Abstract
In this paper a decision support framework, for the production scheduling problem, is presented. It will help with the allocation of jobs to machines and sequence them, in order to minimize a performance measure. The proposed framework enables to solve different kind of scheduling problems, including Job-Shop problems, which consist of a set of operations that must be performed on a predetermined number of machines, with predetermined production times. Job-Shop problems are NP-hard and the proposed framework enables to solve these problems through one among several Meta-Heuristics put available and selected by the user. The proposed framework is quite flexible, particularly regarding its ability to solve scheduling problems occurring in different kind of machine environments, and by selecting multiple performance measures, which makes it more attractive than other existing and more problem specific approaches. In this paper the proposed framework is illustrated through the use of Simulated Annealing, but several other Meta-Heuristics are implemented in the prototype. In order to demonstrate the framework’s potential, instances of Flow-Shop and Job-Shop will be presented in detail, both based on well-known, benchmark problems. The viability of the proposed framework for real-world application is also analysed, by highlighting several of its main advantages and disadvantages.
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Acknowledgement
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and UIDP/04077/2020.
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Reis, P., Santos, A.S., Bastos, J., Madureira, A.M., Varela, L.R. (2021). A Production Scheduling Support Framework. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_80
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