A Multi-Objective Heuristic Approach to Enhancing Perishable Supply Chain Efficiency via Progressive Dataset Partitioning
Abstract/Description
Efficient transport systems are crucial to reducing logistics costs and improving the performance of supply chains, particularly in the dairy sector, where the decomposition of milk requires timely and cost-effective collection. This study can bridge the gap between cost control and the transportation of perishable goods by optimizing a milk collection network for dairy processors. The proposed algorithm reduces the network, dividing the problem into subproblems, and applying MST iteratively for each truck and passing through the next truck until the last truck exhausts the entire network. The results show that the optimized route significantly reduces operational costs, travel distances and collection time while preserving the vulnerability constraints. The proposed algorithm is applied to the Sahiwal district of Punjab, Pakistan. Comparative analysis reveals that our proposed MST-based iterative heuristic, approach exceeds many of the methods mentioned in the literature on the data set. These findings provide logistical planners and dairy companies with flexible and scalable decision support tools to ensure sustainable and cost-effective milk transportation.
Keywords
Perishable Supply Chain, Minimum Spanning Tree (MST), Capacitated Vehicle Routing Problem (CVRP), Supply Chain Optimization, Milk Routing Problem.
Track
Management
Session Number/Theme
Management - Session II
Session Chair
Dr. Kamran Mumtaz
Start Date/Time
14-6-2025 9:00 AM
End Date/Time
14-6-2025 10:40 AM
Location
MCC 14 Ground Floor, AMAN CED Building
Recommended Citation
Ashraf, S., Butt, M. A., & Shafi, M. I. (2025). A Multi-Objective Heuristic Approach to Enhancing Perishable Supply Chain Efficiency via Progressive Dataset Partitioning. IBA SBS 4th International Conference 2025. Retrieved from https://ir.iba.edu.pk/sbsic/2025/program/56
COinS
A Multi-Objective Heuristic Approach to Enhancing Perishable Supply Chain Efficiency via Progressive Dataset Partitioning
MCC 14 Ground Floor, AMAN CED Building
Efficient transport systems are crucial to reducing logistics costs and improving the performance of supply chains, particularly in the dairy sector, where the decomposition of milk requires timely and cost-effective collection. This study can bridge the gap between cost control and the transportation of perishable goods by optimizing a milk collection network for dairy processors. The proposed algorithm reduces the network, dividing the problem into subproblems, and applying MST iteratively for each truck and passing through the next truck until the last truck exhausts the entire network. The results show that the optimized route significantly reduces operational costs, travel distances and collection time while preserving the vulnerability constraints. The proposed algorithm is applied to the Sahiwal district of Punjab, Pakistan. Comparative analysis reveals that our proposed MST-based iterative heuristic, approach exceeds many of the methods mentioned in the literature on the data set. These findings provide logistical planners and dairy companies with flexible and scalable decision support tools to ensure sustainable and cost-effective milk transportation.
