The Collaborative Green Vehicle Routing Problem with Time-Dependent Travel Speeds

This study investigates the collaborative green vehicle routing problem with time-dependent travel speeds (CGVRP-TD), which integrates horizontal collaboration among multiple depots with time-dependent traffic conditions. The problem jointly optimizes customer allocation, vehicle routing, and departure-time decisions to minimize transportation-related carbon emissions subject to vehicle capacity and customer time-window constraints. We formulate the CGVRP-TD as a mixed-integer programming model and develop a two-phase adaptive large neighborhood search algorithm with embedded departure-time optimization. The first phase explores routing and customer-assignment decisions using problem-specific operators, including two speed-related removal operators, while the second phase applies exact departure-time optimization to fixed routes. Computational experiments show that the proposed algorithm obtains high-quality solutions efficiently and that both departure-time optimization and speed-related operators contribute to emission reduction. The results further demonstrate that combining horizontal collaboration with time-dependent travel-speed information can substantially reduce transportation emissions while preserving on-time service. We also discuss emission-savings allocation mechanisms for sustaining collaboration among participating depots.