Robust optimal decisions with stochastic nonlinear economic systems

Becker, Robin; Hall, Stephen G.; Rustem, Berç

doi:10.1016/0165-1889(94)90072-8

Cited by 28 publications

(6 citation statements)

References 4 publications

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“…Policymakers can evaluate the robustness of future policies with this information (e.g. policies that ensure profitability in a range of future developments) (Becker et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Business case uncertainty of power plants in future energy systems with wind power

et al. 2016

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The operation of thermal power plants is affected by CO 2 prices and wind power. A new tool quantifies the effect of their uncertainty on power plant profitability. New power plants are unprofitable and show a large spread in expected profits. Uncertain future climate policy is a key factor in all business cases (7 56% change). Increasing wind power penetration (10-21%) decreases profitability by 14%.

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“…Policymakers can evaluate the robustness of future policies with this information (e.g. policies that ensure profitability in a range of future developments) (Becker et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Business case uncertainty of power plants in future energy systems with wind power

et al. 2016

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“…Hence, these back-off (penalty) terms are being updated at each iteration in the present algorithm. This is a key difference with respect to penalty-based methods, which penalize deviations from a desired behavior through the implementation of constant penalty terms or sum of errors (i.e., sum of squares) in the objective function. ,, The reformulation of the inequality constraints implies that, by shifting away from the nominal operation of the plant (i.e., when Θ̂ unc is considered), dynamic operability of the plant may be ensured, even under the effects of stochastic realizations in the uncertain parameters. Robustness of the solution can be fine-tuned by modifying the user-defined weight parameter λ p , which determines the amount of variability (i.e., back-off terms) that is considered on each constraint g p,t due to model uncertainty. s.t. …”

Section: Back-off Decomposition Algorithmmentioning

confidence: 99%

A Novel Back-off Algorithm for Integration of Scheduling and Control of Batch Processes under Uncertainty

Valdez-Navarro

Ricardez‐Sandoval

2019

Ind. Eng. Chem. Res.

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A novel decomposition algorithm for the integration of scheduling and control of multiproduct, multiunit batch processes under stochastic parameter uncertainty is presented. This iterative algorithm solves a scheduling and dynamic optimization problem around a nominal point while approximating uncertainty through backoff terms, embedded in the operational process constraints. Monte Carlo simulations are performed to propagate uncertainty and to evaluate dynamic feasibility; statistical information is drawn from these simulations to update the back-off terms. Convergence of the algorithm results in a set of scheduling and control decisions that aim to keep the plant dynamically feasible under the effect of uncertainty up to a user-defined tolerance criterion. The proposed algorithm is shown to be successfully applied to a multiproduct, multiunit batch plant under the effects of different probability density functions in the uncertain parameters. The algorithm's performance is gauged against a fully integrated, mixed logic dynamic optimization problem with multiscenario-based uncertainty. The solution to the integrated algorithm is obtained with mixed integer nonlinear programming solvers. Results show that the proposed decomposition algorithm remains computationally attractive, without compromising the quality of the solution.

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“…Apparently, the more estimation errors in the forecast, the less willingness planners are likely to adopt a dynamic order admission policy in practice. In the researches of Becker, Hall, and Rustem (1994) and Balakrishnan et al (1999), they also concluded that if a model is not sensitive to estimation error, it can make the model more risk-averse and more attractive for the planners. Based on these statements, we intend to investigate the impact of different types of estimation error under various order structure.…”

Section: Introductionmentioning

confidence: 95%

The impact of estimation error on the dynamic order admission policy in B2B MTO environments

Wu¹,

Chiang²

2009

Expert Systems with Applications

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Robust optimal decisions with stochastic nonlinear economic systems

Cited by 28 publications

References 4 publications

Business case uncertainty of power plants in future energy systems with wind power

Business case uncertainty of power plants in future energy systems with wind power

A Novel Back-off Algorithm for Integration of Scheduling and Control of Batch Processes under Uncertainty

The impact of estimation error on the dynamic order admission policy in B2B MTO environments

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