A Fuzzy Bi-objective Mathematical Model for Perishable Medical Goods Supply Chain Network Considering Crisis Situations: An Empirical Study.

In case of crisis, the salvation of injuries depends on the timely provision of medical goods, relief supplies, and equipment. The aim of this study is to present a mathematical model for the supply chain network of perishable medical goods in crisis situation considering the uncertain environment....

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Publicado en:Health Services Insights pp. 1 - 25
Autores principales: Shahrabadi, Fereshteh, Kia, Hamidreza, Heidari, Ali, Khalilzadeh, Mohammad
Formato: algorithm equations & formulas pictorial research tables/charts Journal Article
Publicado: Sage Publications Inc. 11/2/2024
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 11/2/2024
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      pub: Sage Publications Inc.
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        10.1177/11786329241288772
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        atl: A Fuzzy Bi-objective Mathematical Model for Perishable Medical Goods Supply Chain Network Considering Crisis Situations: An Empirical Study.
      aug:
        au:
          Shahrabadi, Fereshteh
          Kia, Hamidreza
          Heidari, Ali
          Khalilzadeh, Mohammad
        affil: School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
      sug:
        subj:
          Drug Storage Evaluation
          Equipment and Supplies Supply and Distribution
          Disaster Planning
          Health Services Accessibility
          Materials Management
          Models, Theoretical
          Human
          Descriptive Statistics
          Empirical Research
          Decision Making
          Drugs
          Healthcare Supply Chain
          Humanitarian Aid
          Uncertainty
          Questionnaires
      ab: In case of crisis, the salvation of injuries depends on the timely provision of medical goods, relief supplies, and equipment. The aim of this study is to present a mathematical model for the supply chain network of perishable medical goods in crisis situation considering the uncertain environment. In this paper, a three-level supply chain including suppliers, intermediate warehouses, and final customers is developed for perishable medical items. The uncertainty of customer demand for service and the spent time in the intermediate warehouses are considered using the exponential distribution functions. Also, it is assumed that the life-cycle of perishable medical goods follow the Weibull distribution function. The model attempts to minimize the total costs of the supply chain and total presence time of perishable items in the whole chain. The LP-Metric method is employed for solving small-sized problems. Due to the NP-Hardness of the problem, the modified Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are utilized as 2 well-known and efficient meta-heuristic algorithms for solving large-sized problems. The findings indicate that the meta-heuristic algorithms are efficient in achieving close to the optimal solution for large-size problems in a reasonable time. Also, the results demonstrate that NSGA-II outperforms MOPSO in terms of the high quality solution. Finally, the applicability of the model to real-world problems is demonstrated using a real case study. This paper can assist the planners and decision-makers of perishable drugs supply chain networks in crisis conditions with on-time supplying and distributing the required emergency items.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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