Location Planning For Express Service Convenience Store

With the emergence of e-commerce in the recent ten years, the express industryhas developed rapidly both in China and abroad. However, the express industry wasthought of as a low-end service for the public due to the fact that the delivery courierswere usually seen riding the electric motorcar to send express everywhere in the city.Therefore, the establishment of express service convenience stores with unifiedstandard and visible corporate logo can not only improve the corporation image, butalso strengthen the recognition by the public. Meanwhile, the express company canprovide additional services such as self-pickup and self-sending, express packagingand so on to increase revenue.The methods to solve facility location are mainly divided into comprehensiveanalysis method and modeling method. By analyzing economy, transportation, demand,costs with the analytic hierarchy process (AHP), the comprehensive analysis methodsynthetically evaluates the candidate location points to select the optimal locationpoints which meet the objectives of the company. The results of comprehensiveanalysis method are usually reasonable, but also time-consuming, labor andresources-demanding. Due to of the involvement of many convenience stores-, weestablished two multi-objective location models based on different assumptions for theconvenience store location problem.In this paper, we divided customer demands into sending demand points andpickup demand points based on the discrete demand, that is, the point-demand. Takingthe market share and location costs as targets, we established a model with thethreshold constraint. Taking the transceiver express data of an express company inXuhui District, Shanghai for a week as the input data,the model was solved bymulti-objective evolutionary algorithm MNSGA-II and NSGA-II, obtaining a group ofnon-dominated Pareto solution sets. According to the four multi-objective optimizationevaluation indexes, these two algorithms were compared and the results showed thatthe MNSGA-II was slightly better than the NSGA-II, but the non-dominated solutionsets of NSGA-II and MNSGA-II were both a good approximation of the Paretooptimal solution set. In the multi-objective location model based flat-demand, roads and water systemswere divided into multiple sub-regions by using ArcGIS, and the densities of thesesub-regions were calculated. Taking the center of each sub-region as the candidatepoints of the convenience store, the total covered demand quantity of each candidatepoints were calculated by considering the blocking factors of road and water systems.Considering the threshold constraint and distance limitation among convenience stores,we established a model taking the market share and location costs as targets. Themodel was solved by the NSGA-II algorithm, achieving good performance in terms ofsolution quality and speed.After the Pareto non-dominated solution set was obtained, Technique for OrderPreference by Similarity to an Ideal Solution (TOPSIS) was applied to get the mostreasonable solution. In addition, according to the decision makers’ own conditions,they can select the appropriate location project from the Pareto non-dominated solutionset.