Parallel Cooperative Evolutionary Local Search for the Heterogeneous Vehicle Routing Problem

1. 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, France Université de Technologie de Troyes, LOSI, France

2. Sommaire • Parallel metaheuristics • Technical choices • Parallel Cooperative Evolutionary Local Search • Heterogeneous Vehicle Routing Problem • Experimentations

3. Parallel metaheuristics

4. Some publications Parallel GRASP with path-relinking for job shop scheduling R.M. Aiex, S. Binato, and M.G.C. Resende Parallel Computing, 29:393-430, 2003. Uma investigação experimental da distribuição de probabilidade de tempo de solução em heurísticas GRASP e sua aplicação na análise de implementações paralelas R.M. Aiex PhD thesis, Department of Computer Science, Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2002. Parallelization strategies for the metaheuristic GRASP A.C.F. Alvim Master's thesis, Department of Computer Science, Catholic University of Rio de Janeiro, Rio de Janeiro, RJ 22453-900 Brazil, April 1998. Load balancing in the parallelization of the metaheuristic GRASP A.C.F. Alvim and C.C. Ribeiro Technical report, Department of Computer Science, Catholic University of Rio de Janeiro, Rio de Janeiro, RJ 22453-900 Brazil, 1998. Parallel strategies for GRASP with path-relinking R.M. Aiex and M.G.C. Resende Technical report, Internet and Network Systems Research Center, AT&T Labs Research, Florham Park, NJ, 2003.

5. Comments • Parallel Tabou • Parallel Grasp • Parallel Genetic algorithm etc… • No parallel metaheuristic provides the best published results

6. Technical choices

7. Technical choices (1/2) • Threads programming • Take advantages of multi-cores • Manual management of common resources

8. Technical choices (2/2)

9. Parallel Cooperative Evolutionary Local Search

10. Classical optimization scheme

11. Routing problem : 2 solution spaces

12. Proposition • n ELS parallel • Synchronization of the n ELS • Restart with the best commun solution from the n ELS

13. Example

14. Numerical tests VFMP … HVRP

15. 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

16. Published Results– HVRP

17. New instances

18. Presentation (1/5) http://www.isima.fr/~lacomme/students.html

19. 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

20. Auvergne….

21. Aube…

22. Nightmare instances

23. GRASPxELS solutions (2/2) • Solutions from 5 to 35 trips

24. Machine de test

25. Machine (1/2) • Windows Server 2003 • 8 processors • 1 processor  4 cores

26. Machine (2/2) • 4 threads  communication time = nul • 8 threads  slowdown factor = 2 • 16 threads  slowdown factor = 4 • 32 threads  slowdown factor = 8 BUS

27. Numerical Results

28. Small instances (1/3)

29. Small instances (2/3)

30. Small instances (3/3)

31. Medium Scale Instances (1/2)

32. Medium Scale Instances (2/2)

33. Concluding remarks

34. Conclusion • Significant impact of hardware • Parallel metaheuristic proves its capacity to provide high quality results • Increase convergence rate • Increase solution quality