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Optimization and Engineering

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19.02.2016

An uncertainty management framework for industrial applications

verfasst von: Danielle Zyngier

Erschienen in: Optimization and Engineering | Ausgabe 1/2017

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Abstract

Uncertainty affects industrial systems in many ways. It may describe the amount of system variability. It may influence the way we make decisions. This paper provides a novel framework that maps the effects of uncertainty to existing mathematical methods. In addition, new techniques to assess, adjust and abate parametric uncertainty are presented. Due to the computational burden associated with solving uncertain models of large-scale industrial processes, some simplification techniques may be required, including scenarios with the different parameter realizations. Issues regarding scenario generation, comparison, assessment, management and practical implementation are also discussed.

Vorheriger Artikel Overview of estimation methods for industrial dynamic systems

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Averbakh I (2000) Minimax regret solutions for minimax optimization problems with uncertainty. Oper Res Lett 27:57–65MathSciNetCrossRefMATH

Ben-Tal A, Nemirovski A (1998) Convex optimization in engineering: modeling, analysis, algorithms. Research Report, Technion, IsraelMATH

Ben-Tal A, Nemirovski A (2000) Robust solutions of linear programming problems contaminated with uncertain data. Math Progr 88:411–424MathSciNetCrossRefMATH

Ben-Tal A, Nemirovski A (2001) On polyhedral approximations of the second-order cone. Math Oper Res 26(2):193–205MathSciNetCrossRefMATH

Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis forecasting and control, 3rd edn. Prentice-Hall Inc, Englewood CliffsMATH

Charnes A, Cooper WW (1963) Deterministic equivalents for optimizing and satisficing under chance constraints. Oper Res 11(1):18–39MathSciNetCrossRefMATH

Chinneck JW, Ramadan K (2000) Linear programming with interval coefficients. J Oper Res Soc 51:209–220CrossRefMATH

Dembo R (1991) Scenario optimization. Ann Oper Res 30:63–80MathSciNetCrossRefMATH

Draper NR, Smith H (1998) Applied regression analysis, 3rd edn. Wiley, HobokenMATH

Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, NorwellCrossRefMATH

Grossmann IE, Floudas CA (1987) Active constraint strategy for flexibility analysis in chemical processes. Comp Chem Eng 11:675–693CrossRef

Halemane KP, Grossmann IE (1983) Optimal process design under uncertainty. AIChE J 29:425–433MathSciNetCrossRef

Ierapetritou MG, Pistikopoulos EN, Floudas CA (1996) Operational planning under uncertainty. Comp Chem Eng 20(12):1499–1516CrossRef

Kelly J D, Zyngier D (2008a) Continuously improve the performance of planning and scheduling models with parameter feedback. Presented at the Foundations of Computer-Aided Process Operations (FOCAPO) 2008, Massachusetts, USA, pp 459–462

Kelly JD, Mann JL (2003) Crude oil blend scheduling optimization: an application with multimillion dollar benefits. Hydrocarb Process 47–53:72–79

Kelly JD, Zyngier D (2008) Hierarchical decomposition heuristic for scheduling: coordinated reasoning for decentralized and distributed decision-making problems. Comp Chem Eng 32:2684–2705CrossRef

Kelly JD, Zyngier D (2012) Apparatus and method for order generation and management to facilitate solutions of decision-making problems. US Patent 8255348

Koltai T, Terlaky T (2000) The difference between the managerial and mathematical interpretation of sensitivity analysis results in linear programming. Int J Prod Econ 65:257–274CrossRef

Li Z, Ierapetritou M (2008) Process scheduling under uncertainty: review and challenges. Comp Chem Eng 32:715–727CrossRef

Li Z, Ding R, Floudas CA (2011) A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization. Ind Chem Eng Res 50(18):10567–10603CrossRef

Lin X, Janak SL, Floudas CA (2004) A new robust optimization approach for scheduling under uncertainty. Comp Chem Eng 28:1069–1085CrossRef

Rockafellar RT, Wets RJ-B (1991) Scenarios and policy aggregation in optimization under uncertainty. Math Oper Res 16:119–147MathSciNetCrossRefMATH

Sahinidis NV (2004) Optimization under uncertainty: state-of-the-art and opportunities. Comp Chem Eng 28:971–983CrossRef

Sen S, Higle JL (1999) An introductory tutorial on stochastic linear programming models. Interfaces 29(2):33–61CrossRef

Swaney RE, Grossmann IE (1985a) An index for operational flexibility in chemical process design. Part I: formulation and theory. AIChE J 31:621–630CrossRef

Swaney RE, Grossmann IE (1985b) An index for operational flexibility in chemical process design. Part II: computational algorithms. AIChE J 31:631–641CrossRef

Varvarezos DK, Grossmann IE, Biegler LT (1995) A sensitivity based approach for flexibility analysis and design of linear process systems. Comp Chem Eng 19:1301–1316CrossRef

Williams HP (2013) Model building in mathematical programming, 5th edn. Wiley, EnglandMATH

Zyngier D (2006), Ph.D. thesis, McMaster University, Hamilton, ON, Canada

Zyngier D, Marlin T E (2006) Monitoring and improving LP optimization with uncertain parameters. In: Proceedings of the 16th European Symposium on Computer Aided Process Engineering (ESCAPE-16), Garmisch-Partenkirchen, Germany, pp 427–432

Titel: An uncertainty management framework for industrial applications
verfasst von: Danielle Zyngier
Publikationsdatum: 19.02.2016
Verlag: Springer US
Erschienen in: Optimization and Engineering / Ausgabe 1/2017
Print ISSN: 1389-4420
Elektronische ISSN: 1573-2924
DOI: https://doi.org/10.1007/s11081-016-9309-2

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