287949 Short-Term Scheduling of Multiproduct Batch Plants: A Novel CP Approach

Tuesday, October 30, 2012: 5:27 PM
326 (Convention Center )
Franco Novara, Industrial Engineering, Universidad Nacional del Litoral, Santa Fe, Argentina, Juan M. Novas, INTEC (UNL-CONICET), Santa Fe, Argentina and Gabriela P. Henning, Universidad Nacional del Litoral, CONICET, Santa Fe, Argentina

Short-Term Scheduling of Multiproduct Batch Plants: A Novel CP Approach

Franco M. Novara#*, Juan M. Novas*, Gabriela P. Henning*  

# Facultad de Ingeniería Química, UNL, Santiago del Estero 2829, 3000 Santa Fe, Argentina

* INTEC (UNL, CONICET), Güemes 3450, 3000 Santa Fe, Argentina

                This contribution addresses the short-term scheduling problem of multiproduct multistage batch plants by means of a novel Constraint Programming (CP) methodology. This problem has been extensively studied in the last decade by several authors, by resorting to various solution approaches (Méndez et al., 2001; Harjunkoski and Grossmann, 2002; Castro and Grossmann, 2005; Castro et al., 2006, Castro et al., 2009; Liu and Karimi, 2007a, 2007b, 2008; Marchetti and Cerdá, 2009; Zeballos et al., 2011). Most of the available solution procedures assume that the batching problem has been solved beforehand (Maravelias, 2012), and so does this work. This means that no batching decisions are made, and that the solution approach focuses on unit assignment, batch sequencing and timing decisions (as well as on the proper handling of some limited renewable resources), while optimizing a time-based or cost-based objective function.

                The proposed approach is a totally new CP model, which is based on the ILOG-IBM OPL language and the CP Optimizer (ILOG-IBM, 2012). It takes advantage of some popular CP constraints, like cumulative functions, as well as other user-defined ones. The formulation can easily handle different features found in industrial environments, like production orders requiring several batches of each product, which are addressed in a campaign mode. When these campaigns are run, upper and lower bound limits on the number of batches per product campaign need to be complied with. The proposed CP model can also take into account the existence of dissimilar parallel equipment at each stage, topology constraints, forbidden job-equipment assignments, order release times, finite unit ready times, as well as limitations on renewable discrete resources, like heating and cooling utilities, manpower, etc. Regarding discrete resources, the approach can also address sequence-dependent changeover tasks that require limited cleaning resources. With respect to inter-stage storage and operational policies, the proposal considers UIS, NIS/ZW and NIS/UW, as well as mixed ones.

                 The approach has been extensively tested by means of several literature examples having various difficulty degrees. The formulation has rendered good computational results for a variety of inter-stage storage policies, campaign sizes and objective functions. Results that compare this approach with others are presented and discussed. Additionally, scalability studies are reported.

 References

 Castro, P.M., & Grossmann, I.E. (2005). New Continuous-Time MILP Model for the Short-Term Scheduling of Multistage Batch Plants. Ind. Eng. Chem. Res., 44, 9175-9190.

Castro, P.M., Grossmann, I.E., & Novais, A.Q. (2006). Two New Continuous-Time Models for the Scheduling of Multistage Batch Plants with Sequence Dependent Changeovers. Ind. Eng. Chem. Res., 45, 6210-6226.

Castro, P.M., Harjunkoski, I., & Grossmann I.E. (2009). Optimal Short-Term Scheduling of Large-Scale Multistage Batch Plants. Ind. Eng. Chem. Res., 48, 11002–11016.

Harjunkoski, I., & Grossmann, I.E. (2002). Decomposition Techniques for Multistage Scheduling Problems Using Mixed-integer and Constraint Programming Methods. Computers and Chemical Engineering, 26, 1533-1552.

ILOG-IBM CP Optimizer, http://www-01.ibm.com/software/integration/optimization/cplex-cp-optimizer/

Liu, Y., & Karimi, I.A. (2007a). Scheduling multistage, multiproduct batch plants with non identical parallel units and unlimited intermediate storage, Chemical Engineering Science, 62, 1549-1566.

Liu, Y., & Karimi, I.A. (2007b). Novel continuous-time formulations for scheduling multi-stage batch plants with identical parallel units. Computers and Chemical Engineering, 31, 1671-1693.

Liu, Y., & Karimi, I.A. (2008). Scheduling multistage batch plants with parallel units and no interstage storage. Computers and Chemical Engineering, 32, 671-693.

Maravelias, C., (2012). General Framework and Modeling Approach Classification for Chemical Production Scheduling, AIChE Journal. In Press

Marchetti, P.A., & Cerdá, J. (2009). A general resource-constrained scheduling framework for multistage batch facilities with sequence-dependent changeovers. En: Computers and Chemical Engineering, 33, 871–886.

Méndez C.A., Henning, G.P., & Cerdá, J. (2001). An MILP continuous time approach to short-term scheduling of resource-constrained multistage flowshop batch facilities. Computers and Chemical Engineering, 25, 701–711.

Zeballos, L.J., Novas, J.M., & Henning, G.P. (2011). A CP formulation for scheduling multiproduct multistage batch plants. Computers and Chemical Engineering, 35, 2973– 2989.


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