Sustainable collaborative strategy in pharmaceutical refrigerated logistics routing problem

Abstract

The rapid growth of pharmaceutical refrigerated logistics poses sustainability challenges, including elevated costs, energy consumption, and resource inefficiency. Collaborating multiple depots can enhance logistics efficiency when standalone distribution centres have limited transport resources, i.e. refrigerated vehicles. However, the sustainable benefits and performance across different strategies remain unexplored. This study fills this research gap by addressing a refrigerated pharmaceutical routing problem. While many collaborative strategies prioritise economic and environmental benefits, our approach highlights a vital social indicator: maintaining vehicle flow equilibrium at each depot during collaboration. This ensures the stability of transport resources for all stakeholders, promoting sustainable collaborative logistics. The problem is formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Three collaborative strategies using Clustering VRP (C_LUVRP) and improved Open VRP (OVRP) are proposed and compared. We develop two approaches to address traditional OVRP limitations in ensuring vehicle flow equilibrium at each depot. Our models consider perishable pharmaceuticals and time-dependent travel speeds. Three hybrid heuristics based on Simulated Annealing and Variable Neighborhood Search (SAVNS) are proposed and evaluated for efficacy. Computational experiments and a case study demonstrate distinct sustainable benefits across various strategies, offering valuable insights for decision-makers in the refrigerated logistics market.

KEYWORDS:

• Sustainable refrigerated logistics
• multi-depot vehicle routing
• hybrid heuristics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71971156 and 72371188], and the Fundamental Research Funds for the Central Universities [grant number 22120210241].

Notes on contributors

Tingting Chen

Tingting Chen is a PhD student in Management Science and Engineering from the School of Economics and Management, Tongji University, China. She received the B.S. degree in electrical business from Sichuan University, China, in 2021. Her research interests are sustainable last-mile logistics, pharmaceutical supply chain management, integer programming and combinatorial optimisation.

Feng Chu

Feng Chu received the B.S. degree in electrical engineering from Hefei University of Technology, Hefei, China, in 1986; the M.S. degree in metrology, automatic control, and electrical engineering from National Polytechnic Institute of Lorraine, Lorraine, France, in 1991; and the Ph.D. degree in automatic control, computer science, and production management from University of Metz, Metz, France, in 1995. Feng Chu is currently a Full Professor of Operations Research at Univ Evry, University of Paris Saclay, Evry, France and leader of the AROBAS research team of the IBISC Lab. Her research interests include combinatorial optimisation, operations research, stochastic programming and Petri nets for modelling, analysis, and optimisation of complex systems, such as intelligent transportation systems and logistic and production. She is the author/co-author of more than 120 papers in international journals. She is awarded by the French ministry of Education for her Doctoral Supervision and Research Activities (PEDR) since 2005. She was selected for Foreign Hundred Talents Program of Anhui province in 2013, Thousand Talents Program of Sichuan province in 2016 and Min Jian Scholar in Fujian province of Chine, 2018. Dr. Chu was an Associate Editor for IEEE T-SMC, Part C from 2010 to 2013. She is currently an Associate Editor for IEEE T-ITS and IEEE TASE, an Guest Editor for IJPR, TRC and JOCO. She is an IPC member for over 80 international conferences.

Jiantong Zhang

Jiantong Zhang received the B.S. degree from the Department of Management Science at Jilin University, China, in 1988; the M.S degree from the Institute of Mathematics at Jilin University, China, in 1991; the M.S degree in Financial Statistics from Paris Diderot University, France, in 1996, and the Ph.D. degree in Statistics from University of Paris VI, France, in 2000. Jiantong Zhang is currently a Full Professor at the School of Economics and Management at Tongji University in China. She has served as a guest editor for medical journals such as Healthcare, etc. In recent years, Dr. Zhang has been actively involved in research in the areas of big data analysis, optimisation theory, and has led as well as participated in numerous domestic and international research projects, including grants from the National Natural Science Foundation of China, the China Scholarship Council, and over ten other research projects. Dr. Zhang has authored over 50 high-quality papers in prestigious journals such as Production and Operations Management, Transportation Research Part E, OMEGA-The International Journal of Management Science, and International Journal of Production Research, etc.

Jiaqing Sun

Jiaqing Sun is currently doing his PhD in the Department of Management Science and Engineering at Tongji University. He graduated from the School of Mathematical Science of Anhui University in 2018. He has been pursuing his PhD in the Department of Management Science and Engineering at Tongji University since 2018. His PhD research topic is related to bike-sharing rebalancing problem and vehicle routing problem.