A New Intuitionistic Fuzzy Multi-Criteria Decision-making Method for Designing Organ Transplantation Supply Chain Network Problem: Golden Eagle Metaheuristic Algorithm

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

2 Associate Professor, Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

Abstract

In today's world, organ donation has been identified as a life-giving process worldwide and has been welcomed by many people in different societies. This process can increase the people's quality of life in various countries and also increase the level of social security. In this study, to investigate the issue of organ donation, a supply chain network is presented that includes three sections: donation hospitals, transplant centers, and recipient zones. Besides, to make the appropriate decision to select the best receiver, a new multi-criteria decision-making (MCDM) method is used under intuitionistic fuzzy conditions. Then the proposed mathematical model is presented. In this model, the requirements of climate change and its effects on the transportation system, the quality of organs affected by cold ischemic time, and queuing in transplant centers are examined. A new decision-making method to select the best type of organ recipient and benefit from mathematical modeling provides to apply the issues of organ quality, climate change risk, and the concept of queuing that are the strengths points and innovations of this paper. Then, after presenting the model, using a compromise solution approach, the proposed multi-objective model with the objectives of cost, time, and quality of the organs becomes an equivalent one-objective model. Then, a golden eagle meta-heuristic algorithm is used to solve the problem. Finally, a practical example validates the decision-making method and the proposed mathematical model.

Keywords

References

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Journal of Industrial Engineering Research in Production Systems
Volume 9, Issue 18
September 2021
Pages 179-195

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