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A discrete teaching-learning based optimization algorithm with local search for rescue task allocation and scheduling
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DOI：10.1016/j.asoc.2022.109980.png)
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The allocation and scheduling of the emergency rescue forces is a fundamental task in emergency management. This paper aims to address the allocation and scheduling problem to minimize the average completion time of all rescue teams by using a discrete teaching-learning based optimization algorithm with local search (DTOLS). First, an improved k-means clustering algorithm with constraints is proposed to assign tasks to rescue teams based on the location of rescue tasks. Second, a hybrid discrete optimization algorithm based on a teaching-learning mechanism is designed to generate the task scheduling sequence for each rescue team as an initial solution. Next, an efficient two-phase local search strategy is presented to improve the current solution. For three neighborhood task moves based on problem characteristics, which contains insert task within a team, swap tasks within a team, insert task between teams, the speed-up techniques are introduced to reduce the computational complexity of calculating completion time of a rescue team. Finally, the parameters of DTOLS are calibrated by Taguchi method to determine appropriate values. DTOLS is compared with the state-of-the-art algorithms, and the experimental results demonstrate the effectiveness of DTOLS in solving a set of test instances.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Allocation
Scheduling
k-means clustering
Teaching-learning mechanism
Local search
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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