Abstract
Aiming to improve the timeliness of logistics distribution and render the optimized route scheme effective under the real traffic network, we study the green vehicle routing problem with dynamic travel speed from both dimensions of time and space. A discrete formulation is proposed to calculate the travel time based on periods and arcs, which allows a vehicle to travel across an arc in multiple periods. Then, we establish a mixed-integer nonlinear programming model with minimum distribution costs including transportation costs, carbon emissions costs, and penalty costs on earliness and tardiness. A hybrid adaptive genetic algorithm with elite neighborhood search is developed to solve the problem. In the algorithm, a neighborhood search operator is employed to optimize elite individuals so that the algorithm can stimulate the intensification and avoid falling into a local optimum. Experimental instances are constructed based on benchmark instances of vehicle routing problem. The numerical results indicate that the proposed algorithm is rather effective in global convergence. Compared with the routing schemes in which travel speed merely varies with time periods or locations, the vehicle route optimized on spatiotemporal-varying speed outperforms them in terms of carbon emissions and timeliness. The research can provide a scientific and reasonable method for logistics enterprises to plan the vehicle schedule focusing on spatiotemporal-dependent speed of the road network.
Highlights
Most freight companies ignore the varying speed of the vehicle when planning the distribution routes (i.e., Çam and Sezen [1]), so that the preoptimized route scheme is hard for vehicles to service customers within the time windows
With the increasing carbon emissions, reducing vehicle fuel consumption has become the main trend of logistics distribution. us, the objective of distribution should take carbon emissions influenced by travel speed on arcs into account. erefore, we focus on the green vehicle routing problem with the spatiotemporal-varying vehicle speed and soft time windows, which is a variant of vehicle routing problem (VRP)
We focus on the spatiotemporal-dependent green vehicle routing problem with time windows which is related to the time-dependent vehicle routing problem with time windows (TDVRPTW) and green vehicle routing problem with time windows (GVRPTW)
Summary
Most freight companies ignore the varying speed of the vehicle when planning the distribution routes (i.e., Çam and Sezen [1]), so that the preoptimized route scheme is hard for vehicles to service customers within the time windows. In order to ensure the timeliness of distribution, the vehicle route planning should apply the traffic information about the road network to calculate the travel time more precisely. Erefore, we focus on the green vehicle routing problem with the spatiotemporal-varying vehicle speed and soft time windows, which is a variant of vehicle routing problem (VRP). Liu et al [6] applied ant colony algorithm, a metaheuristic algorithm, to solve time-dependent vehicle routing problem with time windows (TDVRPTW). Time-dependent green vehicle routing problem with time windows (TDGVRPTW) studied in our work is more complicated than TDVRPTW which needs to calculate the carbon emissions of each period and road section.
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