Shahrabadi Fereshteh, Kia Hamidreza, Heidari Ali, Khalilzadeh Mohammad
School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Health Serv Insights. 2024 Nov 2;17:11786329241288772. doi: 10.1177/11786329241288772. eCollection 2024.
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.
在危机情况下，伤病员的救治取决于医疗物资、救援物资和设备的及时供应。本研究的目的是提出一个考虑不确定环境的危机情况下易腐医疗物资供应链网络的数学模型。本文针对易腐医疗物品构建了一个包括供应商、中间仓库和最终客户的三级供应链。使用指数分布函数考虑客户服务需求的不确定性以及在中间仓库的停留时间。此外，假设易腐医疗物资的生命周期遵循威布尔分布函数。该模型试图最小化供应链的总成本以及易腐物品在整个链条中的总停留时间。对于小规模问题采用LP-Metric方法求解。由于该问题具有NP难性质，改进的多目标粒子群优化算法（MOPSO）和非支配排序遗传算法（NSGA-II）被用作两种著名且高效的元启发式算法来求解大规模问题。研究结果表明，元启发式算法能够在合理时间内有效地获得接近大规模问题最优解的结果。此外，结果表明NSGA-II在高质量解方面优于MOPSO。最后，通过实际案例研究证明了该模型在实际问题中的适用性。本文可为危机情况下易腐药品供应链网络的规划者和决策者及时供应和分发所需应急物资提供帮助。
