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Optimal acquisition policy for a supply network with discount schemes and uncertain demands.

机译:具有折扣计划和不确定需求的供应网络的最优采购策略。

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摘要

This study uses a mathematical programming approach in which a series of Mixed Integer Non-Linear Programming (MINLP) models are developed to represent a supply network for a manufacturer dealing with various quantity or volume discount schemes from suppliers, as well as incorporating uncertain product demands that follow Normal distributions. Furthermore, the manufacturer's optimal acquisition policy and production level are obtained simultaneously by solving the models with an objective of maximizing the expected value of the manufacturer's profit.;Although complicated by the employment of an integration function, the mathematical models are solved by a GAMS program with integrated SBB, CONOPT, MINOS, and SNOPT solvers working in collaboration. This research is one of the few studies in this field to use commercial optimization software for solving such complex mathematical models. The MINLP models and the GAMS solution program are applied in two real-world cases, and the preliminary results justify the capabilities of both the mathematical models and the GAMS solution program. Numerical analysis supports the managerial implications regarding the acquisition policy, and the comparison between the quantity discount and the volume discount. (Abstract shortened by UMI.)
机译:这项研究使用一种数学编程方法,其中开发了一系列混合整数非线性编程(MINLP)模型来代表制造商的供应网络,该制造商处理来自供应商的各种数量或数量折扣计划,以及合并不确定的产品需求遵循正态分布。此外,通过求解模型以同时获得制造商的最优购置策略和生产水平,以最大化制造商利润的期望值为目标。;尽管由于使用了积分函数而变得复杂,但数学模型还是通过GAMS程序求解的与集成的SBB,CONOPT,MINOS和SNOPT求解器协同工作。这项研究是该领域为数不多的使用商业优化软件来解决此类复杂数学模型的研究之一。 MINLP模型和GAMS解决方案程序已在两个实际案例中应用,初步结果证明了数学模型和GAMS解决方案程序的功能都是合理的。数值分析支持了有关购置政策以及数量折扣与数量折扣之间的比较的管理意义。 (摘要由UMI缩短。)

著录项

  • 作者

    Ma, Liping.;

  • 作者单位

    University of Windsor (Canada).;

  • 授予单位 University of Windsor (Canada).;
  • 学科 Engineering Industrial.
  • 学位 M.A.Sc.
  • 年度 2005
  • 页码 115 p.
  • 总页数 115
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:41:38

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