Post on 07-Jul-2018
transcript
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
1/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
1
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:ville.a.mattila@tkk.fimailto:kai.virtanen@tkk.fimailto:kai.virtanen@tkk.fimailto:ville.a.mattila@tkk.fi
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
2/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
(
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
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
3/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
2
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 $%m%l%inen 1::(, (6618
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
4/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
;
Manual planning
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
5/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
<
=mplementation of the )chedule
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
6/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
>
4he multi-objective )imulation-optimization approach
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
7/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
?
@eneration of non-dominated )olution)
* +i)ting algorithm) for multi-objective )imulation-optimization
Multi-objective evolutionar. algorithm) 5+A)8 5e#g#, ee et al# (667B @oh and 4an (66:8
* +#g# ran'ing of )olution) ba)ed on probabilit. of dominance 5$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
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
8/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
7
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
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
9/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
:
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
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
10/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
16
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
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
11/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
11
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
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
12/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
1(
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 $rt point estimates
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
13/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
12
Overall value interval)
* 12 )olution) ab)olutel.dominated
* ? )olution) remain, A###@
* E)e of deci)ion rule)
Maima9A ha) highe)t upper bound
Maimin9
D ha) highe)t lower bound
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
14/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
1;
3onclu)ion)
* 4he multi-objective )imulation-optimization approach
4he multi-objective )imulated annealing algorithm utilizing probabilit. of
dominance
4he multi-criteria deci)ion anal.)i) model utilizing preference programming
* Application in a comple maintenance )cheduling problem
Deing implemented a) a deci)ion-)upport tool
* Future re)earch on multi-objective )imulation-optimization algorithm)
E)e of preference information
+fficient allocation of computational effort
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
15/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
1<
!eference)* Dand.obadh.a. S#, Saha S#, Mauli' E#, and 0eb #, (667# A Simulated Annealing-Da)ed Multiobjective
Optimization Algorithm9 AMOSA# !""" Transactions on "voltionary #om$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#
8/18/2019 Maintenance Scheduling of Fighter Aircraft Fleet With Multi-Objective Simulation-Optimization
16/16
S ystemsAnalysis LaboratoryHelsinki University of Technology
1>
!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#