A Quadratically Constrained Mixed-Integer Non-linear Programming Model for Multiple Sink Distributions

Rising traffic congestion and fuel costs pose significant challenges for supply chains with numerous retailers. This paper addresses these challenges by optimizing transportation routes for processed tomatoes within a long-haul and intercity distribution network. We use the heterogeneous capacitated vehicle routing problem framework to create a new quadratically constrained mixed-integer non-linear programming model that aims to meet demand at multiple destinations while minimizing transportation costs. Our model incorporates real-time data and route optimization strategies that consider traffic conditions based on freight time and route diversions for expedited deliveries. It aims to devise an optimal transportation schedule that minimizes fuel, operational, and maintenance costs while ensuring efficient delivery of tomato paste. By applying this model to a real-world case study, we estimate a significant 27. 59% reduction in transportation costs, dropping them from GH¢20, 270 (1, 638. 91) to GH¢14, 676 (1, 186. 61) on average. This highlights the effectiveness of our strategy in lowering costs while maintaining smooth and efficient deliveries.