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Complex System Modeling and Simulation 2023, 3(2): 83-101 https://doi.org/10.23919/CSMS.2023.0006
Open Access | Issue | Published: 20 June 2023

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

Show Author's Information Zhen Shu1,  Aijuan Song2,  Guohua Wu2( ),  Witold Pedrycz3
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
Keywords:
evolutionary algorithm, emergency material scheduling, variable reduction strategy
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information Zhen Shu1, Aijuan Song2, Guohua Wu2( ), Witold Pedrycz3
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

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 algorithm, emergency material scheduling, variable reduction strategy

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Received: 09 January 2023
Revised: 05 March 2023
Accepted: 11 March 2023
Published: 20 June 2023
Issue date: June 2023

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