Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization

8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization

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S ystemsAnalysis LaboratoryHelsinki University of Technology

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Maintenance Scheduling of Fighter Aircraft Fleetwith Multi-Objective Simulation-Optimization

Ville Mattila, ai Virtanen, and !aimo "# $%m%l%inen

Systems Analysis Laboratory

Helsinki University of Technology

ville#a#mattila&t''#fi, 'ai#virtanen&t''#fi, raimo&hut#fi

mailto:[email protected]:[email protected]:[email protected]:[email protected]


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Maintenance )cheduling of fighter aircraft fleet

* +ten)ive periodic maintenance

+n)uring

* Flight )afet.

* "erformance

/ormal condition)

Several maintenance level)

* 0uration)

* Fea)ible time window of maintenance↔

+lap)ed flight hour) of an aircraft

* Maintenance )cheduling

Aircraft availabilit. guaranteed

Maintenance re)ource) guaranteed

"lanning period ≈ 1 .ear


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3hallenge) in maintenance )cheduling

* Maintenance and u)age coupled through comple nonlinear

interaction) feedbac')* Maintenance and u)age entail uncertaintie)

⇒ 4raditional )cheduling formulation) not )uitable

Our multi-objective simulation-optimization approach

* 0i)crete-event )imulation model for aircraft maintenance and u)age5Mattila, Virtanen, and !aivio (6678

* Optimization algorithm9 Simulated annealing u)ing probabilit. of dominance

⇒ /on-dominated )olution)

* Multi-attribute deci)ion anal.)i) model⇒ "referred )olution "reference programming 5Salo and $%m%l%inen 1::(, (6618


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Manual planning


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=mplementation of the )chedule


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4he multi-objective )imulation-optimization approach


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@eneration of non-dominated )olution)

* +i)ting algorithm) for multi-objective )imulation-optimization

Multi-objective evolutionar. algorithm) 5+A)8 5e#g#, ee et al# (667B @oh and 4an (66:8

* +#g# ran'ing of )olution) ba)ed on probabilit. of dominance 5$ughe) (6618

"opulation-ba)ed )imulated annealing 5SA8, weighted objective) 5@utjahr (668

Succe)) of multi-objective SA algorithm) in determini)tic )etting)

5Smith et al# (667B Dand.obadh.a. et al# (6678

* 4he multi-objective SA algorithm for maintenance )cheduling

"erformance of a )olution ba)ed on probabilit. of dominance Outperformed population-ba)ed SA 5@utjahr (66


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4he multi-objective SA algorithm

* Structure )imilar to ba)ic SA* Modification) for multi-objective )imulation-optimization

"erformance of )olution x↔

"robabilit.9 Solution x dominate) member) y of non-dominated )et S

* "robabilit. wrt objective i9

* "robabilit. wrt )olution y9

⇒

Maintaining non-dominated )et S

* Fied number of )olution) with highe)t performance included

P x dom y wrt objective i

P x dom y =∏i

P x dom y wrt objective i

"erformance of x=∑ y∈S

P x dom y


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Selection of the preferred non-dominated )olution

* E)e of preference information in multi-objective )imulation-optimization

4ran)formation into a )ingle objective

* Etilit. function a ran'ing and )election method

5Dutler, Morrice, and Mullar'e. (6618

* Value function a re)pon)e )urface method

5!o)en, $armono)'., and 4raband (66?8

* Our deci)ion anal.)i) approach "o)t-optimization anal.)i)

"reference programming and interval techniGue) 5Salo and $%m%l%inen 1::(, (6618

⇒3on)ider) uncertaint. both in objective function value) and 0MH)

preference )tatement)

* Iuan et al# 5(66?89 E)e of interval) in an +A ⇒ "referred )ub)et) of non-

dominated )olution) in a determini)tic )etting


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4he multi-attribute deci)ion anal.)i) model

V x =w Av A xw D v D x

v A , v D Objective function value)

for Availabilit. and 0eviation

)ingle attribute value)w

A ,w

DJeight)

{V x =min

w A , w D

w A v A xw D v D x

V x =maxw A , w Dw A v A xw D v D x

Additive presentation of DM'spreference for solution x

Simulation model⇒

Confidence intervals ofobjective function values

Single attribute

value interval)9

[v A x , v A x]

[v D x , v D x]

DM⇒

Incomplete preferencstatements

Jeight interval)9

[w A , w A]

[w D , w D ]

Overall value

interval of a

solution


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3ompari)on of non-dominated )olution)

* 0ominance concept)

Ab)olute dominance9

Value interval) do not overlap

"airwi)e dominance9

Value interval) do not overlap for an. fea)ible combination) of weight)

* =f )ingle dominating 5Kpreferred8 )olution doe) not ei)t

More preci)e preference information⇒ narrow) weight interval)

Additional )imulation⇒ narrow) )ingle attribute value interval)

0eci)ion rule), e#g#, maximin, maximax, central vales


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A ca)e eample

* 1> aircraft

* 4ime period of 1 .ear

* >; )cheduled

maintenance activitie)

Reference non-dominated set• Weigted aggregation of objectives functions

• Several optimi!ation runs

"on-dominated solutions using te

multi-objective SA algoritm

#se of probabilistic dominance* "on-dominated set can contain solutions dominated $rt point estimates


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Overall value interval)

* 12 )olution) ab)olutel.dominated

* ? )olution) remain, A###@

* E)e of deci)ion rule)

Maima9A ha) highe)t upper bound

Maimin9

D ha) highe)t lower bound


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3onclu)ion)

* 4he multi-objective )imulation-optimization approach

4he multi-objective )imulated annealing algorithm utilizing probabilit. of

dominance

4he multi-criteria deci)ion anal.)i) model utilizing preference programming

* Application in a comple maintenance )cheduling problem

Deing implemented a) a deci)ion-)upport tool

* Future re)earch on multi-objective )imulation-optimization algorithm)

E)e of preference information

+fficient allocation of computational effort


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!eference)* Dand.obadh.a. S#, Saha S#, Mauli' E#, and 0eb #, (667# A Simulated Annealing-Da)ed Multiobjective

Optimization Algorithm9 AMOSA# !""" Transactions on "voltionary #om$tation, 1(528, pp# (>:-(72#

* Dutler C#, Morrice 0# C#, and Mullar'e. "# J#, (661# A Multiple Attribute Etilit. 4heor. Approach to

!an'ing and Selection# %anagement Science, ;?5>8, pp# 766-71>#

* @oh 3# # and 4an # 3#, (66:# +volutionar. Multiobjective Optimization in Encertain +nvironment)#

Springer#

* @utjahr J# C#, (66-;:1#* Mattila V#, Virtanen #, and !aivio 4#, (667# =mproving Maintenance 0eci)ion-Ma'ing in the Finni)h Air

Force through Simulation# !nterfaces, 27528, pp# 17?-(61#


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!eference)

* Mattila V# and Virtanen #, (66># Scheduling "eriodic Maintenance of Aircraft through Simulation-Da)ed

Optimization# =n Cuu)o +#, ed#, Procee)ings of the /0 th #onference on Simlation an) %o)eling , $el)in'i,

Finland, September (?-(:, pp# 27-;2#

* Iuan @#, @reenwood @# J#, iu 0#, and $u S#, (66?# Searching for Multiobjective "reventive Maintenance

Schedule)9 3ombining "reference) with +volutionar. Algorithm)# "ro$ean -ornal of +$erational

.esearch, 1??, pp# 1:>:-1:7;#

* !o)en S# #, $armono)'. 3# M#, and 4raband M# 4#, (66?# A Simulation-Optimization Method that

3on)ider) Encertaint. and Multiple "erformance Mea)ure)# "ro$ean -ornal of +$erational .esearch,

171, pp# 218, pp# 161#

* Salo A# and $%m%l%inen !# "#, (661# "reference !atio) in Multi-Attribute +valuation 5"!=M+8 +licitation)

and 0eci)ion "rocedure) Ender =ncomplete =nformation# !""" Transactions on Systems, %an, an)

#ybernetics 1 Part A' Systems an) Hmans, 215>8, pp#

Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization

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