Efficient Routing for Precedence-Constrained Package Delivery for Heterogeneous Vehicles

• Published in: IEEE transactions on automation science and engineering Vol. 17; no. 1; pp. 248 - 260
• Main Authors: Bai, Xiaoshan, Cao, Ming, Yan, Weisheng, Ge, Shuzhi Sam
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Computer simulation; Customers; Drone vehicles; Drones; Genetic algorithms; Graph theory; Heterogeneous vehicles; heuristic algorithm; Heuristic algorithms; Job shop scheduling; Lower bounds; Operations research; Optimization; package delivery; Precedence constraints; Production scheduling; Routing; Sorting; Sorting algorithms; Task analysis; task assignment; topological sorting; Trucks
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2019.2914113

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.