异构生产周期下确定性无能力批量问题的多项式时间算法

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2025-05-15 DOI:10.1016/j.orl.2025.107304

Tan Yu, Xiang Zhong, Yongpei Guan

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引用次数: 0

摘要

在本研究中，我们探讨了具有不同质量和生产周期的两种产品的独特的确定性无生产能力的批量问题。任何一种产品都可以满足需求，这就需要并行的库存跟踪，并增加了模型的复杂性。我们的目标是降低成本，并通过优化订购策略保持平均质量水平。结合后向动态规划和最短路径算法的多重排序特性，论证了有限值空间枚举，给出了一个复杂度为0 （N4）的最优算法，其中N为规划水平周期数。代码可在Github上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Polynomial-time algorithms for deterministic uncapacitated lot-sizing problem with heterogeneous production cycles

In this study, we explore a unique deterministic uncapacitated lot-sizing problem with two products of distinct quality and production cycles. Either product can fulfill the demand, necessitating concurrent inventory tracking, and adding model complexity. We target cost reduction and maintain an average quality level by optimizing ordering strategies. Incorporating multiple ordering properties into backward dynamic programming and the shortest path algorithm, we demonstrate finite value space enumeration and present an optimal algorithm with

O (N^{4})

complexity, where N denotes the number of planning horizon periods. The code is available at Github.

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.