MO-FDJSPM GA - Sharif MBA Students Forum

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7 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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MO-FDJSPM
GA
(
N_nahavandi@modares.ac.ir
)
(
M.abbasian586@gmail.com
)
MO-FDJSPM
MO-FDJSPM
[
1
]
[2]
MO-FDJSPM
[3]
[4]
[5]
MO-FDJSPM
[6]
[7]
[8]
NP-hard
[9]
[10]
R
[11]
[1]
[12]
R
[13]
R
N-R
[14]
R
NP-hard
N-R
S-R
PM
N-R
[15]
N-
R
[6]
JS
N-R
[16]
N-R
Rolling Time Horizon
[18,17]
SA
4
PSO
5
SEA
7
GA
9
[4]
[19]
[20]
[21]
FJS
[22]
FJS
[23]
FJS
[3]
Np-hard
[24]
SPT
[26,25]
FSPM
RKGA
[4]
[27]
FJSP
FJSP
LEGA
K-nearest
[28]
[29]
[6]
FJSP
[31,30]
FDJSPM
[32]
SAS
[33]
GEP
GEP
[34]
[35]
[3]
[5]
Mutation
1
Mutation
2
MO-FDJSPM
NP-hard
[
1
]
Max
C
T
T
T
F
[
23
]
MO-FDJSPM
n
m
O
r
M
L
[5]
n
L
i
o
i
i
i
r
l
m
l
o
ik
m
lj
M
ikl
W
l
p
iklj
m
lj
R
lj
P
Mljr
t
ljr
o
i
i
lj
m
t
m
lj
o
ik
k
i
R
lj
m
lj
i
r
i
PM
ljr
r
m
lj
W
l
l
t
ljr
PM
ljr
m
l
l
E
ljr
U
PM
ljr
m
lj
j
l
L
ljr
U
PM
ljr
p
iklj
o
ik
m
lj
PM
ljr
M
ikl
o
ik
l
c
ik
o
ik
u
ljr
PM
ljr
L
ljr
E
ljr
UU,
Min
332211
FFFF
(Minimization Weighted Sum of Three Objectives)
(1)
NiCMaxCF
i
,...,1|
max1
(Minimization of Makespan)
(2)
N
i
ii
rC
N
FF
1
2
max
1
(Minimization of Mean flow time)
(3)
N
i
iii
dC
N
TF
1
3
0),(max
1
(Minimization of Mean Tardiness)
(4)
S.t
.
ikljikljkiik
xpcc
)(
1,

jliok
i
,,;,...,3,2
(5)

01
ikhqhqljikljikljhqik
yxxpcc

jlqkhihi,,,,,
(6)

0
ikhqhqljikljhqljikhq
yxxpcc

jlqkhihi,,,,,
(7)

0)1(
ikljrikljiikljljrik
vxrpuc

rjlki,,,,
(8)

0
ikljrikljiljrikljr
vxrtcu

rjlki,,,,
(9)

L
l Mj
iklj
ikl
x
1
1

ki,
(10)

L
ljrljr
E
ljr
UuU

rjl,,
(11)

0
ik
c

ki,
(12)

0
ljr
u

rjl,,
(13)

1,0,,
ikljrikhqiklj
vyx

rjlqkhihi,,,,,,
(14)
MO
-
FDJSPM
123
,,
1
j
123
,,
123
,,
3
1
MO
-
FDJSPM
Lingo
MO
-
FDJSPM
NP-hard
MO
-
FDJSPM
FDJSP
[2]
FDJSP
Np-hard
[22]
MO
-
FDJSPM
Np-hard
MO
-
FDJSPM
[36]
[37]
MO
-
FDJSPM
[23]
[25]
SizePop
RMX
c
P
RMX
1
m
p
2
m
p
[17]
max_gen
[24]
[11]
Best
GA
[6]
L
ljrljr
Uu
E
ljrljrmljr
UttMaxu
lj
,
RKGA
(R=0)
RKGA
(R=0)
RKGA
(R=1,2,3)
(R=0)
[6]
U
(R=1,2,3)
C++
GA
U(
) – U(
)
U(
) – U(
)
U(
) – U(
)
U(
)
RKGA
R=1
R=0
R=2
R=0
)(
c
p
1m
p
2m
p
(R=0)
RKGA
RKGA
RKGA
(R=0)
RKGA
(R=1,2,3)
[6]
R=0
R=1
R=0
R=2
(R=0)
(R=1)
R=0
R=2
R=0
R=3
R=0
R=3
R=3
R=0
[26,25,27]
TWK
-
method
[12]
c
MO
-
FJSP
ji
p
,
ji
p
,
))((
ij
ofn
i
MO
-
FDJSPM
))((
))((
,,
ij
ofn
kji
ij
ofn
p
p
ij
m
k
k
ji
k
m
k
l
pm
pmkkji
ji
k
ij
la
Spa
p
k
1
),(
1 1
,,,
),(
][
),( ji
k
a
k
ij
O
pmk
S
,
k
l
pmk
M
,
k
(R=0)
RKGA
(R=0)
(R=1)
(R=0)
(R=2)
(R=0)
(R=3)
)(
)(
c
p
)(
1
m
p
)(
2
m
p
GA
c
p
)(
1
m
p
)(
2
m
p
size
p
op
)_( genMax
)(
)(
c
p
)(
1
m
p
)(
2
m
p
C++
Pentium IV (CPU 3 GHz, 1 GB of RAM
)
Borland C++
5.02
GA
GP
GA
GP
[30]
MO-FDJSPM
States
Values
:
Parameter
1
100%(FJSP-100) ; 50%(FJSP-50) ;
20%(FJSP-20)
[1
m] ; [0.5
m] ; [0.2
m]
:Flexibility
1
10
5,20
5,50
5,20
10,50
10,10
0
10,
50
15,100
15 and 200
15
:
nJobs
nMachines
1
U[(nMachines)/2,(nMachines)
2]
:
Processing time (
kji
p
,,
)
15:
),(
,,,,tjikji
ppdev
1U[m/2,m] :# of Operation
1
If nJobs
50:U[0,40]; Otherwise,
U[0,20]
:
Release Date (
i
r
)
31.2 (tight), 1.5(moderate), 2(loose):
Tightness factor of Due Date(
c
)
1
i
n
j
iji
pcr
1
:
Due Date (
i
d
)
2Variable Constant :Machine distribution
2U[1,4] - U[1, n] 2 - n :
# of machines (
i
L
)
1U[1,3]:Speed of machines
12
:
Number of Scenarios
GA
GP
FJSP-20
GA
GP
GA
GP
GP
GA
GA
GP
GA
GA
GP
GA
GP
GP
GA
GA
GA
GP
GA
GP
FJSP-50
GA
GP
FJSP-100
FJSP-30
FJSP-50
FJSP-100
GA
GP
GA
GP
GP
GA
GP
GP
(
MO-FDJSPM
1
)
MO-FDJSPM
GP
no-free lunch
(R=0)
RKGA
RKGA
(R=1,2,3)
(R=0)
Multi-Objective Flexible
Dynamic Job- Shop with
Parallel Machines
Genetic Algorithm (GA)
Parallel Machines (PM)
Flexible Job shop (FJS)
Job shop (JS)
Shortest Processing Time
(SPT)
Simulated Annealing
Flow Shop with Parallel
Machines (FSPM)
Particle Swarm Optimization
Flexible Job Shop with Parallel
Machines (FJSP)
Artificial Intelligent
Flexible Dynamic Job Shop
with Parallel Machines
(FDJSP)
Symbiotic Evolutionary
Algorithm
Position Based Crossover
Fuzzy Approach
Partial Mapped
[1] Kubzin M. A., Strusevich V. A. “
Two-machine flow shop no-wait scheduling with machine maintenance
”, A Quarterly Journal of Operations Research. 3,
2005, pp. 303–313.
[2] Brandimarte P., “
Routing and scheduling in a flexible job shop by taboo search
”, Annals of Operations Research, 41, 1993, pp. 157–183.
[3] Tay J.C., Wibowo D., “
An Effective Chromosome Representation for Evolving Flexible Job-Shop Scheduling
”, Genetic and Evolutionary Computation
Conference, 2004.
[4] Tay J.C., Ho N.B., “
Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems
”, Computer & Industrial
engineering, 2007, Available from <www.elsevier.com
>.
[5] Kim Y.K., Park K., Ko J., “
A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling
”, Computers & Operations
Research, 30, 2004, pp. 1151–1171.
[6] Gao J., Gen M., Sun L. “
Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm
”, Journal of Intelligent Manufacturing. 17,
2006, pp. 493–507.
[7] Schmidt G. “
Scheduling on semi-identical processors
”, Zeitschrift fur Operations Research. 28, 1984, pp. 153-162.
[8] Adiri I., Bruno J., Frostig E., Rinnooy-Kan A. H. G. “
Single machine flow-time scheduling with a single breakdown
”, Acta Informatica. 26, 1989, pp. 679-696.
[9] Schmidt G. “
Scheduling with Limited Machine Availability
”, European Journal of Operational Research. 121, 2000, pp. 1–15.
[10] Xie J., Wang X. “
Branch and bound algorithm for flexible flowshop with limited machine availability
”, Asian Information Science Life. 1, 2002, pp. 241-
248.
[11] Cheng T. C. E., Liu Z. “
Approximability of Two-Machine Flow shop Scheduling with Availability Constraints
”, Operations Research Letters. 31, 2003, pp.
319–322.
[12] Xie J., Wang X. “
Complexity and algorithms for two-stage flexible flow shop scheduling with availability constraints
”, Computers and Mathematics with
Applications. 50, 2005, pp. 1629-1638.
[13] Wu C. C., Lee W. C. “
A note on single-machine scheduling with learning effect and an availability constraint
”, Int. J. Advance Manufacturing Technology, 2006.
[14] Zribi N., Kamel A. E., Borne P. “
Minimizing the makespan for the MPM job-shop with availability constraints
”, Int. Journal of Production Economics, ,
2007, pp. 1-10.
[15] Breit J. “
Improved approximation for non-preemptive single machine flow-time scheduling with an availability constraint
”, European Journal of Operational
Research, , 2006, pp. 1-9 .
[16] Zegordi, H., Rahimi, M., “
Job Shop Scheduling With Maintenance Constraint
”, Journal of Sharif, 46, pp. 131-137, 2010.
[17] Bruker, P., Jurisch, B., Sievers, B., “
Discrete Applied Mathematics
”, 49, 1994, pp. 107-127.
[18] Carlier, J., Pinson, E.,
Management Science
, 35, 1989, pp. 164-176.
[19] Gen, M., Cheng, R., “
Genetic Algorithms and Engineering Design
”, John Wiley & Sons, 1997.
[20] Brandimarte P. “
Theory and Methodology, Exploiting process plan flexibility in production scheduling: A multi-objective approach
”, European Journal of
Operational Research, 114, 1999, pp. 59-71.
[21] Ghedjati, F., “
Genetic algorithms for the job-shop scheduling problem with unrelated parallel constraints: heuristic mixing method machines and
precedence
”, Computer & Industrial Engineering, 37, 1999, pp. 39-42.
[22] Kacem, I., Hammadi, S., Borne, P., “
Approach by localization and multi objective evolutionary optimization for Flexible Job-Shap scheduling problems
”,
IEEE Transaction on Systems, Man and Cybernetics, 32(1), 2002, pp. 1–13.
[23] Lee, Y.H., Jeong, C.S., Moon, C., “
Advanced planning and scheduling with outsourcing in manufacturing supply chain
”, Computer & Industrial
Engineering, 43, 2002, pp. 351-374.
[24] Gen M., Cheng R. “
Genetic Algorithm in Search, Optimization and Machine Learning
”, Addition-Wesley, Reading, MA, 2004.
[25] Kurz, M.E., Askin, R.G., “
Scheduling flexible flow-lines with sequence-dependent setup times
”, European Journal of Operational Research, 159, 2004, pp. 66-82.
[26] Kurz, M.E., Askin, R.G., “
Comparing scheduling rules for flexible flow-lines
”, Int. J. Production Economics, 85, 2003, pp. 371-388.
[27] Ho, N.B., Tay, J.C., Lai, E. “
An effective architecture for learning and evolving Flexible Job Shop schedules
”, European Journal of Operational Research,
179, 2007, pp. 316–333.
[28] Reeves Coline R., “
Modern Heuristic Techniques for Combinatorial Problems
”, John Wiley & Sons, 1993.
[29] Dagli, C.H., Sittisathanchai, S., “
Genetic neurons-scheduler: a new approach for job shop scheduling
”, International Journal of Production Economics, 41,
1995, pp. 135-145.
[30] Abbasian, M., Nahavandi, N., “
Minimization Flow Time in a Flexible Dynamic Job Shop with Parallel Machines
”, Tehran, Tarbiat Modares University,
Engineering Department of Industrial Engineering, Master of Science Thesis, 2009.
[31] Abbasian, M., Nahavandi, N., “
Minimization Flow Time in a Flexible Dynamic Job Shop with Parallel Machines
”, Journal of Sharif, in press, 2010.
[32] Amiri, M., Jamshidi, S.F., Sadeghiani J.S. “
A Genetic Algorithm Approach for Statistical Multi-Response Models Optimization: A Case Study
”, Journal of
Science & Technology, 49, pp. 131-137, 2009, Available from <www.google.com
>.
[33] Moreno-Torres, J.G., Llora, X., Goldberg D.E. “
Binary Representation in Gene Expression Programming: Towards a Better Scalability
”, IlliGAL Report
No. 2009003, 2009, Available from <www.google.com
>.
[34] Abbasian, M., Nahavandi, N., “
Solving Multi-Objective Flexible Dynamic Job-Shop Scheduling Problem with Parallel Machines
”, International Journal of
Industrial Engineering of Production Research, in press, 2010.
[35] Verma, A., Llora, X., Venkataraman, S., Goldberg, D.E., Campbell R.H. “
Scaling eCGA Model Building via Data-Intensive Computing
”, IlliGAL Report
No. 2010001, 2010.
[36] Jansen K. “
APPROXIMATION ALGORITHMS FOR FLEXIBLE JOB SHOP PROBLEMS
", International Journal of Foundations of Computer Science, 2001.
[37] Low, C., “
Simulated annealing heuristic for flow shop scheduling problem with unrelated parallel machines
”, Computer & operation Research, 32, 2005,
pp. 2013-2025.