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Open Access

Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada, and also with the Systems Research Institute, Polish Academy of Sciences, Warsaw 01447, Poland
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Abstract

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution.

Keywords

evolutionary algorithmemergency material schedulingvariable reduction strategy

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Complex System Modeling and Simulation
Volume 3 Issue 2,
June 2023
Pages 83-101
DOI: 10.23919/CSMS.2023.0006
Cite this article:
Shu Z, Song A, Wu G, et al. Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling. Complex System Modeling and Simulation, 2023, 3(2): 83-101. https://doi.org/10.23919/CSMS.2023.0006

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Received: 09 January 2023
Revised: 05 March 2023
Accepted: 11 March 2023
Published: 20 June 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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