Disaster Rescue Task Scheduling: An Evolutionary Multiobjective Optimization Approach

Efficient rescue task scheduling plays a key role in disaster rescue operations. In real-world applications, such an emergency scheduling problem often involves multiple objectives, complex constraints, inherent uncertainty, and limited response time requirement. In this paper, we propose a fuzzy multiobjective optimization problem of rescue task scheduling, the aim of which is to simultaneously maximize the task scheduling efficiency and minimize the operation risk for the rescue team. We then develop an efficient multiobjective biogeography-based optimization (EMOBBO) algorithm for solving the problem. To cope with the uncertainty, we employ three correlated fuzzy ranking criteria, and use the concept of fuzzy dominance for comparing the dominance relation of solutions. In EMOBBO, we define new migration and mutation operators for effectively evolving the permutation-based solutions, use a problem-specific solution rearrangement mechanism for filtering out inefficient solutions, and employ a local neighborhood structure to suppress premature convergence. Computational experiments show that the proposed EMOBBO algorithm outperforms some state-of-the-art evolutionary multiobjective optimization algorithms, and our algorithm has been successfully applied to several real-world disaster rescue operations in recent years.