Abstract
Real-world car sequencingproblems deal with lots of constraints, which differ in their types and priorities. We evaluate three permutation-based local search algorithms that use different acceptance criteria for moves. The algorithms meet industrial requirements to obtain acceptable solutions in a rather short time. It is essential to employ move operators which can be evaluated quite fast. Further, using different move types enlarges the neighbourhood, thereby decreasing the total number of local optima in the search space. The comparison of the acceptance criteria shows that the greedy approach is inferior to two variants of threshold acceptingthat allow escapingfrom local optima.
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© 2002 Springer-Verlag Berlin Heidelberg
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Puchta, M., Gottlieb, J. (2002). Solving Car Sequencing Problems by Local Optimization. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_14
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DOI: https://doi.org/10.1007/3-540-46004-7_14
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