Sustainable collaborative strategy in pharmaceutical refrigerated logistics routing problem

• Published in: International journal of production research Vol. 62; no. 14; pp. 5036 - 5060
• Main Authors: Chen, Tingting, Chu, Feng, Zhang, Jiantong, Sun, Jiaqing
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 17.07.2024; Taylor & Francis LLC
• Subjects: Clustering; Collaboration; Computer Science; Energy consumption; Energy costs; Flow stability; hybrid heuristics; Logistics; multi-depot vehicle routing; Operations Research; Pharmaceuticals; Routing; Simulated annealing; Sustainability; Sustainable refrigerated logistics; Time dependence; Vehicle routing; Windows (intervals); hybrid heuristics; multi-depot vehicle routing; Sustainable refrigerated logistics
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2023.2283566

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 LU VRP) 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.