Green Vehicle Routing Problem with Time-Varying Traffic Congestion

We present a linear mixed integer programming model for the time-dependent heterogeneous green vehicle routing and scheduling problem (GVRSP) with the objective of minimizing total carbon dioxide emissions and weighted tardiness. Instead of discrete time intervals, the proposed model takes the traveled distances of arcs in different time periods as decision variables to determine the travel schedules of vehicles. We propose an exact dynamic programming method to calculate the optimal discrete departure/arriving time for the GVRSP. The dynamic programming method significantly reduces the computational complexity of the GVRSP when applying existing heuristic algorithms to solve large-sized problems. A genetic algorithm with dynamic programming (GA-DP) is developed to solve the formulated problem. Computational experiments are carried out to study the efficiency of the proposed hybrid solution approach with promising results.