Abstract

This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a bi-objective mixed-integer programming (BOMIP) model is proposed to assign and schedule the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents. The second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to be more similar to the real world, time windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position of the corresponding relief team and the fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the Pareto solution in a reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of the proposed algorithms are compared with respect to different performance metrics in large-size test problems. Finally, the sensitivity analysis and the managerial suggestions are provided to investigate the impact of some parameters on the Pareto frontier.

Highlights

  • Disasters have always been a major threat to human societies such that disasters have led to many casualties and economic losses during recent years

  • A large variety of metaheuristic algorithms such as NSGA-II and MOPSO are used to solve the problem, and the results showed the superiority of these algorithms in a reasonable time

  • This research addresses the allocation and scheduling of relief teams which has a key role in the response phase of the disasters

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Summary

Introduction

Disasters have always been a major threat to human societies such that disasters have led to many casualties and economic losses during recent years. The fatigue effect has been introduced by Nayeri et al [34] in disaster management According to this phenomenon, the processing time of the incidents not to be fixed in the planning horizon. The rescuers lose their physical power after successive activities and their performance decreased step-by-step, and the necessary time to process the incidents can be increased due to the fatigue effect This concept is considered in this paper. Disaster relief must be done within a specified time after the incident; otherwise the damages will be irreparable We consider this issue as time window in the research problem. In addition to the physical power of the rescuers (fatigue effect), the time window and the damaged routes are considered in this research due to similarity to the real world.

Literature Review
Definition and modeling of the problem
Model assumptions
Mathematical model
Solution framework
Crossover operator
Mutation operator
Initial population generation
Time window consideration
Computational experiments
Performance metrics
Data generation
Parameter setting
Analysis of the results
Sensitivity analysis of the fatigue effect
Sensitivity analysis of the travel time
Findings
Conclusions and future work
Full Text
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