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)

Document Type : Industry Article

Authors

1 PhD student in Industrial Management, Department of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

2 Assistant Prof. Department of Industrial Engineering, Faculty of Technology and Engineering, East of Guilan, University of Guilan, Iran

3 Assistant Professor, Department of Management and Accounting, RashtBranch, Islamic Azad University, Rasht, Iran.

Abstract

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%.

Keywords

References

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Journal of Modeling in Engineering
Volume 18, Issue 60
June 2020
Pages 107-125

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History

  • Receive Date: 27 November 2019
  • Revise Date: 26 January 2020
  • Accept Date: 05 February 2020

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