Abstract
We present an extension of a multi-objective algorithm based on Ant Colony Optimisation to solve a more realistic variant of a classical industrial problem: Time and Space Assembly Line Balancing. We study the influence of incorporating some domain knowledge by guiding the search process of the algorithm with preferences-based dominance. Our approach is compared with other techniques, and every algorithm tackles a real-world instance from a Nissan plant. We prove that the embedded expert knowledge is even more justified in a real-world problem.
UPC Nissan Chair as well as the Spanish Government partially funded this work by means of PROTHIUS-II project: DPI2007-63026 including EDRF fundings.
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Chica, M., Cordón, Ó., Damas, S., Pereira, J., Bautista, J. (2008). Incorporating Preferences to a Multi-objective Ant Colony Algorithm for Time and Space Assembly Line Balancing. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_34
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DOI: https://doi.org/10.1007/978-3-540-87527-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87526-0
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