Abstract
Minimizing total tardiness on identical parallel machines is an \(\mathcal {NP}\)-hard parallel machine scheduling problem that has received much attention in literature due to its direct application to real-world applications. For solving this problem, we present a variable neighbourhood search that incorporates a learning mechanism for guiding the search. Computational results comparing with the best approaches for this problem reveals that our algorithm is a suitable alternative to efficiently solve this problem.
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Acknowledgements
This work has been partially funded by the European Regional Development Fund, the Spanish Ministry of Economy and Competitiveness (project TIN2012-32608). Eduardo Lalla-Ruiz thanks the Canary Government for the financial support he receives through his doctoral grant.
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Lalla-Ruiz, E., Voß, S. (2015). Minimizing Total Tardiness on Identical Parallel Machines Using VNS with Learning Memory. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_10
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DOI: https://doi.org/10.1007/978-3-319-19084-6_10
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