Abstract
In this paper we deal with different models of production lines of factories and consider the makespan optimization problem based on these models. We consider state-of-the-art and novel mathematical optimizers including exact and heuristic methods. We apply these optimizers to both standard academic and industrial data sets. We see that in a large rate of the considered cases the novel exact optimizers converged to the optimum fast which is surprising being the problems NP-hard and the problem sizes big. This shows the importance of exploiting the structure present in the industrial data using standardized industrial data sets for testing mathematical algorithms devoted to solve industrial problems and that some provided exact mathematical optimizers are fast and perform accurately on the considered industrial problems.
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Hajba, T., Horváth, Z., Kiss-Tóth, C., Jósvai, J. (2017). Production Line Optimization with Model Based Methods. In: Ghezzi, L., Hömberg, D., Landry, C. (eds) Math for the Digital Factory. Mathematics in Industry(), vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-63957-4_8
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DOI: https://doi.org/10.1007/978-3-319-63957-4_8
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