Low-carbon VRP for cold chain logistics considering real-time traffic conditions in the road network

Purpose This paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost. Design/methodology/approach This study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set. Findings The result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax. Research limitations/implications There are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle. Originality/value Existing research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.

Automated summary

This study proposed a low-carbon cold chain logistics routing optimization model considering the complexity of the road network and time-varying traffic conditions. The results show that the model can accurately assess distribution costs and carbon emissions, providing guidance for logistics companies and governments.