Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant

Guillermo Andrés Durand, Marta Susana Moreno, Fernando Daniel Mele, Jorge Marcelo Montagna, Alberto Bandoni

Resumo


This paper addresses the comparison between two techniques for the optimization under parametric uncertainty of multiproduct batch plants integrating design and production planning decisions. This problem has been conceived as a two-stage stochastic Mixed Integer Linear Programming (MILP) in which the first-stage decisions consist of design variables that allow determining the batch plant structure, and the second-stage decisions consist of production planning continuous variables in a multi-period context. The objective function maximizes the expected net present value. In the first solving approach, the problem has been tackled through mathematical programming considering a discrete set of scenarios. In the second solving approach, the multi-scenario MILP problem has been reformulated by adopting a simulation-based optimization scheme to accommodate the variables belonging to different management levels. Advantages and disadvantages of both approaches are demonstrated through a case study. Results allow concluding that a simulation-based optimization strategy may be a suitable technique to afford two-stage stochastic programming problems.

Palavras-chave


Uncertainty; Two-stage stochastic programming; Simulation-based optimization

Texto completo:

PDF/A (English)

Referências


BALASUBRAMANIAN, J.; GROSSMANN, I.E. Approximation to multistage stochastic optimiza-tion in multiperiod batch plant scheduling under demand uncertainty. Industrial and Engineering Chemistry Research, v. 43, p. 3695-3713, 2004.

BIRGE, Z.; LOUVEAUX, S. Principles on Stochastic Programming. New York (USA): Springer-Verlag, 1997.

CHENG, L.; SUBRAHMANIAN, E.; WESTERBERG, A.W. Design and planning under uncertainty: Issues on problem formulations and solutions. Computers & Chemical Engineering, v. 27, p. 781-801, 2003.

CHENG, L.; SUBRAHMANIAN, E.; WESTERBERG, A.W. A comparison of optimal control and stochastic programming from a formulation and computation perspective. Computers & Chemical Engineering, v. 29, p. 149-164, 2004a.

CHENG, L.; SUBRAHMANIAN, E.; WESTERBERG, A.W. Multiobjective decisions on capacity planning and inventory control. Industrial and Engineering Chemistry Research, v. 43, p. 2192-2208 2004b.

DIWEKAR, U. Optimization under Uncertainty: an overview. SIAG/OPT Views-and-News, v. 13, n. 1, p.1-8, 2002.

DUENAS, A.; PETROVIC, D. An approach to predictive-reactive scheduling of parallel ma-chines subject to disruptions. Annals of Operations Research, v. 159, n. 1, p. 65-82, 2000.

DURAND, G.A.; MELE F.D.; GUILLÉN-GOSÁLBEZ G.; BANDONI J.A. Design and planning of the bioethanol supply chain via simulation-based optimization: The case of Argentina, 1º Simposio Argentino de Informática Industrial, Proceedings,… 41º JAIIO. Presentación oral. Edición en CD. Páginas 72-81. La Plata (Argentina), 2012.

DURAND, G.A.; MELE, F.D.; BANDONI, J.A. Determination of storage tanks location for optimal short-term scheduling in multipurpose/multiproduct batch-continuous plants under uncertainties. Ann. Oper. Res. DOI: 10.1007/s10479-011-0970-8, 2011.

JUNG, J.Y.; BLAU, G.; PEKNY, J. F.; REKLAITIS, G.V.; EVERSDYK, D. A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, v. 28, p. 2087-2106, 2004.

KUSTER, J.; JANNACH, D.; FRIEDRICH, G. Applying Local Rescheduling in response to schedule disruptions. Annals of Operations Research, v. 180, n. 1, p. 265-282, 2010.

MORENO, M.S.; MONTAGNA, J.M.; IRIBARREN, O.A. Multiperiod optimization for the design and planning of multiproduct batch plants. Computers & Chemical Engineering, v. n. 31, p. 1159-1173, 2007.




e-ISSN 2175-8018


Creative Commons License
IJIE - Iberoamerican Journal of Industrial Engineering foi licenciada sob uma Licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


Iberoamerican Journal of Industrial Engineering. Universidade Federal de Santa Catarina. Departamento de Engenharia de Produção e Sistemas. Florianópolis, SC, Brasil.

Para entrar em contato com a equipe editorial do IJIE, encaminhe um e-mail para periodico.ijie@gmail.com ou ijie@contato.ufsc.br