Dynamic vehicle routing for online B2C delivery

Electronic commerce (EC) is increasingly popular in today's businesses. The business-to-consumer EC environment has voluminous, unpredictable, and dynamically changing customer orders. A major part of the delivery system of this environment is the dynamic vehicle routing (DVR) system. This study investigates several algorithms suitable for solving the DVR problem in business-to-consumer (B2C) EC environment. It designs the solution process into three phases: initial-routes formation, inter-routes improvement, and intra-route improvement. A computer program is created to demonstrate a system simulating vehicle routing process under the online B2C environment. The simulated system collects data for system performance indexes such as simulation time, travel distance, delivery time, and delay time. The results show that when orders are placed through the Internet in an online B2C environment, the Nearest algorithms can be used to find satisfactory routes during the first phase of a DVR delivery system. The three-phase solution process is proven to be significantly better in travel distance and delivery time than the conventional single-phase solution process.

[1]  Adil Baykasoglu,et al.  A simulated annealing algorithm for dynamic layout problem , 2001, Comput. Oper. Res..

[2]  Fred W. Glover,et al.  Tabu Thresholding: Improved Search by Nonmonotonic Trajectories , 1995, INFORMS J. Comput..

[3]  Daniel J. Rosenkrantz,et al.  An Analysis of Several Heuristics for the Traveling Salesman Problem , 1977, SIAM J. Comput..

[4]  Harilaos N. Psaraftis,et al.  Dynamic vehicle routing: Status and prospects , 1995, Ann. Oper. Res..

[5]  G. Laporte The traveling salesman problem: An overview of exact and approximate algorithms , 1992 .

[6]  Michel Gendreau,et al.  Diversion Issues in Real-Time Vehicle Dispatching , 2000, Transp. Sci..

[7]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

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

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

[10]  Bharath S. Vaidyanathan,et al.  A capacitated vehicle routing problem for just-in-time delivery , 1999 .

[11]  Lawrence Bodin,et al.  Classification in vehicle routing and scheduling , 1981, Networks.

[12]  Robert E. Tarjan,et al.  Efficient algorithms for finding minimum spanning trees in undirected and directed graphs , 1986, Comb..

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

[14]  Warren B. Powell,et al.  Stochastic and dynamic networks and routing , 1995 .

[15]  Barrie M. Baker,et al.  A genetic algorithm for the vehicle routing problem , 2003, Comput. Oper. Res..

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

[17]  Robert B. Dial,et al.  Autonomous dial-a-ride transit introductory overview , 1995 .

[18]  Irène Charon,et al.  Application of the noising method to the travelling salesman problem , 2000, Eur. J. Oper. Res..

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

[20]  Jean-Yves Potvin,et al.  A computer assistant for vehicle dispatching with learning capabilities , 1995, Ann. Oper. Res..

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

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

[23]  Alexander H. G. Rinnooy Kan,et al.  Vehicle Routing with Time Windows , 1987, Oper. Res..

[24]  D. J. Rosenkrantz,et al.  Approximate Algorithms for the Traveling Salesperson Problem , 1974, SWAT.

[25]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[26]  Billy E. Gillett,et al.  A Heuristic Algorithm for the Vehicle-Dispatch Problem , 1974, Oper. Res..

[27]  Gilbert Laporte,et al.  The vehicle routing problem: An overview of exact and approximate algorithms , 1992 .

[28]  Dimitris Bertsimas,et al.  Stochastic and Dynamic Vehicle Routing in the Euclidean Plane with Multiple Capacitated Vehicles , 1993, Oper. Res..

[29]  D. Bertsimas,et al.  Worst-case examples for the spacefilling curve heuristic for the Euclidean traveling salesman problem , 1989 .

[30]  Bruce L. Golden,et al.  VEHICLE ROUTING: METHODS AND STUDIES , 1988 .

[31]  Anthony Wren,et al.  Computer Scheduling of Vehicles from One or More Depots to a Number of Delivery Points , 1972 .

[32]  Davor Skrlec,et al.  An efficient implementation of genetic algorithms for constrained vehicle routing problem , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

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

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

[35]  Shen Lin Computer solutions of the traveling salesman problem , 1965 .

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

[37]  M M Solomon,et al.  VEHICLE ROUTING AND SCHEDULING PROBLEMS WITH TIME WINDOW CONSTRAINTS: EFFICIENT IMPLEMENTATIONS OF SOLUTION IMPROVEMENT PROCEDURES , 1988 .

[38]  Marshall L. Fisher,et al.  A generalized assignment heuristic for vehicle routing , 1981, Networks.

[39]  Gülay Barbarosoglu,et al.  A tabu search algorithm for the vehicle routing problem , 1999, Comput. Oper. Res..