With the rapid development of e-commerce, urban end distribution plays more and more important role in e-commerce logistics. The collection and delivery points(CDPs), between online retailers and customers,provide a w...With the rapid development of e-commerce, urban end distribution plays more and more important role in e-commerce logistics. The collection and delivery points(CDPs), between online retailers and customers,provide a way to improve the service quality of urban end distribution. But it will be more difficult to obtain an optimal solution of urban end delivery plan when many CDPs joint a complicated delivery network, since the solution space is always too large for many traditional heuristic algorithms to search. In this paper, a two-stage optimization method based on geographic information system(GIS) and improved cooperative particle swarm optimization(CPSO) is proposed. This method takes full advantage of powerful network analysis of GIS and strong global search of CPSO. A new cooperative learning mechanism, global sub-swarm, local sub-swarm and normal sub-swarm(GS-LS-NS), is used to improve the search mode of CPSO. Finally, several experiments are conducted to show the better performance of GIS-CPSO, compared with single PSO, GIS-CPSO and Arc GIS(software of GIS) separately. The conclusion of this research is much useful and applicable for logistics service providers.展开更多
文摘With the rapid development of e-commerce, urban end distribution plays more and more important role in e-commerce logistics. The collection and delivery points(CDPs), between online retailers and customers,provide a way to improve the service quality of urban end distribution. But it will be more difficult to obtain an optimal solution of urban end delivery plan when many CDPs joint a complicated delivery network, since the solution space is always too large for many traditional heuristic algorithms to search. In this paper, a two-stage optimization method based on geographic information system(GIS) and improved cooperative particle swarm optimization(CPSO) is proposed. This method takes full advantage of powerful network analysis of GIS and strong global search of CPSO. A new cooperative learning mechanism, global sub-swarm, local sub-swarm and normal sub-swarm(GS-LS-NS), is used to improve the search mode of CPSO. Finally, several experiments are conducted to show the better performance of GIS-CPSO, compared with single PSO, GIS-CPSO and Arc GIS(software of GIS) separately. The conclusion of this research is much useful and applicable for logistics service providers.
