Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA

Abstract

In order to tackle the issue of carbon emissions in logistics and distribution, a vehicle routing model was proposed with the aim of minimizing the overall cost, which includes the vehicle’s fixed cost, transportation costs, and carbon emission costs. An enhanced genetic algorithm, based on a modified Nearest Neighbor Construction (NNC) method, was developed to solve this model. A comparative analysis was conducted using the Solomon dataset to study the impact of carbon emissions on vehicle routing optimization, comparing scenarios with and without considering carbon emission costs. The research findings revealed that the Improved NNC (INNC) method exhibited faster convergence compared with the random generation and random insertion methods. Despite a slight increase of 0.5127% in transportation cost when factoring in carbon emission costs, there was a decrease of 4.6914% in carbon emission costs and 0.3578% in total cost. These results offer theoretical insights and empirical evidence to inform the development of models for the logistics industry in the context of a low-carbon economy.

Keywords

• low carbon logistics
• genetic algorithm
• vehicle path optimization
• nearest neighbor algorithm