Disaster Rescue Task Scheduling: An Evolutionary Multiobjective Optimization Approach

doi:10.1109/TETC.2014.2369957

Return

Disaster Rescue Task Scheduling: An Evolutionary Multiobjective Optimization Approach

2018-04-01

PRE

Yu‐Jun Zheng *

Hai-Feng Ling

Jinyun Xue

DOI：10.1109/TETC.2014.2369957

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Disaster rescue

task scheduling

evolutionary multiobjective optimization (EMO)

fuzzy optimization

biogeography-based optimization (BBO)

permutation flowshop scheduling problem (PFSP)

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.