A hybrid genetic algorithm for the traveling salesman problem with drone

This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-ti...

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Bibliographic Details
Published in:Journal of heuristics 2020-04, Vol.26 (2), p.219-247
Main Authors: Ha, Quang Minh, Deville, Yves, Pham, Quang Dung, Hà, Minh Hoàng
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Artificial Intelligence
Calculus of Variations and Optimal Control
Optimization
Completion time
Crossovers
Drone vehicles
Genetic algorithms
Job shops
Logistics
Management Science
Mathematics
Mathematics and Statistics
Operating costs
Operations Research
Operations Research/Decision Theory
Production scheduling
Searching
Traveling salesman problem
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Summary:This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms two existing methods in terms of solution quality and improves many best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to investigate their sensitivity to the performance of our method.
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-019-09431-y