A hybrid multiobjective evolutionary algorithm for solving truck and trailer vehicle routing problems

This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability and multimodal combinatorial optimization problem, a hybrid multiobjective evolutionary algorithm (HMOEA) is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multiobjective optimization results. The computational results have shown that the HMOEA is effective for solving multiobjective combinatorial problems, such as finding useful trade-off solutions for the TTVRP.

[1]  William J. Cook,et al.  Solution of a Min-Max Vehicle Routing Problem , 2002, INFORMS Journal on Computing.

[2]  Gilbert Laporte,et al.  A unified tabu search heuristic for vehicle routing problems with time windows , 2001, J. Oper. Res. Soc..

[3]  Jörg Homberger,et al.  A parallel two-phase metaheuristic for routing problems with time windows , 2001 .

[4]  I-Ming Chao,et al.  A tabu search method for the truck and trailer routing problem , 2002, Comput. Oper. Res..

[5]  Andrzej Jaszkiewicz,et al.  Do multiple-objective metaheuristics deliver on their promises? A computational experiment on the set-covering problem , 2003, IEEE Trans. Evol. Comput..

[6]  M. Dror.  Arc Routing : Theory, Solutions and Applications , 2000 .

[7]  Alex Van Breedam,et al.  A parametric analysis of heuristics for the vehicle routing problem with side-constraints , 2002, Eur. J. Oper. Res..

[8]  Walter Ukovich,et al.  A comparison of different solution approaches to the vehicle scheduling problem in a practical case , 2000, Comput. Oper. Res..

[9]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[10]  Gang Yu,et al.  A Branch-and-Cut Procedure for the Vehicle Routing Problem with Time Windows , 2002, Transp. Sci..

[11]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[12]  Edmund K. Burke,et al.  A multistage evolutionary algorithm for the timetable problem , 1999, IEEE Trans. Evol. Comput..

[13]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[14]  Jean Berger,et al.  A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows , 1998, Canadian Conference on AI.

[15]  Michel Gendreau,et al.  METAHEURISTICS FOR THE VEHICLE ROUTING PROBLEM. , 1994 .

[16]  Paolo Toth,et al.  The Vehicle Routing Problem , 2002, SIAM monographs on discrete mathematics and applications.

[17]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[18]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[19]  Patrick Prosser,et al.  A Comparison of Traditional and Constraint-based Heuristic Methods on Vehicle Routing Problems with Side Constraints , 2000, Constraints.

[20]  Tong Heng Lee,et al.  Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization , 2001, IEEE Trans. Evol. Comput..

[21]  Loo Hay Lee,et al.  Vehicle capacity planning system: a case study on vehicle routing problem with time windows , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[22]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[23]  Susana Cecilia Esquivel.  Evolutionary algorithms for solving multi-objetive problems . Carlos A. Coello Coello, David A. van Veldhuizen and Gary R., Lamont , 2002 .

[24]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

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

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

[27]  Michel Gendreau,et al.  SINTEF REPORT TITLE Route Construction and Local Search Algorithms for the Vehicle Routing Problem with Time Windows , 2002 .