ine
c ew assignmen s is p esen ed in six main s eps in he pape . The model akes
in o accoun cons ain s ega ding esou ces, wo kspace and p oduc i i y.
[119]
2012 PSP and wo kspace
con lic s p oblem.
GA-based minimiza ion o
wo kspace con lic s and a 4D CAD
sys em a e used.
The p oposed s udy e eals ha he wo kspace model and compu ed con lic s
a io should be de e mined p io o he op imisa ion o wo kspace con lic s.
The eupon, adjacency o mu ual wo kspaces can be e iewed and schedule
con lic s can be de ec ed. Da a can be op imized by GA in o de o ob ain e-
sul s wi h minimum du a ion o con lic s. I is expec ed ha all hese unc ions
will be used as basic da a o he es ablishmen o an ac i e BIM en i onmen .
To he bes o ou knowledge, he e m "Ac i e BIM" is used o in his pape
o he e y i s ime.
[120]
2013 PSP o educing sched-
ule o e laps.
BIM wi h uzzy heo y unc ion is
used o quan i ying cons uc ion
isk. GA is applied o schedule
op imisa ion.
Reduc ion in schedule o e laps on a cons uc ion p ojec wi h e olu ion o
each GA gene a ion can be isually checked ia he p esen ed ac i e BIM
sys em. Resul s ob ained by he p oposed GA model e eal ha he schedule
o e lapping a e is educed by app oxima ely 33 %.
[121]
2013
PSP o scheduling wi h
he minimum o e lap-
ping le el.
GA and uzzy-based isk analysis
a e used.
The au ho s demons a e ha e olu ion o esul s in each gene a ion can be
isually checked by an ac i e BIM sys em. I is shown in he pape ha GA
can be used no only o schedule op imisa ion bu also o de eloping p ojec
schedules om he sc a ch.
[122]
2014 PSP o educing sched-
ule o e laps.
The au ho s use a e ised GA
o minimizing schedule and
wo kspace in e e ence, as well
as an ac i e BIM-based simula-
ion sys em o isualisa ion o he
solu ion.
Acco ding o he au ho s, he de eloped sys em can be u ilized as an ac i e
BIM ool ha can be applied o si es wi h minimum special knowledge equi ed
om he adminis a o . Based on s udy esul s, he alue o he schedule-
wo kspace in e e ence impac ac o is educed o 26.4% o he planned alue
(i.e. educed by 73.6% compa ed o he plan). Howe e , he p oposed app oach
is used on a simple example and a de ailed modi ica ion o he 4D model is
conside ed impo an p io o i s applica ion on o he p ojec s.
[123]