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Parameter optimization of shared electric vehicle dispatching model using discrete Harris hawks optimization.

Yuheng Wang1, Yongquan Zhou1,2, Qifang Luo1,2

  • 1College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China.

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|June 22, 2022
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Summary
This summary is machine-generated.

This study introduces a discrete Harris Hawks optimization (DHHO) algorithm to solve the complex shared electric vehicle scheduling (SEVS) problem, enhancing electric vehicle routing efficiency.

Keywords:
discrete Harris hawks optimizationmetaheuristic optimizationshared electric vehicle dispatching schedulingtransfer function

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Transportation Science

Background:

  • The vehicle routing problem (VRP) is an NP-hard combinatorial optimization challenge.
  • Traditional methods struggle with VRP complexity, necessitating advanced algorithms.
  • Metaheuristic algorithms offer robust solutions for complex engineering optimization tasks.

Purpose of the Study:

  • To develop and evaluate a discrete Harris Hawks optimization (DHHO) algorithm for the shared electric vehicle scheduling (SEVS) problem.
  • To analyze the impact of transfer functions on the SEVS model within the DHHO framework.
  • To assess the effectiveness and robustness of the proposed DHHO algorithm for electric vehicle routing.

Main Methods:

  • A novel discrete Harris Hawks optimization (DHHO) algorithm was developed.
  • The SEVS problem, a VRP variant, was modeled considering charging schedules.
  • The DHHO algorithm's performance was evaluated using randomly generated datasets of varying scales.

Main Results:

  • The proposed DHHO algorithm demonstrated effectiveness in solving the SEVS problem.
  • Transfer functions significantly influenced the algorithm's robustness and solution accuracy.
  • Statistical analysis confirmed performance variations across different transfer functions.

Conclusions:

  • The DHHO algorithm provides an effective approach for shared electric vehicle scheduling.
  • Careful selection of transfer functions is crucial for optimizing DHHO performance in SEVS.
  • This research contributes to efficient electric vehicle routing and scheduling solutions.