Two-Phase Hybrid Search Algorithm for Time-Dependent Cold Chain Logistics Route Considering Carbon Emission and Traffic Congestion

Abstract

This paper studies the time-dependent cold chain logistics vehicle routing problem considering both traffic congestion and carbon emissions. A cold chain logistics model with time-dependent green vehicle paths with time windows (TDGVRPTW) was developed to fulfil the demands of green logistics and to take comprehensive account of consideration should be given to factors such as road congestion and carbon emissions. The objective of the model is to minimise total costs, which include carbon emission costs, penalty costs, fuel consumption costs, fixed costs, damage costs and refrigeration costs. Two-phase hybrid search algorithm was developed to solve this model. During the initial stage of the algorithm, a dual-population ant colony optimization (DACO) algorithm sharing the optimal individual is employed to optimize the distribution route of the vehicle. During the second phase, an adaptive golden section search (AGSS) algorithm is used to optimise the departure time of the vehicle from the distribution centre to avoid traffic congestion time periods. To validate the effectiveness of the suggested two-phase hybrid search algorithm, it is applied to the improved Solomon benchmark test set. The experimental findings demonstrate that the two-phase hybrid search algorithm can reasonably plan the driving routes and departure times for each vehicle, effectively avoiding peak traffic congestion periods in the city, and reducing the overall delivery cost.

Keywords

• Time-dependent green vehicle routing problem with time windows
• traffic congestion
• cold chain logistics
• ant colony optimization algorithm
• carbon emission