Spatiotemporal-Dependent Vehicle Routing Problem Considering Carbon Emissions

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.

Research on Optimization of Transport Path for Novel Coronavirus Detection Samples

“…Jeong et al [43] studied the impact of package weight on drone flight duration and considered the situation where drone flights are prohibited in some delivery areas during certain periods. Furthermore, Liu et al [11] innovatively incorporated the concepts of no-fly zones and pickup and delivery tasks into the VRPD framework. Their solution strategy was grounded in a two-stage heuristic algorithm, anchored…”

“…Constraint (10) not only maintains the flow balance of trucks, but also ensures that trucks can visit a detection node at most once. Constraint (11) requires that each edge be visited by a drone at most once. Constraint (12) requires that each edge visited by the drone has at least one node service during this drone trip to prevent the drone from flying along the truck's path unnecessarily.…”

“…Subsequently, a profusion of offshoots materialized, among which were TSPD (Traveling Salesman Problem with Drone) [6], MFSTSP (Multiple Flying Sidekicks Traveling Salesman Problem) [7], TSP-mD (Traveling Salesman Problem with Multiple Drones) [8], VRPD (Vehicle Routing Problem with Drone) [9], to name but a few, each tailored to distinct delivery scenarios and endowed with different objective functions. Researchers have predominantly built to formulate mathematical models with delivery time [10] or delivery cost [11] as the centerpiece objective. They have proffered sundry algorithmics to tackle these quandaries, including the column generation algorithm [12], the genetic algorithm [13], the variable neighborhood search algorithm [14], and the hybrid algorithm [15], among others.…”

Multivisit Drone-Vehicle Routing Problem with Simultaneous Pickup and Delivery considering No-Fly Zones

“…Te above literature suggests that the joint distribution problem of drones and trucks can be regarded as adding drones to the VRP. Terefore, there are also numerous types of research worth learning in this regard [52,53].…”

TDGVRPSTW of Fresh Agricultural Products Distribution: Considering Both Economic Cost and Environmental Cost

“…In order to evaluate the efficiency of the proposed approach in the TDGVRPSTW model, two GA-based algorithms are used for comparison. There are many variants of GA for GVRP model [38], among which adaptive genetic algorithm (AGA) and hybrid genetic algorithm (HGA) are commonly used [39]. AGA and HGA are coded as follows:…”