Parallel Cooperative Evolutionary Local Search for the Heterogeneous Vehicle Routing Problem
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Transcript of Parallel Cooperative Evolutionary Local Search for the Heterogeneous Vehicle Routing Problem
Parallel Cooperative Evolutionary Local Search
for the Heterogeneous Vehicle Routing Problem
EU/MEeting – 3/4 June 2010
P. Lacomme, C. Prodhon
Université de Clermont-Ferrand II, LIMOS, FranceUniversité de Technologie de Troyes, LOSI, France
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Parallel metaheuristics
Technical choices
Parallel Cooperative Evolutionary Local Search
Heterogeneous Vehicle Routing Problem
Experimentations
Sommaire
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Parallel metaheuristics
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Some publicationsParallel GRASP with path-relinking for job shop scheduling R.M. Aiex, S. Binato, and M.G.C. ResendeParallel Computing, 29:393-430, 2003.
Uma investigação experimental da distribuição de probabilidade de tempo de soluçãoem heurísticas GRASP e sua aplicação na análise de implementações paralelas R.M. AiexPhD thesis, Department of Computer Science, Catholic University of Rio de Janeiro, Rio deJaneiro, Brazil, 2002.
Parallelization strategies for the metaheuristic GRASP A.C.F. AlvimMaster's thesis, Department of Computer Science, Catholic University of Rio de Janeiro, Rio deJaneiro, RJ 22453-900 Brazil, April 1998.
Load balancing in the parallelization of the metaheuristic GRASP A.C.F. Alvim and C.C. RibeiroTechnical report, Department of Computer Science, Catholic University of Rio de Janeiro, Rio de Janeiro,
RJ22453-900 Brazil, 1998.
Parallel strategies for GRASP with path-relinking R.M. Aiex and M.G.C. ResendeTechnical report, Internet and Network Systems Research Center, AT&T Labs Research, Florham Park, NJ,
2003.
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Comments
Parallel Tabou Parallel Grasp Parallel Genetic algorithm etc…
No parallel metaheuristic provides the best published results
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Technical choices
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Technical choices (1/2)
Threads programming
Take advantages of multi-cores
Manual management of common resources
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Technical choices (2/2)
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Parallel Cooperative Evolutionary Local
Search
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Classical optimization scheme
Determine a QDRS
A quasi-direct representation of solution (QDRS)
A solution S.
Improved solution S’fA quasi-direct
representation of solution (QDRS)
Heuristics dedicated to the
problem
A solution S.
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A quasi-direct representation of solution (QDRS)
Initial set of QDRS
Initialization of the framework
Diversification Process
Local Search(LS)
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Improvement of solution Framework iterations
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Routing problem : 2 solution spaces
Split
Concat
metaheuristic search space A routing solution
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Proposition
Solution
Random Sampling
Evolutionary Local Search
np GRASP iterations
Greedy randomized heuristic
Local Search
Solution S
Mutation on hubs
Solution Solution Solution
Mutation
Local Search
Solution
Mutation
Local Search
Solution
Mutation
Local Search
Solution
ni ELS iterations
nc children-solutions
S replaced by best child in case of improvement
Selection
n ELS parallel
Synchronization of the n ELS
Restart with the best commun solution from the n ELS
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ExampleS0 : 10 324
Best sur 10 : 10 075
Best sur 10 : 9 633
Best sur 10 : 9 607
Best sur 10 : 9 545
Best sur 10 : 9 546
Best sur 10 : 9 884
Best sur 10 : 9 606
Best sur 10 : 9 536
Best sur 10 : 9 630
Best sur 10 : 9 636
Best sur 10 : 9 764
Best sur 10 : 9 696
Best sur 10 : 9 687
Best sur 10 : 9 618
Best sur 10 : 9 608
Best sur 10 : 9 759
Best sur 10 : 9 592
Best sur 10 : 9 592
Best sur 10 : 9 393
Best sur 10 : 9 287
Processeur 1 Processeur 2 Processeur 3 Processeur 4
Pour chaque processeur, on garde les 10 meilleurs
Les 4 meilleures solutions : 9287 (processeur 4), 9330 (P4), 9336 (P4), ??? (P??)
Best sur 10 : 9 881
Best sur 10 : 9 515
Best sur 10 : 9 390
Best sur 10 : 9 389
Best sur 10 : 9 386
Processeur 1Depart depuis:
9287Depart depuis:
9330
Best sur 10 : 9 881
Best sur 10 : 9 515
Best sur 10 : 9 390
Best sur 10 : 9 389
Best sur 10 : 9 386
Etc... Etc...
Depart depuis: 9336
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Numerical tests VFMP … HVRP
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HVRP (1/3)
VRP + heterogeneous fleet of vehicles A depot : node 0 n nodes (clients)
dj demands on node j
Cij cost from node i to j
Fleet of K vehicle types For each type K of vehicles nk vehicles For each type K of vehicles Qk vehicle capacity
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Published Results– HVRP
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New instances
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Presentation (1/5)
http://www.isima.fr/~lacomme/students.html
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Presentation (2/5)
96 French districts
From 20 to 300 nodes Non euclidien distances 8 vehicles types
4 subset of instances < 100 nodes DLP_HVRP_1 From 100 to 150 nodes DLP_HVRP_2 From 150 to 200 nodes DLP_HVRP_3 + 200 nodes DLP_HVRP_4
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Auvergne….
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Aube…
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Nightmare instances
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GRASPxELS solutions (2/2)
Solutions from 5 to 35 trips
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Machine de test
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Machine (1/2)
Windows Server 2003
8 processors
1 processor 4 cores
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Machine (2/2)
4 threads communication time = nul 8 threads slowdown factor = 2 16 threads slowdown factor = 4 32 threads slowdown factor = 8
BUS
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Numerical Results
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Small instances (1/3)
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Small instances (2/3)
Comparative study between total time and best time
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1P 2P 4P 8P 16P
Best time
Total time
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Small instances (3/3)
% of best solutions
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1P 2P 4P 8P 16P
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Medium Scale Instances (1/2)
Comparative study between the total time and the best time
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1P 2P 4P 8P 16P
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Medium Scale Instances (2/2)
% of best solutions
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1P 2P 4P 8P 16P
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Concluding remarks
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Conclusion
Significant impact of hardware
Parallel metaheuristic proves its capacity to provide high quality results
Increase convergence rate
Increase solution quality