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    摘要本论文是就零部件供应商选址规划的模型进行研究的。供应商若要不断提高自身的竞争优势而不被市场所淘汰,就要持续不断地在各个方面做出努力。而选址作为企业得以成立和经营前的一个很关键且必不可少的环节,它的选择合适与否也将会影响到企业能否在行业中占有一席之地。就目前来讲,选址的方法可大致上分为数学模型法和综合评价法两大类。但是由于综合评价法的主观因素比较重,在使用这种方法时容易按照自己的经验和观点来看待问题,所以由此得到的结果的可信度并不是很高。数学模型法是从费用和成本的角度来考虑选址问题,并以最小成本为目标,然后设定目标函数和约束条件,最后建立合适的数学规划模型。本文通过采用双层规划模型的方法,综合考虑供应商的选址成本和配送成本,最后得出最优的选址方案和运输路线。68329

    毕业论文关键词  零部件供应商  选址  双层规划模型  遗传算法

    毕业设计说明书(论文)外文摘要

    Title Study on the planning model for location of parts suppliers

    Abstract

    This paper is to research the model of the location of parts suppliers.

    Location play a pre-critical and essential role in a business's building and operation.If the parts suppliers want to keep their competitive  without being eliminated by the market ,they must make efforts in all aspects.As a critical and essential part before a business established, choosing a right location is very important to supplier.  For now,there are two methods to solve the location problem,which is comprehensive evaluation method and mathematical model law.There are heavy subjective factors occurring in the comprehensive evaluation method .Because people is easy to follow their own experiences and perspectives to look at the problem when using this method.So the reliability of results causing by this method is not very   high.Considering from the perspective of fees and costs,the mathematical model law with a goal of minimize costs first set the objective function and constraints and then establish an appropriate mathematical programming model.This paper considers both the suppliers's location costs and distribution costs and then came to a conclusion about the optimal sitting programs and transportation routes by using bi-level  programming  model.

    Keywords    parts suppliers   location   bi-level programming model   

    single genetic

    目   次

    目   次 I

    1 绪论 1

    1.1 研究背景 1

    1.2 选址的研究现状 2

    1.3 选址的研究现状 2

    2 双层规划模型 4

    2.1 双层规划的产生 4

    2.2 双层规划的特点 4

    2.3 双层规划的应用 5

    2.4 双层规划模型的建立 5

    2.5 双层规划的求解方法 7

    3 遗传算法

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