An adaptive evolutionary approach for real-time vehicle routing and dispatching

Computers and Operations Research

Volumn 40, Issue 7, 2013, Pages 1766-1776

  Barkaoui, Mohamed ^a   Gendreau, Michel ^b  

^a UNIVERSITÉ LAVAL   (Canada)

^b INTERUNIVERSITY RESEARCH CENTRE ON ENTERPRISE NETWORKS   (Canada)

Author keywords

Adapting operator settings; Dynamic vehicle routing; Genetic algorithms

Indexed keywords

ADAPTIVE PROCESS; ADAPTIVE VERSIONS; CONVERGENCE PROCESS; DYNAMIC VEHICLE ROUTING; DYNAMIC VEHICLE ROUTING PROBLEMS; EVOLUTIONARY APPROACH; HYBRID GENETIC ALGORITHMS; PERFORMANCE SENSITIVITY;

COMPUTER SCIENCE; OPERATIONS RESEARCH;

GENETIC ALGORITHMS;

EID: 84875929462     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2013.01.022     Document Type: Article

References (49)

1. A.E. Eiben, R. Hinterding, and Z. Michalewicz Parameter control in evolutionary algorithms IEEE Transactions on Evolutionary Computation 3 2 1999 124 141
2. A.E. Eiben, and J.E. Smith Introduction to evolutionary computing 2003 Springer Berlin Heidelberg
3. Ho, CW, Lee KH, Leung KS. A genetic algorithm based on mutation and crossover with adaptive probabilities. In: Proceedings of the 1999 congress, evolutionary computation, 1. Washington, DC, USA. p. 768-775.
4. Julstrom BA. What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm. In: L.J. Eshelman, editor, Proceedings of the sixth international conference on genetic algorithms and their applications. Pittsburgh, PA; 1995, p. 81-87.
5. Julstrom BA. Adaptive operator probabilities in a genetic algorithm that applies three operators. In: Proceedings of the 1997 ACM symposium on applied computing (SAC '97). San Jose, California, USA. p. 233-238.
6. Tuson A, Ross P. Cost based operator rate adaptation: an investigation. In: Proceedings of the 4th international conference on parallel problem solving from nature (PPSN IV). Springer Verlag; 1996. p. 461-469.
7. Hong I, Kahng AB, Moon BR. Exploiting synergies of multiple crossovers: initial studies. In: Proceedings of the second IEEE conference on evolutionary computation. Piscataway, NJ: IEEE Press; 1995. p. 245-250.
8. Spears WM. Adapting crossover in evolutionary algorithms. In: Proceedings of the 4th annuual conference on evolutionary programming. Cambridge, MA, USA; 1995. p. 367-384.
9. H.S. Yoon, and B.R. Moon. An empirical study on the synergy of multiple crossover operators IEEE Transactions on Evolutionary Computation 6 2 2002 212 223
10. Derigs U, Kabath M, Zils M. Adaptive genetic algorithms: a new approach for solving nonstandard vehicle routing problems efficiently. In: Kischka P, Lorenz H-W, Derigs U, Domschke W, Kleinschmidt P, Möhring R, editors. Operations Research Proceedings; 1997, p. 539-544.
11. A. Tuson, and P. Ross Adapting operator settings in genetic algorithms Evolutionary Computation 6 2 1998 84 161
12. W.A. Landgraaf, A.E. Eiben, and V. Nannen Parameter calibration using meta-algorithms IEEE Congress on Evolutionary Computation 2007 71 78
13. Cortez P, Rocha M, Neves J. A meta-genetic algorithm for time series forecasting. In: Torgo L, editor. Proceedings of workshop on artificial intelligence techniques for financial time series analysis (AIFTSA-01), 10th Portuguese conference on artificial intelligence (EPIA'01). Oporto, Portugal; 2001. p. 21-31.
14. J. Grefenstette Optimization of control parameters for genetic algorithms IEEE Transaction on on Systems, Man and Cybernetics 16 1 1986 122 128
15. Lee M, Takagi H. Integrating design stage of fuzzy systems using genetic algorithms. In: Proceedings of the second IEEE international conference on fuzzy systems. vol. 1; 1993. p. 612-617.
16. Nannen V, Eiben AE. Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proceedings of the international joint conference on artificial intelligence (IJCAI); 2007, p. 975-980.
17. Samsonovich, AV, Jong KAD. Pricing the 'Free Lunch' of meta-evolution. In: Beyer H-G, O'Reilly U-M, editors. GECCO 2005. Proceedings of the 2005 conference on genetic and evolutionary computation. New York, NY, USA. p. 1355-1362.
18. K. Shahookar, and P. Mazumder. VLSI cell placement techniques ACM Computing Surveys 23 2 1991 143 220
19. Clune J, Goings S, Punch B, Goodman E. Investigations in Meta-GAs: panaceas or pipe dreams? Proceedings of the genetic and evolutionary computation conference (GECCO). Washington, D.C, USA; 2005, p. 235-241.
20. Wang G, Dexter T, Goodman E, Punch W. Optimization of a GA and within the GA for a 2-dimensional layout problem. In: Proceedings of the first international conference on evolutionary computation and its applications. Presidium, Russian Academy of Sciences; 1996. p. 18-29
21. G. Wang, E. Goodman, and W. Punch On the optimization of a class of blackbox optimization algorithms. Technical report, GARAGe/Case Center 1997 Michigan State University USA
22. J.E. Smith Co-evolving memetic algorithms: a review and progress report IEEE Transactions in Systems, Man and Cybernetics, Part B 37 1 2007 6 17
23. Takashima E, Murata Y, Shibata N, Ito M. Self adaptive island GA. In: Proceedings of the 2003 congress on evolutionary computation (CEC 2003). p. 1072-1079.
24. Fialho A, Da Costa L, Schoenauer M, Sebag M. Extreme value based adaptive operator selection. In: Proceedings of the 10th international conference on Parallel Problem Solving from Nature. Berlin, Heidelberg: Springer-Verlag; 2008, p. 175-184.
25. Fialho A, Da Costa L, Schoenauer M, Sebag M. Dynamic multi-armed bandits and extreme value-based rewards for adaptive operator selection. In: Procceeding of learning and intelligent optimization (LION-3). Springer-Verlag; 2009.
26. D. Thierens Adaptive strategies for operator allocation Studies in Computational Intelligence (SCI) 54 2007 Springer-Verlag Berlin Heidelberg [p.77-90]
27. Murata, T, Ishibuchi H. Positive and negative combination effects of crossover and mutation operators in sequencing problems. In: Proceedings of the IEEE international conference on evolutionary computation. Nagoya, Japan; 1996. p. 170-175.
28. F. Herrera, and M. Lozano Adaptive genetic operators based on coevolution with fuzzy behaviors IEEE Transactions on Evolutionary Computation 5 2 2001 149 165
29. M. Oltean Evolving evolutionary algorithms using linear genetic programming Evolutionary Computation 13 2005 MIT Press MA, USA [p. 387-410]
30. DeJong KA. Parameter Setting in EAs: A 30 Year Perspective. Parameter Setting in Evolutionary Algorithms. Heidelberg; 2007. p. 1-18.
31. M. Gendreau, F. Guertin, J.-Y. Potvin, and É.D. Taillard Parallel tabu search for real-time vehicle routing and dispatching Transportation Science 33 4 1999 381 390
32. J. Berger, and M. Barkaoui. A parallel hybrid genetic algorithm for the vehicle routing problem with time windows Computers & Operations Research 31 Issue 12 2004 2037 2053
33. Gendreau, M Potvin J-Y. editors. Transportation Science, volume 4. INFORMS. Special issue on real-time Weet management; 2004.
34. G. Ghiani, F. Guerriero, G. Laporte, and R. Musmanno Real-time vehicle routing: solution concepts, algorithms and parallel computing strategies European Journal of Operational Research 151 1 2003 1 11
35. S. Ichoua, M. Gendreau, and J.-Y. Potvin Planned route optimization for real-time vehicle routing V. Zeimpekis, C.D. Tarantilis, G.M. Giaglis, I. Minis, Dynamic Fleet Management, Volume 38 of Operations Research/Computer Science Interfaces 2007 Springer US 1 18
36. Larsen A, Madsen OBG, Solomon MM. Recent developments in dynamic vehicle routing systems. In: Golden BL, Raghavan S, Wasil EA, editors. The vehicle routing problem: latest advances and new challenges. Springer; 2008. p. 199-218.
37. Pillac V, Gendreau M, Guéret C, Medaglia AL. A review of dynamic vehicle routing problems. European Journal of Operational Research 2013;225(1):1-11.
38. V. Zeimpekis, C.D. Tarantilis, G.M. Giaglis, and I. Minis Dynamic fleet management, volume 38 of operations research computer science interfaces series 2007 Springer US
39. Pillac V, Gendreau M, Guéret C, Medaglia A. A review of dynamic vehicle routing problems. Technical Report CIRRELT-2011-62, Centre interuniversitaire de recherche sur les réseaux d'enterprise, la logistique et le transport (CIRRELT). Montreal, Canada; 2011.
40. Schaffer DJ, Caruana RA, Eshelmna LJ, Das R. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the third international conference on genetic algorithms. San Mateo, CA; 1989. p. 51-60.
41. D.E. Goldberg Genetic algorithms in search, optimization, and machine learning 1989 Addison-Wesley New York, USA
42. DeJong KA. Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, Dept. Computer and Communication Sciences: Univ. of Michigan, MI, USA; 1975.
43. F.-H. Liu, and S.-Y. Shen A route-neighborhood-based metaheuristic for vehicle routing problem with time windows European Journal of Operational Research 118 1999 485 504
44. M.M. Solomon Algorithms for the vehicle routing and scheduling problems with time window constraints Operations Research 35 2 1987 254 265
45. Berger J, Barkaoui M. An improved hybrid genetic algorithm for the vehicle routing problem with time windows. In: Proceeding of the international ICSC symposium on computational intelligence, part of the international ICSC congress on intelligent systems and applications. University of Wollongong, Wollongong: Australia; 2000.
46. P. Shaw Using constraint programming and local search methods to solve vehicle routing problems Principles and Practice of Constraint Programming 1998 Springer-Verlag New York, USA [p. 417-431]
47. Harvey WD, Ginsberg ML. Limited discrepancy search. In: Proceedings of the 14th international joint conference on artificial intelligence. Montreal, Canada; 1995.
48. I.H. Osman Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem Annals of Operations Research 41 1993 421 451
49. M Wall GAlib - A C++ Genetic Algorithms Library. Version 2.4 1995 MIT Boston, USA