GRASP-ILS and set cover hybrid heuristic for the synchronized team orienteering problem with time windows
Résumé
Wildfires are a natural phenomenon that occurs regularly in many terrestrial ecosystems. Due to global warming, the rate and the span of wildfires have remarkably increased during the last years, causing important economic losses as well as human casualties. In the last years, several initiatives have been undertaken in order to apply operations research tools to help firefighting teams schedule and optimize their protection activities when fighting wildfires. In this context, a recent variant of the Team Orienteering Problem (TOP) called the Asset Protection Problem (APP) has been proposed in [21]. In this problem, firefighting teams should provide a protective service to a set of assets endangered by wildfires. These activities can be performed by a heterogeneous fleet of vehicles and should occur within specific time intervals estimated based on the firefronts progression. This variant of the TOP incorporates three additional constraints, namely, time windows, synchronized visits and compatibility constraints between vehicles and assets. In this paper, we propose a hybrid approach that combines a Greedy Randomized Adaptive Search Procedure coupled with an Iterated Local Search (GRASP×ILS) and a post-optimization phase that consists of a Set Covering Problem formulation to efficiently solve the problem. Interestingly, the GRSAP×ILS incorporates an adaptive candidate-list based insertion heuristic as well as a Variable Neighborhood Descent (VND) procedure. The post optimization phase is used at the end of the resolution to improve the best solution found by the GRSAP×ILS. Detailed computational tests have been carried out to prove the effectiveness of our method. The results show that our method, called Hybrid-Heuristic, out-performs the methods of the literature, since it achieves an overall improvement of the best known solutions by 8.52% and 7.8% on medium and large-size instances respectively, while maintaining much lesser computational times.
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