A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows

A variety of hybrid genetic algorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of the intermediate solutions computed during the evolutionary process to explore the solution space. This paper presents a new hybrid genetic algorithm for VRPTW. It investigates the impact of using explicitly domain knowledge and a priori knowledge/characteristics about expected solutions during the recombination and mutation phases of the algorithm. Basic principles borrow from recent hybrid and standard genetic algorithms, and features of well-known heuristics to drive the search process. Designed to support time-constrained reasoning tasks, the procedure is intended to be conceptually simple, easy to implement and allow fast computation of near-optimal solution. A computational experiment has been conducted to compare the performance of the proposed algorithm with similar and standard techniques.

[1]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

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

[3]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

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

[5]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[6]  Jacques Desrosiers,et al.  Survey Paper - Time Window Constrained Routing and Scheduling Problems , 1988, Transp. Sci..

[7]  Jacques Desrosiers,et al.  Time Constrained Routing and Scheduling , 1992 .

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

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

[10]  Michel Gendreau,et al.  A PARALLEL TABU SEARCH HEURISTIC FOR THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS , 1997 .

[11]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[12]  Jacques Desrosiers,et al.  Time Window Constrained Routing and Scheduling Problems: a Survey , 1987 .

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

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Sam R. Thangiah,et al.  An Adaptive Clustering Method Using a Geometric Shape for Vehicle Routing Problems with Time Windows , 1995, ICGA.

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

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

[18]  Roger L. Wainwright,et al.  Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms , 1993, ICGA.

[19]  L. Bodin.  ROUTING AND SCHEDULING OF VEHICLES AND CREWS–THE STATE OF THE ART , 1983 .

[20]  Samuel J. Raff,et al.  Routing and scheduling of vehicles and crews : The state of the art , 1983, Comput. Oper. Res..