The Aplication Of Improved Particle Swarm Optimization Algorithm To Commercial Retail Net Location

With the rapid development of Chinese economy, urbanization and urban expansionspeed up. And city size, population and industry development in large and medium cities aredifferent, so the layout of the commercial site is also different. The current commercial siteselection not only should improve its development framework but also should pay attention tothe uniqueness of site selection environment, such as outstanding local characteristics and thesite selection of humanities connotation. Site selection and layout are very important parts inenterprise management strategy, and site selection, to a great extent, will affect enterpriseplanning and development. The development of swarm intelligence algorithm provides a newthought and method for the optimal commercial site selection. Based on the analysis of thetraditional commercial site selection model, influencing factors to the model were analyzed toestablish quantitative factors and mathematical relation, and a more comprehensivemathematical model was established with the swarm intelligence algorithm and improvedparticle swarm algorithm.In this paper, from the perspective of influencing factors on commercial site selection ofoutlets, similarities and differences were compared and analyzed between commercial siteselection and other site selections. And influencing factors on commercial site selection andthe mathematical expression of individual factors were established by the questionnaireanalysis and expert consultation. The whole site selection model consists of the analysis ofaffecting factors and the determination of model factors, such as the analysis of demographicfactors. The determination of model factors is mainly based on population, traffic networkconnection degree, objective conditions (urban prosperity degree coefficient), customer value,competition and consumption potential to establish site selection model finally. The goal ofsite selection was achieved by the improved multi-objective particle swarm optimizationalgorithm after calculating the corresponding optimal solution. The paper’s main work andinnovation points are as follows:(1) The basic line of commercial site selection was put forward based on the status quo ofcommercial site selection in China after searching a large number of domestic and foreignrelated research literatures.(2) The similarities and differences were compared and analyzed between for-profitcommercial site selection and non-profit organization, and the final selection model wasestablished after the mathematical expression was achieved by quantitative and qualitativeanalysis.(3) The performance of multi-objective particle swarm optimization algorithm wasgreatly improved by the external concentration variation which was provided by Gaussvariation formula. Based on previous algorithm, the multi-objective particle swarmoptimization algorithm was improved in terms of the initial solution, the objective functionand the parameters such as number of iterations. Theoretical analysis and simulationcomparison of the numerical experiment results verify the feasibility of the algorithm. At last,taking commercial bank site selection in Dingxi, Gansu province as a case, the selectionmethod in this paper was practiced and applied comprehensively to achieve good practicaleffects.The study about commercial site selection can optimize the allocation of resources andreduce the waste of resources caused by the blind layout so that maximum economic benefitsto the enterprise can result in. The field of commercial site selection is very wide, and manyareas have a very good prospect such as logistics delivery points, retail enterprises,supermarkets, bank sites and catering entertainment online.