Hierarchical Traffic Network for Heuristic Approximation Method of Vehicle Routing Problems

Abstract In actual society, accurate delivery planning that can deal with both large scale customers and dynamically fluctuating traffic conditions are expected. Therefore, such delivery planning needs a high performance calculation method for approximate solutions that calculates many approximate solutions to deal with various delivery conditions in a short time. For this reason, we propose an approximate solution calculation method for vehicle routing problems (VRPs) that obtains a better solution in a shorter time. The proposed method generates an approximate solution by using a hierarchical traffic network composed on the basis of a vehicle's behavior, which is the frequency of using roads. We confirmed that the calculation time of the proposed method depends on the constitution rule of the hierarchical network. In this paper, we describe the composition of a hierarchical network that moves closer to the best approximate solution in a short time.

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

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

[3]  Xin Zhao,et al.  A dynamic vehicle routing problem based on real-time traffic information , 2010 .

[4]  Eiichi Taniguchi,et al.  An Approximation Algorithm for Vehicle Routing Problems with Hierarchized Traffic Network , 2012 .

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

[6]  Christine L. Mumford,et al.  Capacitated vehicle routing: perturbing the landscape to fool an algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[7]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[8]  Giovanni Rinaldi,et al.  Computational results with a branch and cut code for the capacitated vehicle routing problem , 1998 .

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

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

[11]  Éric D. Taillard,et al.  Parallel iterative search methods for vehicle routing problems , 1993, Networks.

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

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

[14]  Teodor Gabriel Crainic,et al.  A cooperative parallel meta-heuristic for the vehicle routing problem with time windows , 2005, Comput. Oper. Res..

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

[16]  Eiichi Taniguchi,et al.  Probabilistic Vehicle Routing and Scheduling with Variable Travel Times , 2000 .

[17]  Yang Dong-yuan,et al.  An improved genetic algorithm for Time Dependent Vehicle Routing Problem , 2010, 2010 2nd International Conference on Future Computer and Communication.

[18]  Takayoshi Yokota,et al.  Constructing Two-layered Freight Traffic Network Model from Truck Probe Data , 2011, Int. J. Intell. Transp. Syst. Res..

[19]  G. Croes.  A Method for Solving Traveling-Salesman Problems , 1958 .

[20]  G. Clarke,et al.  Scheduling of Vehicles from a Central Depot to a Number of Delivery Points , 1964 .