Multi-objective Optimization of Blood Supply Network Using the Meta-Heuristic Algorithms

Subject Areas : Healthcare management

Zeinab Kazemi ¹ (Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mahdi Homayounfar ² (Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mehdi Fadaei ³ (Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mansour Soufi ⁴ ( Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Ali Salehzadeh ⁵ (Department of Biology, Rasht Branch, Islamic Azad University, Rasht, Iran)

Received: 2024-07-28 Accepted : 2024-08-17 Published : 2024-07-14

Keywords: Multi-objective optimization, blood, supply chain, genetic algorithm, NSGA II,

Abstract :

Management of blood product consumption is a complex and important issue in health systems. Limited blood supply, corruption, special conditions for storage of blood products, and high costs due to losses and lack of blood in medical centers are among the factors affecting the problem. In this study, all three levels of donors, blood collection centers, and customers (hospitals) are considered for modeling the blood supply network in the form of a multi-objective model. Three objectives of the proposed model are: (a) minimizing total costs, (b) minimizing total delivery time of blood units, and (c) minimizing the maximum unmet demand of hospitals in each period. Next, the model used two multi-objective optimization algorithms namely NSGAII and MOPSO algorithms for solving 30 sample problems in different dimensions (small, medium, and large). After solving the sample problems, the efficiency of the two algorithms were compared with each other. According to the results, for the cost objective function and each of its components separately, it can be seen that the values resulted from the NSGA-II algorithm were less than the MOPSO . Finally, a real word data set from the Tehran blood center was used to evaluate the validity of the proposed model.

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