Providing a mathematical optimization model for the multi-product supply chain despite the possibility of supplier disruption under sanctions (Case Study of Refinery Repair Industries)

In this study, an optimized mathematical model for a multi-product supply chain with the possibility of supplier disruption under sanctions is presented. A supply chain involves the alignment of companies that market products or services. In order to manage the multi-product supply chain, there is a...

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Bibliographic Details
Published inمجله مدل سازی در مهندسی Vol. 18; no. 60; pp. 107 - 125
Main Authors Amir Rahimimanesh, Hamzeh Amin-Tahmasbi, Kambiz shahroodi
Format Journal Article
LanguagePersian
Published Semnan University 01.05.2020
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Summary:In this study, an optimized mathematical model for a multi-product supply chain with the possibility of supplier disruption under sanctions is presented. A supply chain involves the alignment of companies that market products or services. In order to manage the multi-product supply chain, there is a need for supplies and raw materials to be used in the processes and the supply of these items is subject to uncertainty so that suppliers may meet part of the ordered demand at the required time. Not to be provided to the customer. There are two types of suppliers to deal with this form of uncertainty. The first category is low-cost but unreliable suppliers, and in the second, there are suppliers that are reliable but more expensive than the first. Items received from suppliers are used in the manufacturing or repair process and a model for managing this process must be provided. To integrate these decisions into an integrated model, a stochastic two-stage decision model has been used and the approximation of mean sampling method has been used to solve the proposed problem. The results show that using the random two-step model results in an improvement of 6.84% in the outputs of the problem and also the use of suppliers along with the cheap supplier leads to the improvement of the model outputs. It was 14.9%.
ISSN:2008-4854
2783-2538
DOI:10.22075/jme.2020.19137.1807