Title: Using improved genetic algorithm under uncertain circumstance of site selection of O2O customer returns
Authors: Hongying Sun; Yu Tian
Addresses: School of Business, Sun Yat-sen University, Guangzhou, 510275, China; College of Computational Science, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China ' School of Business, Sun Yat-sen University, Guangzhou, 510275, China; School of Business, Jishou University, Jishou, 416000, China
Abstract: Online-to-offline (O2O) e-commerce supports online purchase and offline servicing. In recent years, with the growth of online shopping in China, O2O has become a new popular mode of e-commerce appliance. Buying online and returning offline are becoming a dominant shopping mode. The returns of customer should be collected to be treated in a more cost-efficient manner. To this end, this paper aims to propose an integer programming model to minimise the cost in construction couple with operating charges by optimising the sites of reverse logistics with the customer returns. For lowering storage costs, physical stores and their geographical sites should be far away from the residential area. In addition, this paper designs an improved genetic algorithm for solving two-stage heredity under random circumstance in that this model builds up multilayer reverse logistics network for recycling customer returns. Both the simulation and numerical examples prove the effectiveness and feasibility of this improved genetic algorithm.
Keywords: reverse logistics; site selection; improved genetic algorithm; O2O e-commerce.
International Journal of Data Analysis Techniques and Strategies, 2018 Vol.10 No.3, pp.241 - 256
Received: 28 Apr 2016
Accepted: 17 Dec 2016
Published online: 01 Aug 2018 *