A scheduling optimization model based on modified genetic algorithm for green automobile logistics

DOI:	10.11809/bqzbgcxb2025.01.030
Keywords:	green automobile logistics; scheduling optimization; carbon emissions; genetic algorithm; mayfly algorithm
Abstract:	Automobile logistic enterprises require a comprehensive scheduling network to assist sales activities due to the enormous market demand. Therefore, a green automobile logistics scheduling optimization model is constructed in this study to address the difficult of planning the scheduling network for automobile logistic enterprises. The proposed model combines factory direct distribution to dealers with distribution to dealers through distribution centers, and considers the carbon emissions of carbon dioxide, methane, and nitrous oxide. The proposed model aims to minimize the total costs, which include fixed cost, transportation cost, distribution center cost, highway access cost, and carbon emission cost. To improve the solving ability, this study designs a modified genetic algorithm (GA MA) that fully utilizes the benefits of both the strong global search ability of the genetic algorithm and the strong local search ability of the mayfly algorithm. A real life case is used to validate the effectiveness of the proposed model and algorithm, and it is shown how the model contributes to lower costs and carbon emissions.
Issue:	Vol. 46 No. 1 (2025)
Published:	2025-01-31
PDF