Affiliations: [a]
School of Economics and Management, Yanshan University, Qinhuangdao, Hebei, China
| [b]
Research Center of Regional Economic Development, Yanshan University, Qinhuangdao, Hebei, China
Correspondence:
[*]
Corresponding author. Chuang Tu, School of Economics and Management, Yanshan University, Qinhuangdao 066004, Hebei, China. E-mail: [email protected]
Abstract: To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development.
Keywords: Community group-buying, the route problem of end-distribution, improved genetic algorithm, carbon emission cost
