Metaheuristics for the team orienteering problem

Abstract

The Team Orienteering Problem (TOP) is the generalization to the case of multiple tours of the Orienteering Problem, known also as Selective Traveling Salesman Problem. A set of potential customers is available and a profit is collected from the visit to each customer. A fleet of vehicles is available to visit the customers, within a given time limit. The profit of a customer can be collected by one vehicle at most. The objective is to identify the customers which maximize the total collected profit while satisfying the given time limit for each vehicle. We propose two variants of a generalized tabu search algorithm and a variable neighborhood search algorithm for the solution of the TOP and show that each of these algorithms beats the already known heuristics. Computational experiments are made on standard instances.

Planning for opportunistic surveillance with multiple robots

“…A tabu search heuristic procedure is developed in [19]. Two variants of a tabu search heuristic and a slow and fast Variable Neighbourhood Search (VNS) algorithm is given in [20]. Four variants of an ant colony optimization approach for the TOP is developed in [21].…”

Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs

“…The TOP appeared first under the name Multiple Tour Maximum Collection Problem in [25], but the definition of TOP was introduced by Chao et al in [26] that proposed a heuristic algorithm. Tabu search approaches have been recently presented by [27,28].…”

A Randomized Population Constructive Heuristic for the Team Orienteering Problem

“…This method generates better solution with more computational time compared to (Chao et al, 1996) for a number of instances. (Archetti et al, 2007) addressed a variable neighborhood approach which outperforms (Tang and Miller-Hooks, 2005;Chao et al, 1996) approaches. Ke et al (2008) proposed an Ant Colony Approach that embedded one of the three following methods: Sequential deterministic-concurrent method, randomconcurrent method and simultaneous method.…”

“…Ke et al (2008) proposed an Ant Colony Approach that embedded one of the three following methods: Sequential deterministic-concurrent method, randomconcurrent method and simultaneous method. The performance of the Ant Colony Approach with the sequential method is comparable to (Archetti et al, 2007). Vansteenwegen et al (2009) introduced the disturb method which combines a guided local search method and a diversifying mechanism.…”