A hybrid iterated local search heuristic for the traveling salesperson problem with hotel selection

• Published in: Computers & operations research Vol. 129; p. 105229
• Main Authors: Sousa, Marques Moreira de, González, Pedro Henrique, Ochi, Luiz Satoru, Martins, Simone de Lima
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2021; Pergamon Press Inc
• Subjects: Algorithms; Computing time; Data mining; Frequent itemset mining; Hotels & motels; Iterated local search; Metaheuristics; Operations research; Perturbation; Random variable neighborhood descent; Random variables; Traveling salesman problem; Traveling salesperson problem with hotel selection; Frequent itemset mining; Traveling salesperson problem with hotel selection; Metaheuristics; Iterated local search; Random variable neighborhood descent
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2021.105229

•Development of a hybrid heuristic to solve the Traveling Salesperson Problem with Hotel Selection (TSPHS).•A new variable perturbation based on instance size for ILS.•Use of data mining strategy.•Metaheuristic results for 240 instances only handled to date with exact algorithms.•Establishment of new best solutions for 129 instances. Companies have given close attention to new ways to reduce operational costs, and a way to achieve this point is focusing on optimizing the planning of routes. A variant of the traveling salesperson problem (TSP), called the traveling salesperson problem with hotel selection, was introduced in the last years. Herein, the salesperson needs to visit all customers just once, ensuring that a length-traveled daily not exceeds a length limit constraint. If necessary, a hotel can be used to connect tours on different days. This work proposes a hybrid Iterated Local Search heuristic using a random variable neighborhood descent procedure with a variable perturbation procedure. Furthermore, to perform better, a data mining technique is sporadically executed to construct new solutions based on patterns extracted by frequent itemset mining. Computational experiments show the potential of this algorithm to significantly improve the number of best known solutions when using the same computational time of the principal works available in literature.