Aspects on the optimal decoupling point setting problem

IF 3.4 2区 数学 Q1 MATHEMATICS, APPLIED Applied Mathematics and Computation Pub Date : 2025-02-14 DOI:10.1016/j.amc.2025.129343
Herbert Jodlbauer , Matthias Dehmer , Frank Emmert-Streib
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Abstract

Demand-Driven Material Requirement Planning (DDMRP) represents a combination of traditional Material Requirements Planning (MRP) and the reorder point method. A key consideration in DDMRP revolves around determining the optimal position of decoupling points, also referred to as strategic inventory positions. This article addresses the question of where these decoupling points should be strategically positioned, utilizing a directed universal graph derived from the Bill of Materials (BOM) to formalize the optimal decoupling point setting problem. To address this challenge, analytical formulas are developed. The analytical formulas utilize parameters such as delivery time, demand variance, replenishment time, lot sizes, holding costs, and service levels. These formulas provide insights into key characteristics of optimal decoupling points. The obtained results can be categorized into arguments advocating for decoupling points to be positioned either more upstream or more downstream. Furthermore, we derive specific characteristics that an optimal decoupling point position should possess. This research contributes valuable knowledge for practitioners seeking to enhance the efficiency and effectiveness of their DDMRP implementation.
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最优解耦点设置问题
需求驱动的物料需求计划(DDMRP)是传统物料需求计划(MRP)和再订货点法的结合。DDMRP中的一个关键考虑因素围绕着确定解耦点的最佳位置,也称为战略库存位置。本文利用从物料清单(BOM)中导出的有向通用图来形式化最优解耦点设置问题,解决了这些解耦点应该战略性地定位在哪里的问题。为了应对这一挑战,开发了分析公式。分析公式利用交货时间、需求差异、补充时间、批量、持有成本和服务水平等参数。这些公式提供了对最优解耦点的关键特征的见解。所获得的结果可以被归类为主张将解耦点定位在更上游或更下游的论点。进一步,我们推导出最优解耦点位置应具备的特定特征。本研究为寻求提高DDMRP实施效率和效果的从业者提供了有价值的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.90
自引率
10.00%
发文量
755
审稿时长
36 days
期刊介绍: Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues.
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