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2021 | OriginalPaper | Buchkapitel

Combining Constraint Programming and Temporal Decomposition Approaches - Scheduling of an Industrial Formulation Plant

verfasst von : Christian Klanke, Dominik R. Bleidorn, Vassilios Yfantis, Sebastian Engell

Erschienen in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research

Verlag: Springer International Publishing

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Abstract

This contribution deals with the development of a Constraint Programming (CP) model and solution strategy for a two-stage industrial formulation plant with parallel production units for crop protection chemicals. Optimal scheduling of this plant is difficult: a high number of units and operations have to be scheduled while at the same time a high degree of coupling between the operations is present due to the need for synchronizing charging and discharging operations.

In the investigated problem setting the formulation lines produce several intermediates that are filled into a variety of types of final containers by filling stations. Formulation lines and filling stations each consist of parallel, non-identical sets of equipment units. Buffer tanks are used to decouple the two stages, to increase the capacity utilization of the overall plant.

The CP model developed in this work solves small instances of the scheduling problem monolithically. To deal with large instances a decomposition algorithm is developed. The overall set of batches is divided into subsets which are scheduled iteratively. The algorithm is designed in a moving horizon fashion, in order to counteract the disadvantages of the limited lookahead that order-based decomposition approaches typically suffer from. The results show that the complex scheduling problem can be solved within acceptable solution times and that the proposed moving horizon strategy (MHS) yields additional benefits in terms of solution quality.

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Elkamel, A., Zentner, M., Pekny, J.F., Reklaitis, G.V.: A decomposition heuristic for scheduling the general batch chemical plant. Eng. Optim. 28(4), 299–330 (1997). https://doi.org/10.1080/03052159708941137CrossRef

Harjunkoski, I., et al.: Scope for industrial applications of production scheduling models and solution methods. Comput. Chem. Eng. 62, 161–193 (2014). https://doi.org/10.1016/j.compchemeng.2013.12.001

Kovács, A., Beck, J.C.: A global constraint for total weighted completion time for unary resources. Constraints 16(1), 100–123 (2011). https://doi.org/10.1007/s10601-009-9088-xMathSciNetCrossRefMATH

Ku, W.Y., Beck, J.C.: Mixed integer programming models for job shop scheduling: a computational analysis. Comput. Oper. Res. 73, 165–173 (2016). https://doi.org/10.1016/j.cor.2016.04.006MathSciNetCrossRefMATH

Laborie, P.: An update on the comparison of MIP, CP and hybrid approaches for mixed resource allocation and scheduling. In: van Hoeve, W.-J. (ed.) CPAIOR 2018. LNCS, vol. 10848, pp. 403–411. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93031-2_29CrossRefMATH

Lenstra, J.K., Rinnooy Kan, A.H., Brucker, P.: Complexity of machine scheduling problems. Ann. Disc. Math. 1(C), 343–362 (1977). https://doi.org/10.1016/S0167-5060(08)70743-XMathSciNetCrossRefMATH

Leung, J.Y.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. CRC Press (2004)

Méndez, C.A., Cerdá, J., Grossmann, I.E., Harjunkoski, I., Fahl, M.: State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput. Chem. Eng. 30(6–7), 913–946 (2006). https://doi.org/10.1016/j.compchemeng.2006.02.008CrossRef

Perron, L., Furnon, V.: Or-tools https://developers.google.com/optimization/

10.

Simonis, H., Cornelissens, T.: Modelling producer/consumer constraints. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 449–462. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60299-2_27CrossRef

11.

Wari, E., Zhu, W.: A constraint programming model for food processing industry: a case for an ice cream processing facility. Int. J. Prod. Res. 57(21), 6648–6664 (2019). https://doi.org/10.1080/00207543.2019.1571250CrossRef

12.

Wu, D., Ierapetritou, M.G.: Decomposition approaches for the efficient solution of short-term scheduling problems. Comput. Chem. Eng. 27(8–9), 1261–1276 (2003). https://doi.org/10.1016/S0098-1354(03)00051-6CrossRef

13.

Yfantis, V., Corominas, F., Engell, S.: Scheduling of a consumer goods production plant with intermediate buffer by decomposition and mixed-integer linear programming. IFAC-PapersOnLine 52(13), 1837–1842 (2019). https://doi.org/10.1016/j.ifacol.2019.11.469CrossRef

14.

Yfantis, V., Siwczyk, T., Lampe, M., Kloye, N., Remelhe, M., Engell, S.: Iterative medium-term production scheduling of an industrial formulation plant. Comput. Aided Chem. Eng. 46, 19–24 (2019). https://doi.org/10.1016/B978-0-12-818634-3.50004-7CrossRef

Titel: Combining Constraint Programming and Temporal Decomposition Approaches - Scheduling of an Industrial Formulation Plant
verfasst von: Christian Klanke
Dominik R. Bleidorn
Vassilios Yfantis
Sebastian Engell
Verlag: Springer International Publishing
Buch: Integration of Constraint Programming, Artificial Intelligence, and Operations Research
Print ISBN: 978-3-030-78229-0

Electronic ISBN: 978-3-030-78230-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-78230-6_9

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