Presenting Mathematical Programming Model for Sustainable Supplier Selection and Order Allocation Using the COPRAS Method

Document Type : Research/ Original/ Regular Article

Authors

1 Ph.D. Student, Department of Industrial Engineering, University of Kurdistan

2 Engineering department, University of kurdistan, Iran

Abstract

Supplier selection is one of the most important issues related to supply chain optimization. This research aims to present an effective approach to the problem of supplier selection and order allocation in a way that takes into account supplier stability criteria on the one hand and total profit maximization on the other hand, in an environment of uncertainty. For this purpose, suppliers are first ranked based on sustainability criteria with the COPRAS      multi-criteria decision-making method, which is modified to use type 2 fuzzy numbers and intuitionistic fuzzy. Then, the maximization of the obtained ranks for the selected suppliers is used as the second objective function in a bi-objective programming model, where the first objective function is the maximization of the total profit. To solve the mathematical model, the fuzzy multi-objective optimization method has been used, in which the minimum utility resulting from the fulfillment of each of the objectives is maximized. The mathematical model is solved by the fuzzy multi-objective optimization method, in which the minimum utility obtained from the fulfillment of the objectives is maximized. The proposed approach for a sample hypothetical problem has been solved accurately and by performing sensitivity analysis, the amount of change in the optimal response to changes in the main parameters of the model has been identified and investigated. The results show how to simultaneously increase the profit of the organization as much as possible and, at the same time, allocate more orders to suppliers with a higher degree of sustainability.

Keywords

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References

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History

  • Receive Date: 17 December 2022
  • Revise Date: 10 May 2023
  • Accept Date: 31 May 2023
  • Publish Date: 22 June 2023

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