A practical vehicle routing problem in small and medium cities for fuel consumption minimization

Today, reducing fossil fuel consumption in the transport sector is one of the important requirements to preserve the ecological environment. In this paper, we investigated a logistics problem in small and medium cities. We proposes the time-dependent vehicle routing problem model in small and medium cities considering heterogeneous fleets, time windows, multi-trip, time dependency, and road network. We conducted a numerical analysis on fuel consumption minimizing path selection between two customer nodes and found that the path selection is independent of the payload in most cases. The model is formulated as a mathematical integer linear programming model, and small problems are solved using a commercial optimization solver. For large-scale instances, we proposed a heuristic algorithm based on the variable neighborhood search and evaluated its performance. The proposed algorithm can decrease fuel consumption by 25 % compared to manual routing and scheduling. Furthermore, for provide useful managerial insights, sensitivity analyses were performed considering different vehicle capacities.