Vehicle and UAV Collaborative Delivery Path Optimization Model

Abstract

In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

“…Li et al [62] focused on the optimization of emergency logistics distribution using a collaborative delivery model with vehicles and UAVs. This study addressed the limitations of UAV-only delivery and proposes a model that considers factors such as start-up costs, waiting costs, and penalty costs.…”

Suitable for loading and distributing modular containers

“…The stowage volume constraint is shown in Equation ( 5), which indicates that the total volume of the assembled cargo does not exceed the effective volume of the vehicle. Since the above objective function reflects the cargo loading ratio and the dimensions are consistent, the two objective functions can be combined to obtain the final single objective function (6) [6] .…”

Quad-Rotor Unmanned Aerial Vehicle Path Planning Based on the Target Bias Extension and Dynamic Step Size RRT* Algorithm

“…Path planning consists of planning an optimal path from an initial position to an end position, with quad-rotor UAVs flying in the presence of obstacles on the terrain and the required constraints being met at the same time [10]. At present, the most common algorithms for path planning include Dijkstra's algorithm [11], the A* algorithm [12][13][14], the Artificial Potential Field Method [15], the RRT (Rapid-exploring Random Trees) algorithm [16][17][18][19][20], and the RRT* algorithm [21][22][23][24][25], the last of which is progressively optimized for the RRT algorithm.…”