Energy-conserving cold chain with ambient temperature, path flexibility, and hybrid fleet: formulation and heuristic approach

Abstract

Cold chain logistics networks represent intricate systems that require harmonisation of their influence on the economy, environment, and society. However, simultaneously achieving these goals is hard. This paper defined a comprehensive model aiming to achieve a tradeoff of these goals related to cost efficiency, product quality, delivery timeliness, and environmental impacts. Meanwhile, the influences of ambient temperature, path flexibility, and hybrid fleet on the proposed dual-mode location-routing problem-based cold chain logistics (DMLRPCCL) are analyzed. A meticulously crafted hyper-heuristic framework employing Q-learning has been developed to address the complexity of cold chains to obtain high-quality solutions. The numerical study has shown that the proposed model can analyze various scenarios for perishable products and evaluate their impact on cost, emissions, and quality. The proposed algorithm is efficient and effective in achieving competitive results compared to three tailored algorithms. Extensive analyses are performed to empirically assess the effect of path flexibility, hybrid fleet, and ambient temperature on the DMLRPCCL planning. Several managerial insights are presented.