Research on Urban Cold Chain Logistics Path Optimization Considering Multi-Center and Time-Varying Road Networks

Abstract

This paper aims to address the time-dependent multi-depot vehicle routing problem with time windows (MDVRPTW) in urban cold chain logistics under a dynamic road network. The study considers the impact of carbon emissions and traffic congestion on urban cold chain logistics distribution activities. It proposes a cross-period road segment travel time calculation method, constructs a multi-objective optimization model that minimizes total costs encompassing comprehensive transportation costs, carbon emission costs, time penalty costs, cargo damage costs, and refrigeration costs. An adaptive large neighborhood search ant colony optimization algorithm (ALNS_ACO) is designed, which combines the exploration capability of ant colony optimization algorithm (ACO) with the local search capability of adaptive large neighborhood search algorithm (ALNS) to optimize and solve the model. Finally, the model is optimized and solved through simulation using six sets of C-type, R-type, and RC-type instances from the Solomon test database. The results indicate that: 1) The planned routes can reasonably avoid peak congestion periods in the morning and evening. Compared to the single-center scenario, the multi-center approach achieves superior solutions in terms of the total cost, carbon emissions, and total travel time in urban cold chain logistics distribution; 2) The exacerbation of traffic congestion leads to increased costs, and different optimization objectives have a significant impact on the model solutions; 3) Finally, through multidimensional comparisons of simulation performance with ACO and GA algorithms, the effectiveness of the proposed optimization algorithm is validated.

Keywords

• Multi-depot
• time-varying speeds
• urban cold chain logistics
• multi-objective optimization
• ALNS_ACO algorithm