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Combining Simulation and Optimization for Extended Double Row Facility Layout Problems in Factory Planning

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Simulation Science (SimScience 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 889))

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We investigate the so called Double Row Facility Layout Problem (DRFLP). Given a set of departments with given lengths and pairwise transport weights between them, the aim is to assign the departments to two rows such that the weighted sum of the distances between them is minimized and such that the departments do not overlap. The DRFLP is known to be rather challenging. Even with the best approach known in literature, which is based on an enumeration over all row assignments of the departments and where only the center-to-center distances are measured, the largest instance solved to optimality contains only 16 departments. In this paper we show how the existing models can be extended in various directions in order to handle more aspects that are important in real-world applications such as vertical distances between the departments and restricting the size of the layout area. We also show how the structure of real-world instances, which often contain several departments of the same type, can be exploited in mathematical optimization. This allows us to solve a realistic instance with 21 departments in reasonable time. Furthermore, we propose a new approach which combines optimization and simulation. Here simulation allows the evaluation of the optimized solutions with respect to several performance indicators which play an important role for a smooth production apart from the weighted transport distances. If problems are detected, this information is included in the mathematical models by extending these.

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This work was supported by the Simulation Science Center Clausthal-Göttingen.

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Correspondence to Mirko Dahlbeck .

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Bracht, U., Dahlbeck, M., Fischer, A., Krüger, T. (2018). Combining Simulation and Optimization for Extended Double Row Facility Layout Problems in Factory Planning. In: Baum, M., Brenner, G., Grabowski, J., Hanschke, T., Hartmann, S., Schöbel, A. (eds) Simulation Science. SimScience 2017. Communications in Computer and Information Science, vol 889. Springer, Cham.

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  • Print ISBN: 978-3-319-96270-2

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