Abstract
This paper studies a coordinated system for multidrone single-truck distribution, where a truck delivers goods to a group of customers along a closed ground path with the help of a number of drones. For each delivery, the truck departs from the distribution centre with drones and all goods needed and returns back to the centre after fulfilling the delivery tasks. That is, the truck assigns these delivery tasks to several of its drones, each of which is responsible for sending goods to a different subgroup of customers in the empty air space. This study provides a new mixed-integer programming model of the routing problem with this distribution system based on urban road network. Meanwhile, a hybrid genetic algorithm and a hybrid particle swarm algorithm are designed. Experimental results show that the performance of the hybrid algorithms is better than that of the corresponding basic algorithms.
Highlights
Drones or UAVs (Unmanned Aerial Vehicle) could be used to transport goods such as packages, food, and medicine
A coordinated system for multidrone single-truck distribution means that a truck works with multiple drones to deliver to a set of determined customers. at is, the truck assigns these delivery tasks to several of its drones, each of which is responsible for sending goods to a different subgroup of customers in the empty air space
With the increasing number of decision variables, problem scale, and search space of genetic algorithm (GA) or particle swarm optimization (PSO) for a complex problem, performances of algorithm is impacted negatively due to the slow convergence rate of algorithm and large amount of iterations
Summary
Drones or UAVs (Unmanned Aerial Vehicle) could be used to transport goods such as packages, food, and medicine. Chang and Lee focused on finding an effective delivery route for trucks carrying drones and proposed a new approach on a nonlinear programming model to find shift-weights that move the centres of clusters to make wider drone-delivery areas along shorter truck route after initial K-means clustering and TSP (Traveling Salesman Problem) modelling [20]. Xia et al studied how drones can be scheduled to monitor the sailing vessels in ECAs. ey modelled the dynamics of each sailing vessel using a real-time location function which allows to approximately represent the problem on a timeexpanded network They developed a Lagrangian relaxation-based method to obtain near-optimal solutions [22]. With the increasing size of the problem and the growing number of decision variables, performance of the algorithm gets worse. us, we designed a hybrid algorithm that considers the efficiency and quality of the solution and achieves better computational results at a faster rate
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