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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.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (No. 52372420), the Key Research and Development Program of Zhejiang Province (No. 2023C01168), and the Research Incubation Foundation of Zhejiang University City College (No. J202316).
Data availability
The data are available from the corresponding author on reasonable request.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Longlong Leng
Longlong Leng is an assistant professor in the Department of Mechanical Engineering at the Hangzhou City University. He earned his Ph.D. from the Zhejiang University of Technology in 2020. His research interests include operations research, mathematical optimisation, vehicle routing problems, green logistics, and heuristic optimisation. He has published papers in a variety of operations management journals.
Qilin Jin
Qilin Jin is an associate professor in the Department of Mechanical Engineering at Hangzhou City University. He earned his Ph.D. from Dalian University of Technology in 2020. His main research interests are in modelling methods for complex systems, including sustainable supply chain management and innovation.
Ting Chen
Ting Chen is an assistant professor at the Department of Mechanical Engineering at the Hangzhou City University. He received his Ph.D. from Korea University of Science and Technology in 2020. His main research interests include energy management in electric vehicles, new and renewable energy systems, and mathematical optimisation.
Anping Wan
Anping Wan is a professor in the Department of Mechanical Engineering at the Hangzhou City University. He earned his PhD from the Zhejiang University in 2014. His research includes production scheduling, supply chain, and inventory optimisation.
Zheng Wang
Zheng Wang is an associate professor in the School of Computer & Computational Sciences at Hangzhou City University. He received his M.S. degree in Computational Biology from the University of East Anglia in the UK in 2011 and his Ph.D. in Control Science and Engineering from Zhejiang University of Technology in 2020. His research interests include pattern recognition, intelligent computing, and optimisation dispatch of complex systems.