A metaheuristic framework for stochastic combinatorial optimization problems based on GPGPU with a case study on the probabilistic traveling salesman problem with deadlines |
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Authors: | Dennis Weyland Roberto Montemanni Luca Maria Gambardella |
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Affiliation: | IDSIA, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Galleria 2, 6928 Manno-Lugano, Switzerland |
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Abstract: | In this work we propose a general metaheuristic framework for solving stochastic combinatorial optimization problems based on general-purpose computing on graphics processing units (GPGPU). This framework is applied to the probabilistic traveling salesman problem with deadlines (PTSPD) as a case study. Computational studies reveal significant improvements over state-of-the-art methods for the PTSPD. Additionally, our results reveal the huge potential of the proposed framework and sampling-based methods for stochastic combinatorial optimization problems. |
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Keywords: | GPGPU CUDA Stochastic combinatorial optimization Stochastic vehicle routing Monte Carlo sampling |
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