A quadratically constrained mixed-integer non-linear programming model for multiple sink distributions.

Area of Science:

• Operations Research
• Supply Chain Management
• Transportation Logistics

Background:

• Increasing traffic congestion and fuel prices present major obstacles for supply chains serving multiple retailers.
• Efficient distribution networks are crucial for minimizing operational costs and ensuring timely deliveries.

Purpose of the Study:

• To optimize transportation routes for processed tomatoes in a long-haul and intercity distribution network.
• To develop a model that minimizes transportation costs, including fuel, operational, and maintenance expenses.

Main Methods:

• Utilized the heterogeneous capacitated vehicle routing problem (CVRP) framework.
• Developed a new quadratically constrained mixed-integer non-linear programming (MINLP) model.
• Incorporated real-time data, traffic conditions, and route diversion strategies for expedited deliveries.

Main Results:

• Achieved an estimated 27.59% reduction in average transportation costs.
• Reduced costs from GH¢20,270 ($1,638.91) to GH¢14,676 ($1,186.61) in a real-world case study.
• Demonstrated the model's effectiveness in lowering expenses while ensuring efficient deliveries.

Conclusions:

• The proposed routing optimization strategy effectively reduces transportation costs in complex supply chains.
• The MINLP model provides a viable solution for minimizing expenses and maintaining delivery efficiency.
• This approach offers significant potential for cost savings in the distribution of processed food products.