Abstract 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.

中文翻译：

---

具有学习效应的灾害救援单位分配与调度的鲁棒双目标数学模型

摘要 本文提出了一种新的双目标混合整数线性规划（MILP）模型，用于考虑学习效果的灾难救援单元的分配和调度。当自然现象（如地震或洪水）发生时，所提出的决策支持模型有望帮助应急救援中心的决策者为救援单位提供有效的规划，以最大限度地减少救援行动的总加权完成时间，以及救援行动的总延误。该问题与无关并行机调度（UPMS）问题和旅行商问题（TSP）有一些共同点。为了处理问题的固有不确定性和双目标性质，应用了基于不确定性集的稳健优化技术和具有效用函数的多选择目标规划 (MCGP)。为了证明所提出模型的适用性，提出了伊朗马赞达兰省的真实案例研究。计算结果证实了问题的高度复杂性和不确定性对解决方案的显着影响。此外，分析结果为灾难性情况的决策者提供了有用的见解。