Designing a Mathematical Model to Solve the Uncertain Facility Location Problem Using C Stochastic Programming Method

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Foundations of Computing and Decision Sciences Pub Date : 2023-09-01 DOI:10.2478/fcds-2023-0014
Paitoon Chetthamrongchai, Biju Theruvil Sayed, Elena Igorevna Artemova, Sandhir Sharma, Atheer Y. Oudah, Ahmed Kateb Jumaah Al-Nussairi, Bashar S. Bashar, A. Heri Iswanto
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Abstract

Abstract Locating facilities such as factories or warehouses is an important and strategic decision for any organization. Transportation costs, which often form a significant part of the price of goods offered, are a function of the location of the plans. To determine the optimal location of these designs, various methods have been proposed so far, which are generally definite (non-random). The main aim of the study, while introducing these specific algorithms, is to suggest a stochastic model of the location problem based on the existing models, in which random programming, as well as programming with random constraints are utilized. To do so, utilizing programming with random constraints, the stochastic model is transformed into a specific model that can be solved by using the latest algorithms or standard programming methods. Based on the results acquired, this proposed model permits us to attain more realistic solutions considering the random nature of demand. Furthermore, it helps attain this aim by considering other characteristics of the environment and the feedback between them.
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用C随机规划方法设计求解不确定设施选址问题的数学模型
对任何组织来说,工厂或仓库等设施的选址都是一项重要的战略决策。运输成本通常是所提供商品价格的重要组成部分,它是计划所处位置的一个函数。为了确定这些设计的最优位置,目前已经提出了各种方法,这些方法通常是确定的(非随机的)。在介绍这些具体算法的同时,本研究的主要目的是在现有模型的基础上,利用随机规划和随机约束规划,提出一种定位问题的随机模型。为此,利用具有随机约束的规划,将随机模型转化为可使用最新算法或标准规划方法求解的特定模型。基于所获得的结果,该模型允许我们在考虑需求随机性的情况下获得更现实的解决方案。此外,通过考虑环境的其他特征以及它们之间的反馈,它有助于实现这一目标。
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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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