Optimization Research on the MDVRPTW Problem in Urban Cold Chain Logistics under Time-Varying Road Networks

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 show: 1) The planned vehicle routes can reasonably avoid peak congestion periods in the morning and evening, and multiple depots compared to single depot scenarios achieve better solutions in terms of total costs, carbon emissions, and total travel time for 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.