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考虑混流装配线平衡及排序的供应链网络优化

发布时间:2018-02-10 17:06

  本文关键词: 供应链网络设计 分销策略 混流装配线平衡 排序 资源约束 出处:《南京理工大学》2017年硕士论文 论文类型:学位论文


【摘要】:随着社会的不断发展,顾客对于个性化、多样化以及特色化产品的追求越来越积极。为了满足客户需求,制造企业纷纷采用混合流水装配线生产组织方式。制造企业要想在规定的时间内满足客户需求,不仅需要供应商在合适的时间内供给合适数量的零部件,还需要制造企业能够在合适的时间内及时将成品配送给客户,并且制造企业的产品生产系统必须要高效、平稳,因此需要生产系统和供应链系统要紧密、协同工作。研究表明,装配线平衡系统和供应链网络系统两者之间存在相互影响、相互制约的关系,在构建供应链网络系统时将装配过程和供应分销过程结合考虑,能够提高系统的准确度、可靠度和客户服务水平。本文在多产品、多需求地、多工厂、多供应商的背景下,建立了一个需求拉动式的"供应-装配-分销"供应链网络,在优化供应链网络时,首先考虑了基于混流装配线平衡的供应链网络优化决策,然后考虑产品投产排序问题对供应链网络优化问题的影响及意义,最后研究了在资源使用数量受限制的情况下考虑混流装配线平衡的供应链网络优化问题。通过模型建立、求解和算例设计,得到了结果,并进行了数值化分析,得到以下重要成果:(1)通过比较可以发现,在现有的需求情境下,在多个制造商产能范围之内,模型能够在多供应商、多制造商、多产品、多需求地等节点之间决策出最优的供给、装配、分销策略,使得整个供应链网络的成本最低。(2)文章首次提出了基于最小化MAKESPAN的混合装配线投产排序的整数规划模型。采用该模型,给定任意数量的产品组合,MAKESPAN整数规划模型总是能够得出空余时间最小的排序方案。(3)模型能够根据投产排序约束尽可能使制造商在规定的期限内完成生产,决策出最有效减少总成本的方案。(4)在资源量受限制的情况下,模型能够优化资源的分配情况,有效减少供应链网络的总成本。本文在已有的"考虑简单装配线平衡的供应链网络优化"的基础上,结合实际情况,拓展到考虑混合装配线的供应链网络优化的研究,补充了该领域的研究,对企业的实际运营提供一些参考和建议。
[Abstract]:With the continuous development of the society, customers are more and more active in the pursuit of personalized, diversified and special products. Manufacturing enterprises have adopted the mixed income assembly line production organization mode. If manufacturing enterprises want to meet customer demand within a specified time, they not only need suppliers to supply the right number of parts and components in the right time. It is also necessary for the manufacturing enterprise to be able to deliver the finished product to the customer in time and in time, and the production system of the manufacturing enterprise must be efficient and stable, so the production system and the supply chain system should be close and work together. The assembly line balance system and the supply chain network system have the relationship of mutual influence and restriction. When constructing the supply chain network system, the assembly process and the supply distribution process can be considered together, and the accuracy of the system can be improved. Reliability and customer service level. In the context of multi-product, multi-demand, multi-factory, multi-supplier, this paper establishes a supply-assembly distribution supply chain network with demand pull, and optimizes the supply chain network. Firstly, the optimal decision of supply chain network based on mixed-flow assembly line balance is considered, and then the influence and significance of product commissioning scheduling problem on supply chain network optimization are considered. Finally, the optimization problem of supply chain network considering the balance of mixed-flow assembly line under the condition of limited resource usage is studied. The results are obtained by modeling, solving and example design, and the numerical analysis is carried out. Get the following important results: 1) by comparing, we can find that in the existing demand situation, in the range of multiple manufacturers' capacity, the model can be used in multi-supplier, multi-manufacturer, multi-product. The optimal supply, assembly and distribution strategies are determined between nodes with multiple needs. In this paper, the integer programming model of hybrid assembly line scheduling based on minimized MAKESPAN is proposed for the first time. Given an arbitrary number of product combinations, a MAKESPAN integer programming model is always able to arrive at a minimum spare time ranking scheme. The model can enable manufacturers to complete production within a specified time limit as far as possible according to the scheduling constraints in production. The model can optimize the allocation of resources when the amount of resources is limited. Based on the existing "supply chain network optimization considering simple assembly line balance" and combining with the actual situation, this paper extends the research of supply chain network optimization considering hybrid assembly line. Supplement the research in this field, and provide some references and suggestions for the actual operation of enterprises.
【学位授予单位】:南京理工大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:F274

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