Open AccessParameter optimization of shared electric vehicle dispatching model using discrete Harris hawks optimization
Author(s) -
Yuheng Wang,
Yongquan Zhou,
Qifang Luo
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022344
Subject(s) - metaheuristic , mathematical optimization , robustness (evolution) , optimization problem , computer science , schedule , electric vehicle , vehicle routing problem , scheduling (production processes) , job shop scheduling , discrete optimization , algorithm , routing (electronic design automation) , mathematics , computer network , biochemistry , chemistry , power (physics) , physics , quantum mechanics , gene , operating system
The vehicle routing problem (VRP) problem is a classic NP-hard problem. Usually, the traditional optimization method cannot effectively solve the VRP problem. Metaheuristic optimization algorithms have been successfully applied to solve many complex engineering optimization problems. This paper proposes a discrete Harris Hawks optimization (DHHO) algorithm to solve the shared electric vehicle scheduling (SEVS) problem considering the charging schedule. The SEVS model is a variant of the VPR problem, and the influence of the transfer function on the model is analyzed. The experimental test data are based on three randomly generated examples of different scales. The experimental results verify the effectiveness of the proposed DHHO algorithm. Furthermore, the statistical analysis results show that other transfer functions have apparent differences in the robustness and solution accuracy of the algorithm.