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

Abstract

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.