Volume 33, Issue 3 (IJIEPR 2022)                   IJIEPR 2022, 33(3): 1-17 | Back to browse issues page


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KASSAMI S, Zamma A, Ben Souda S. Designing A Generic Decision-Making Model for Supply Chain Planning in an Uncertain Environment: Viability Mathematical Modeling. IJIEPR 2022; 33 (3) :1-17
URL: http://ijiepr.iust.ac.ir/article-1-1492-en.html
1- Industrial engineering and logistics , sofiakassami@gmail.com
2- 3. Faculty of science and Technology- Normal School of Technical Education Mohammedia Morocco.
Abstract:   (1791 Views)
Modeling supply chain planning problems is considered one of the most critical planning issues in Supply Chain Management (SCM). Nowadays, decisions making must be sufficiently sustainable to operate appropriately in a complex and uncertain environment of the market for many years to beyond the next decade. Therefore, making these decisions in the presence of uncertainty is a critical issue,as highlighted in a large number of relevant publications over the past two decades.The purpose of this investigation is to model a multilevel supply chain problem and determine the constraints that prevent the flow from performing properly, subject to various sources and types of uncertainty that characterize the flow. Therefore, it attempts to establish a generic model that relies on the stochastic approach.  Several studies have been conducted on uncertainty in order to propose an optimal solution to this type of problem. Thus, in this study, we will use the method of "Mixed integer optimization program" which is the basis of the algorithm that will be employed. This inaccuracy of the supply chain is handled by the fuzzy sets. In this paper, we intend to provide a new model for determining optimal planning of tactical and strategical decision-making levels, by building a conceptual model. Therefore, it enables us to model the mathematical programming problem. We investigate in this attempt, attention to solving the mathematical model. So in the resolution we are going through the algorithm in machine learning, therefore providing as in the end an optimal solution for the planning of production.
Full-Text [PDF 697 kb]   (1157 Downloads)    
Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2022/04/20 | Accepted: 2022/05/23 | Published: 2022/09/9

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