Drones Routing with Stochastic Demand

Abstract

Motivated by the increasing number of drones used for package delivery, we first study the problem of Multiple drOne collaborative Routing dEsign (MORE) in this article. That is, given a fixed number of drones and customers, determining the delivery trip for drones under capacity constraint with stochastic demand for customers such that the overall expected traveling cost is minimized. To address the MORE problem, we first prove that MORE falls into the realm of the classical vehicle routing problem with stochastic demand and then propose an effective algorithm for MORE. Next, we have a scheme of resplitting customers into different individual delivery trips while the stochastic demands are determined. Moreover, we consider a variety of MORE, MORE-TW, and design an effective algorithm to address it. We conduct simulation experiments for MORE to verify our theoretical findings. The results show that our algorithm outperforms other comparison algorithms by at least 79.60%.

The Development of an Optimal Operation Algorithm for Food Delivery Using Drones Considering Time Interval between Deliveries

“…The payload and battery capacity of the UAV were considered, and a hybrid genetic and simulated annealing (HGSA) approach was presented to solve the problem. Yu et al [11] introduced the scheduling problem of multiple drones, assuming a situation where customer demand occurs stochastically. It was difficult to determine the number of individual drone deliveries when demand was not deterministic, because the capacity of drones is limited.…”

“…The maximum allowable time interval between deliveries is set at 60 s. Therefore, according to Equation (11), the maximum time interval between the first and last delivery is 120 s for two deliveries, and 180 s for three deliveries. The proposed MILP is solved with the commercial mathematical optimization software CPLEX 22.1.…”