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Production planning and quality of service allocation across the supply chain in a dynamic lead time model.

机译:动态提前期​​模型中的整个供应链的生产计划和服务质量分配。

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摘要

In today's highly competitive marketplace, lowering the overall supply chain (SC) inventory holding cost while providing short product lead time and high customer quality of service (QoS) guarantee is significant. Minimizing some SC costs without actively controlling warehouse and facility inventory distribution is inadequate and may result in inefficient inventory distribution and lower the SC's competitiveness. Yet, state of the art SC production planning approaches only model facility WIP with static predetermined QoS requirements resulting in sub optimal production plans.;This thesis proposes a synergistic SC production planning methodology modeling and minimizing inventory costs under QoS guarantees. Non-linear SC production facility WIP and Inter Facility Inventory (IFI) levels are estimated in each period of the planning horizon using explicit models of stochastic production dynamics. A minimum inventory costs production plan is obtained subject to desired QoS guarantees. This methodology provides a practical and indeed tractable algorithm for solving the SC planning problem in an iterative manner. More specifically, it employs time scale driven decomposition of the original problem to (i) a multiple long time period (e.g. week) planning master problem that determines tentative targets for each SC production facility and inter-facility QoS levels; and (ii) short time period (e.g. hour) facility-specific WIP and QoS horizontal coordination (QoS-HC) sub-problems that determine WIP and IFI levels and necessary sensitivity information based on the tentative master problem targets. Outer linearearization constraints are constructed with sub-problem generated sensitivity information and appended to the master problem iteratively until the master problem representation of the non-linear relationships is sufficient to allow the generated targets to converge to the optimal solution.;This optimal scheduling methodology is implemented based on effective analytic approximations of SC performance evaluation and proposed stop-and-go SC operation protocol. The analytic approximations include SC decomposition, Large Deviations asymptotics, G/G/1/K approximations, Inverse Gaussian distribution approximation, and Monte-Carlo-simulation based calibration of SCV describing functions. Extensive computational experience and Monte-Carlo-simulation verification of the accuracy of the proposed SC production planning methodology is provided to demonstrate its effectiveness and document its superiority relative to industry practice.
机译:在当今竞争激烈的市场中,降低整体供应链(SC)的库存成本,同时提供较短的产品交货时间和高客户服务质量(QoS)保证非常重要。在不主动控制仓库和设施的库存分配的情况下将某些SC成本最小化是不够的,并且可能导致无效的库存分配并降低SC的竞争力。然而,最先进的SC生产计划仅采用具有静态预定QoS要求的模型设施WIP进行建模,从而导致次优生产计划。使用随机生产动态的显式模型,在计划范围的每个阶段中估计非线性SC生产设施的在制品和工厂间库存(IFI)的水平。根据所需的QoS保证,可以获得最低的库存成本生产计划。该方法提供了一种实用且确实易于处理的算法,用于以迭代方式解决SC规划问题。更具体地说,它将原始问题的时标驱动分解用于(i)多个长时间段(例如,一周)的计划主问题,该问题确定每个SC生产设施和设施间QoS级别的暂定目标; (ii)特定于设施的WIP和QoS水平协调(QoS-HC)子问题的较短时间段(例如小时),这些子问题可根据临时的主要问题目标确定WIP和IFI级别以及必要的敏感度信息。使用子问题生成的敏感度信息构造外部线性化约束,并迭代地将其附加到主问题上,直到非线性关系的主问题表示足以使生成的目标收敛到最优解为止。基于SC性能评估的有效解析近似值和提出的停停SC操作协议来实施。解析近似包括SC分解,大偏差渐近,G / G / 1 / K近似,高斯逆分布近似以及基于蒙特卡罗模拟的SCV描述函数的校准。提供了广泛的计算经验,并对提议的SC生产计划方法的准确性进行了蒙特卡洛模拟验证,以证明其有效性并证明其相对于工业实践的优越性。

著录项

  • 作者

    Wu, Chang-Chen.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Engineering System Science.;Operations Research.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 267 p.
  • 总页数 267
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统科学;运筹学;
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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