基于多目标粒子群算法的连锁企业配送中心选址研究

发布时间：2018-08-05 16:57

【摘要】：伴随着社会经济的不断发展,连锁企业快速的发展了起来。配送中心作为连锁企业物流系统的核心环节,其选址不仅直接影响企业的整体经济效益,而且逐渐成为连锁企业核心竞争力的关键。本文以连锁超市为例,从企业的物流总成本,基于配送时间的服务质量以及相关的其它影响因素出发,采取理论与实例相结合的方式对配送中心选址问题进行了研究。首先,本文介绍了论文的研究背景、意义、国内外研究的现状和文章的整体研究路线。接着介绍了选址相关理论基础,其中包括配送中心的概念、类型、选址原则及步骤以及选址的相关影响因素。其次,本文根据提出的问题,兼顾物流总成本最小得到目标函数(?)和配送服务质量最佳得到目标函数(?),建立了带有约束条件的多目标选址数学模型,并介绍了几种经典的求解多目标优化模型的方法,提出采用改进后的多目标粒子群算法求解该选址模型。再次,介绍基本粒子群算法和多目标粒子群算法,并将多目标粒子群算法进行改进运用到多目标选址模型中求解带约束条件的多目标选址问题。最后,将第三章建立的多目标选址模型具体运用到连锁超市配送中心选址案例中。首先利用Lingo软件分别优化两个目标函数得到模型的两种选址方案,然后根据第四章提出的改进的多目标粒子群算法,利用Matlab软件进行计算,得到模型的一组有效非劣解,再利用AHP从非劣解中选出一个满意的选址方案,最后根据目标函数值的不同比较备选方案的优劣,确定最终选址方案。此模型可以为连锁企业物流配送中心的选址提供更加全面、可靠的决策指导。
[Abstract]:With the continuous development of social economy, chain enterprises have developed rapidly. Distribution center as the core link of chain enterprise logistics system, its location not only directly affects the overall economic benefits of the enterprise, but also gradually becomes the key to the core competitiveness of chain enterprise. Taking supermarket chain as an example, this paper studies the location problem of distribution center based on the total cost of logistics, service quality of distribution time and other related factors. First of all, this paper introduces the research background, significance, domestic and foreign research status and the overall research route. Then it introduces the theoretical basis of location selection, including the concept of distribution center, types, location principles and procedures, as well as the related factors of location. Secondly, according to the problems raised, the objective function (?) is obtained by taking account of the minimum total cost of logistics. And the objective function (?) of the best quality of distribution service is obtained, and the mathematical model of multi-objective location with constraints is established, and several classical methods to solve the multi-objective optimization model are introduced. An improved multi-objective particle swarm optimization algorithm is proposed to solve the location model. Thirdly, the basic particle swarm optimization algorithm and the multi-objective particle swarm optimization algorithm are introduced, and the multi-objective particle swarm optimization algorithm is improved to solve the multi-objective location problem with constraints in the multi-objective location model. Finally, the multi-objective location model established in chapter 3 is applied to the case of chain supermarket distribution center location. In this paper, we first use Lingo software to optimize the two objective functions to obtain two site-selection schemes of the model, and then, according to the improved multi-objective particle swarm optimization algorithm proposed in Chapter 4, we use Matlab software to calculate, and obtain a set of effective non-inferior solutions of the model. Then AHP is used to select a satisfactory site selection scheme from the non-inferior solution. Finally, according to the advantages and disadvantages of different alternatives of objective function value, the final site selection scheme is determined. This model can provide more comprehensive and reliable decision guidance for the location of logistics distribution center in chain enterprises.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;F252;F721

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