Matheuristic algorithms for the parallel drone scheduling traveling salesman problem

In a near future drones are likely to become a viable way of distributing parcels in a urban environment. In this paper we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of...

Full description

Saved in:
Bibliographic Details
Published in:Annals of operations research Vol. 289; no. 2; pp. 211 - 226
Main Authors: Dell’Amico, Mauro, Montemanni, Roberto, Novellani, Stefano
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2020
Springer
Springer Nature B.V
Subjects:
Algorithms
Analysis
Business and Management
Combinatorics
Customers
Distribution
Distribution channels
Drone aircraft
Drone vehicles
Heuristic programming
Integer programming
Linear programming
Management
Mixed integer
Operations research
Operations Research/Decision Theory
Original Research
Scheduling
Scheduling (Management)
Technology application
Theory of Computation
Traveling salesman problem
Urban environments
Italy
Drone-assisted deliveries
Traveling salesman problem
Mixed integer linear programming
Heuristic algorithms
Matheuristics
ISSN:0254-5330, 1572-9338
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In a near future drones are likely to become a viable way of distributing parcels in a urban environment. In this paper we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of minimizing the total time required to service all the customers. We present a set of matheuristic methods for the problem. The new approaches are validated via an experimental campaign on two sets of benchmarks available in the literature. It is shown that the approaches we propose perform very well on small/medium size instances. Solving a mixed integer linear programming model to optimality leads to the first optimality proof for all the instances with 20 customers considered, while the heuristics are shown to be fast and effective on the same dataset. When considering larger instances with 48 to 229 customers, the results are competitive with state-of-the-art methods and lead to 28 new best known solutions out of the 90 instances considered.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-020-03562-3