A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect
A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect
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DOI：
10.1016/j.cie.2020.106790
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
E. B. Tirkolaee;N. S. Aydın;M. Ranjbar-Bourani;G. Weber
中科院分区：
文献类型：
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作者：
E. B. Tirkolaee;N. S. Aydın;M. Ranjbar-Bourani;G. Weber
提出了一种考虑学习效应的灾害救援单元分配与调度的双目标混合整数线性规划模型。当自然现象（例如，地震或洪水）发生时，所提出的决策支持模型，预计将帮助应急救援中心的决策者提供有效的规划救援单位，以最大限度地减少总加权完成时间的救援行动，以及在救援行动的总延误。该问题与不相关并行机排序（UPMS）问题和旅行商问题（TSP）有一些共同的特点。为了处理固有的不确定性和双目标性质的问题，基于不确定集的鲁棒优化技术和多选择目标规划（MCGP）与效用函数。为了证明所提出的模型的适用性，在伊朗的马赞达兰省的一个真实的案例研究。计算结果证实了问题的高度复杂性和不确定性对解的显著影响。此外，分析结果提供了有用的见解，决策者的灾难性的情况。
This paper proposes a novel bi-objective mixed-integer linear programming (MILP) model for allocating and scheduling disaster rescue units considering the learning effect. When a natural phenomenon (e.g., earthquake or flood) occurs, the presented decision support model is expected to help decision-makers of emergency relief centers to provide efficient planning for rescue units to minimize the total weighted completion time of rescue operations, as well as the total delay in rescue operations. The problem has some features in common with unrelated parallel machine scheduling (UPMS) problem and traveling salesman problem (TSP). To deal with the inherent uncertainty and bi-objective nature of the problem, an uncertainty-set based robust optimization technique and multi-choice goal programming (MCGP) with utility functions are applied. To demonstrate the applicability of the proposed model, a real case study in Mazandaran province in Iran is presented. The computational results confirm the high complexity of the problem and the significant impacts of the uncertainty on the solution. Moreover, the analytical results provide useful insights to decision-makers for disastrous situations.