A hybrid GA approach for solving the Dynamic Vehicle Routing Problem with Time Windows

The dynamic vehicle routing problem is an extension of conventional routing problems whose particularities are that information can change after initial routes have been constructed or that not all the information is known when the routing process takes place. The main interest of this type of problem is that it corresponds to many real word applications (repair services, courier mail services, taxi cab services). In this paper, we study the particular case of the dynamic vehicle routing problem with time windows (DVRPTW) in which occurrences of new customers appear over time. We propose an original resolution approach based on a genetic algorithm adapted to this dynamic optimisation context. Taguchi's tables have been used in order to adjust the parameters of our genetic algorithm. Experimental results based on the modified Solomon benchmarks show the efficacy of our approach as compared to other meta-heuristic approaches

[1]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[2]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[3]  Michel Gendreau,et al.  Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching , 1999, Transp. Sci..

[4]  Allan Larsen,et al.  The Dynamic Vehicle Routing Problem , 2000 .

[5]  Jorge Tavares.  Crossover and Diversity : A Study about GVR , 2003 .

[6]  Jean-Yves Potvin,et al.  Genetic Algorithms for the Traveling Salesman Problem , 2005 .

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

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

[9]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[10]  Klaus Hinkelmann,et al.  Design and Analysis of Experiments: Advanced Experimental Design , 2005 .

[11]  Kalyanmoy Deb,et al.  Understanding Interactions among Genetic Algorithm Parameters , 1998, FOGA.

[12]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[13]  Li Xiang-yang.  Genetic algorithm for VRP , 2004 .

[14]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.