A messy genetic algorithm for the vehicle routing problem with time window constraints

In vehicle routing problems with time window constraints (VRPTW), a set of vehicles with limited capacity, are to be routed from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. To solve the problem, the optimized assignment of vehicles to each customer is needed as to achieve the minimal total cost without violating the capacity and time window constraints. Combinatorial optimization problems of this kind are NP-hard and are best solved to the near optimum by heuristics. The authors describe their research on a rare class of genetic algorithms, known as the messy genetic algorithms (mGA) in solving the VRPTW problem. The mGA has the merit of directly realizing the relational search needed in VRPTW representation, which cannot be easily realized using simple heuristic methods. The mGA was applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances, and yielded 23 solutions better than or equivalent to the best solutions ever published in literature.

[1]  Jean-Yves Potvin,et al.  The Vehicle Routing Problem with Time Windows Part I: Tabu Search , 1996, INFORMS J. Comput..

[2]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..

[3]  Jörg Homberger,et al.  Two Evolutionary Metaheuristics For The Vehicle Routing Problem With Time Windows , 1999 .

[4]  Wen-Chyuan Chiang,et al.  Simulated annealing metaheuristics for the vehicle routing problem with time windows , 1996, Ann. Oper. Res..

[5]  Michel Gendreau,et al.  A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows , 1997, Transp. Sci..

[6]  J Potvin,et al.  A tabu search heuristic for the vehicle routing problem with time windows , 1992 .

[7]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[8]  Sam R. Thangiah,et al.  Vehicle Routing and Time Deadlines Using Genetic and Local Algorithms , 1993, ICGA.

[9]  K. Deb.  Binary and floating-point function optimization using messy genetic algorithms , 1991 .

[10]  Tong Sun,et al.  Algorithms for the vehicle routing problems with time deadlines , 1993 .

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Yves Rochat,et al.  Probabilistic diversification and intensification in local search for vehicle routing , 1995, J. Heuristics.

[13]  Samy Bengio,et al.  The Vehicle Routing Problem with Time Windows Part II: Genetic Search , 1996, INFORMS J. Comput..

[14]  Martin Desrochers,et al.  A New Optimization Algorithm for the Vehicle Routing Problem with Time Windows , 1990, Oper. Res..

[15]  Kendall E. Nygard,et al.  School bus routing using genetic algorithms , 1992, Defense, Security, and Sensing.

[16]  Eric Taillard,et al.  A NEW NEIGHBORHOOD STRUCTURE FOR THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS. , 1995 .

[17]  Ibrahim H. Osman,et al.  Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem , 1993, Ann. Oper. Res..

[18]  Gilbert Laporte,et al.  A Tabu Search Heuristic for the Vehicle Routing Problem , 1991 .

[19]  Arnoldo C. Hax,et al.  Production and inventory management , 1983 .

[20]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[21]  Andrew Dymek.  An Examination of Hypercube Implementations of Genetic Algorithms , 1992 .

[22]  Jean-Yves Potvin,et al.  Heuristic approaches to vehicle routing with backhauls and time windows , 1996, Comput. Oper. Res..

[23]  Wen-Chyuan Chiang,et al.  A Reactive Tabu Search Metaheuristic for the Vehicle Routing Problem with Time Windows , 1997, INFORMS J. Comput..