In this paper, a two-echelon cooperated routing problem for the ground vehicle (GV) and its carried unmanned aerial vehicle (UAV) is investigated, where the GV travels on the road network and its UAV travels in areas beyond the road to visit a number of targets unreached by the GV. In contrast to the classical two-echelon routing problem, the UAV has to launch and land on the GV frequently to change or charge its battery while the GV is moving on the road network. A new 0–1 integer programming model is developed to formulate the problem, where the constraints on the spatial and temporal cooperation of GV and UAV routes are included. Two heuristics are proposed to solve the model: the first heuristic (H1) constructs a complete tour for all targets and splits it by GV routes, while the second heuristic (H2) constructs the GV tour and assigns UAV flights to it. Random instances with six different sizes (25–200 targets, 12–80 rendezvous nodes) are used to test the algorithms. Computational results show that H1 performs slightly better than H2, while H2 uses less time and is more stable.

The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic areas. We formulate a mixed-integer programming model that minimizes the delivery time, with the consideration of the energy consumption model of drones. To solve CRP-T&D, we develop an improved variable neighborhood descent (IVND) that combines the Metropolis acceptance criterion of Simulated Annealing (SA) and the tabu list of Tabu Search (TS). Meanwhile, the integration of K-means clustering and Nearest neighbor strategy is applied to generate the initial solution. To evaluate the performance of IVND, experiments are conducted by comparing IVND with VND, SA, TS, variants of VND, and large neighborhood search (LNS) on instances with different scales. Several critical factors are tested to verify the robustness of IVND. Moreover, the experimental results on a practical instance further demonstrate the superior performance of IVND.

In this paper, we investigate a novel Two-Echelon Ground vehicle and its mounted Unmanned aerial vehicle Cooperative Routing Problem (2E-GUCRP) for intelligence, surveillance, and reconnaissance (ISR) missions. In a typical cooperative system with Ground Vehicle (GV) and Unmanned Aerial Vehicle (UAV), the UAV is launched from the GV, automatically flies to the designated target and return to the GV before powering off. Meanwhile, acting as a mobile platform, the GV can carry the UAV moving in larger region and charge or exchange the UAV’s battery. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software (e.g. CPLEX), is developed to formulate the problem. In order to quickly solve the medium-scale or above problems, two heuristics are designed. Random instances and practical case based on the road network in Changsha, China, are used for experiments. The computational results indicate that, compared to the mode of Two-Echelon Vehicle Routing Problem (2E-VRP), the cooperative GV and UAV can complete the ISR missions in much shorter time through utilizing the proposed 2E-GUCRP mode. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.

Driven by environmental concerns and new regulations, electric vehicles (EVs) are increasingly becoming popular for package delivery. However, due to their limited driving range, the EV has to be recharged during the route in many situations. A new variant of the electric vehicle routing problem with time windows is investigated through integrating decisions on multiple recharging options, which are partial recharging and battery swapping. A mixed integer programming model is developed to formulate the problem. An improved ant colony optimization (ACO) algorithm hybridized with insertion heuristic and enhanced local search is designed to solve the problem. Also, a new probabilistic selection model in ACO is proposed by integrating the impact of both distances and time windows. Computational experiments based on open data source is utilized to validate the performance of the algorithm, and the results indicate that the newly designed insertion heuristic and local search strategies improve the efficiency for solving the problem. The results for all the instances under the strategy of multiple recharging options are compared with those under strategies of partial recharging and battery swapping, which shows that the former strategy can help saving costs for most of the situations.

Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities and medical items for home-quarantined people. Considering the unmanned operation, agility and use of clean energy of drones, we propose a novel truck-and-drone coordinated delivery system that does not require direct human contact during the delivery process. All final deliveries are completed by the drone while the truck acts as a movable charging station and a carrier, so that the contagion risks are reduced. Moreover, we spilt the whole delivery problem into the customer clustering problem and the routing problems of both the truck and the drone. An encoder-decoder framework combined with reinforcement learning is created to solve the routing problems without handcrafted designed heuristics. We design the different problem contexts specific to the truck routing problem and the drone routing problem. The experimental results show the proposed approach has good generality and can consequently be applied to problems of different scales with high time efficiency.