A Dynamic Sustainable Optimization Model for A Transportation System of Bottled Water

Introduction/Objective The need for sustainable logistics in the bottled water distribution sector is growing rapidly. This study addresses this issue by developing a multi-objective optimization model that creates a balance between travel distance and carbon dioxide emissions. The aim is to create and design efficient delivery routes while utilizing intelligent vehicle allocation to reduce environmental impact. Methods A mixed-integer linear programming model is proposed, integrated with K-means clustering and real-time traffic data imported from Google Maps API. Two different case studies were examined: one employed a heterogeneous fleet consisting of large, medium, and small-sized vehicles, and the other one employed a homogeneous fleet consisting of small vehicles with various emission rates. The two cases were analyzed according to three weighing scenarios: equal priority (50%–50%), distance-prioritized (80%–20%), and emissions-prioritized (20%–80%). Results In the first case, the model selected the same fleet composition in all scenarios. The total objective function values were 467.30, 546.15, and 388.42. In the second case, the model assigned the heaviest loads and longest routes to lower-emission vehicles, resulting in objective values of 65.34, 99.49, and 31.20, with stable route assignments and optimized load utilization. Discussion The model showed strong adaptability across fleet structures and operational priorities. It was able to reduce emissions without compromising operational efficiency, which shows its practical value for logistics planning. The utilization of real-time traffic and emission-based routing enhances its environmental applicability further. Conclusion The proposed approach supports sustainable route planning and fleet utilization. According to the various scenarios and vehicle configurations used to test the robustness of the model, it can be used on a broader scale in logistic systems with environmental constraints.