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Expanding neighborhood search–GRASP for the probabilistic traveling salesman problem

Marinakis Ioannis, Pardalos, P. M, Migdalas, Athanasios

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URI: http://purl.tuc.gr/dl/dias/2188C109-C5BC-45E1-B7E4-919C185CC1B8
Year 2008
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation Y. Marinakis, A. Migdalas , P.M. Pardalos,"Expanding neighborhood search - GRASP for the probabilistic traveling salesman problem," Optim. Letters,vol. 2,no. 3,pp. 351-361,Ju. 2008.doi:10.1007/s11590-007-0064-3 https://doi.org/10.1007/s11590-007-0064-3
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Summary

The Probabilistic Traveling Salesman Problem is a variation of the classic traveling salesman problem and one of the most significant stochastic routing problems. In probabilistic traveling salesman problem only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a variant of the well-known Greedy Randomized Adaptive Search Procedure (GRASP), the Expanding Neighborhood Search–GRASP, is proposed for the solution of the probabilistic traveling salesman problem. expanding neighborhood search–GRASP has been proved to be a very efficient algorithm for the solution of the traveling salesman problem. The proposed algorithm is tested on a numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in six out of ten cases the proposed algorithm gives a new best solution.

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