Abstract
Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches.
Highlights
With the development of sensing, computing, cognitive radio, and robotic technologies, the application of drones, originated from military industry, is rapidly extending to service, agriculture, public safety networks (Sikeridis et al [1]), and healthcare
It is recognized that drones have a great potential to change the traditional express transportation, due to the fact that (i) a drone can respond rapidly to demand and its delivery is not restricted by the road conditions (Hong et al [2]); (ii) drone delivery will be more cost-effective to get to places where traditional transportation modes would be difficult to reach (Zhang and Kovacs [3]); (iii) employing drones in a delivery system is more environmentally friendly, as drones are powered by electricity and lead to less greenhouse gas emissions
Number, and service module of drones deployed on each route are determined, under each scenario, parcels of each customer on each route are handled by drones and couriers via a heuristic rule
Summary
With the development of sensing, computing, cognitive radio, and robotic technologies, the application of drones, originated from military industry, is rapidly extending to service, agriculture, public safety networks (Sikeridis et al [1]), and healthcare. Motivated by the above observations, this paper studies a stochastic drone fleet deployment and planning (DFDP) problem with uncertain customer parcel demand. The problem involves a two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module on each route on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. (1) This paper studies a new stochastic DFDP with uncertain parcel demand, which determines (i) the drone fleet deployment, i.e., the numbers of different types of drones deployed, (ii) the drone service module, and (iii) the numbers of parcels delivered by drones and couriers under each scenario of demand.
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