介绍了一种新的数学方法,用于约束条件下生产过程的库存管理

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Daniel Darmawan, D. Kurniady, A. Komariah, Badrud Tamam, I. Muda, Harikumar Pallathadka
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引用次数: 0

摘要

摘要当前,一些制造企业很可能面临生产限制。例如,如果产品数量增加,公司可能无法生产所有产品。因此,公司可能会面临积压的问题。同时,在产品需求上升的情况下,企业对这种需求的反应能力可能受到限制;因此,它将遭受积压。在本研究的过程中,考虑了面临上述情况的那种公司。为了满足这些超额需求,企业将被迫从外部购买一些产品。因此,本研究的主要目的是定义和计算最优的制造和购买一些产品,以减少和优化总体库存成本。为此,提出了一个模型,称为“随买随做”模型。采用基于分支定界法的精确求解软件对模型进行了设计和求解。研究结果证实了该方法的可行性和有效性,并表明该模型可以用于降低总体库存成本,包括维护成本,订单成本,安装成本和采购成本,以及产品的总成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introduce a New Mathematical Approach to Inventory Management in Production Processes Under Constrained Conditions
Abstract Nowadays, some manufacturing organizations may well face production restrictions. For example, in case the number of products goes up, the company might not be capable of producing all products. As a consequence, the company may face backlogging. In the meanwhile, in case the demand for products rises, the given company may experience a restricted capacity to react to that kind of demand properly; thus, it will suffer backlogging. Over the course of this study, that kind of company facing the mentioned circumstances is considered. To meet those exceeded demands, companies would be forced to purchase some products from outside. Thus, the study’s primary aim is to define and calculate the optimum make and buy a number of products so that overall inventory cost is reduced and optimized. To do so, a model is proposed referred to as the make-with-buy model. This model is designed and solved by exact solution software in the based branch and bound method. The results of the study confirm the feasibility and efficiency of this method and demonstrate that this model can be applied to lessen the overall inventory costs, including maintenance, order, setup, and purchasing costs, and also the total costs of products.
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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