Managing Financial Risk in Planning under Uncertainty(Barbaro and Bagajewicz)-04

Managing Financial Risk in Planning under Uncertainty(Barbaro and Bagajewicz)-04

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Managing Financial Risk in Planning under Uncertainty Andres Barbaro and Miguel J. Bagajewicz School of Chemical Engineering and Materials Science, University of Oklahoma, Norman, OK 73019 DOI 10.1002/aic.10094 Published online in Wiley InterScience (www.interscience.wiley.com). A methodology is presented to include fnancial risk management in the Framework oF two-stage stochastic programming For planning under uncertainty. A known probabilistic defnition oF fnancial risk is adapted to be used in this Framework and its relation to downside risk is analyzed. Using these defnitions, new two-stage stochastic programming models that manage fnancial risk are presented. Computational issues related to these models are also discussed. © 2004 American Institute of Chemical Engineers AIChE J, 50: 963–989, 2004 Keywords: planning, fnancial risk, robust optimization Introduction Planning under uncertainty is a common class of problems found in process systems engineering. Some examples widely found in the literature are capacity expansion, scheduling, supply chain management, resource allocation, transportation, unit commitment, and product design problems. The Frst stud- ies on planning under uncertainty could be accredited to Dantzig (1955) and Beale (1955), who proposed the two-stage stochastic models with recourse, which provide the mathemat- ical framework for this article. The industrial importance of planning process capacity ex- pansions under uncertainty has been widely recognized and discussed by several researchers (Ahmed and Sahinidis, 2000b; Berman and Ganz, 1994; Eppen et al., 1989; Liu and Sahinidis, 1996; Murphy et al., 1987; Sahinidis et al., 1989). In the majority of industrial applications, capacity expansion plans require considerable amount of capital investment over a long- range time horizon. Moreover, the inherent level of uncertainty in forecast demands, availabilities, prices, technology, capital, markets, and competition make these decisions very challeng- ing and complex. Therefore, several approaches were proposed to formulate and solve this problem. They mainly differ in the way uncertainty is handled, the robustness of the plans, and their flexibility. This article follows the two-stage stochastic programming approach with discretization of the uncertainty space by random sampling of the parameter probability distri- butions. In turn, the feasibility constraints for the problem are enforced for every scenario in a deterministic fashion (taking recourse actions with an associated cost) such that the resulting plan or design is feasible under every possible uncertainty realization. A formal two-stage stochastic model for capacity planning in the process industry was presented by Liu and Sahinidis (1996) as an extension of the deterministic models developed by Sahinidis et al. (1989). In the two-stage stochastic approach, it is assumed that the capacity expansion plan is decided before the actual realization of uncertain parameters ( scenarios ), al- lowing only some operational recourse actions to take place to improve the objective and correct any infeasibility. In this
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 09/05/2011 for the course CHE 5480 taught by Professor Staff during the Spring '11 term at OKCU.

Page1 / 27

Managing Financial Risk in Planning under Uncertainty(Barbaro and Bagajewicz)-04

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online