Environmental Modelling and Software 2 nd International Congress on Environmental Modelling and Software-Osnabrück , Germany-June 2004 Jul 1 st , 12 : 00 AM Scenario Reoptimisation under Data Uncertainty (original) (raw)
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Scenario Reoptimisation under Data Uncertainty
2004
Many dynamic planning and management problems are typically characterised by a level of uncertainty regarding the value of data input such as supply and demand patterns. Assigning inaccurate values to them could invalidate the results of the study. Consequently, deterministic models are inadequate for the representation of these problems where the most crucial parameters are either unknown or are based on an uncertain future. In these cases, the scenario analysis technique could be an alternative approach. Scenario analysis can model many real problems in which decisions are based on an uncertain future, whose uncertainty is described by means of a set of possible future outcomes, called "scenarios". In this paper we present a scenario analysis approach to dynamic multi-period systems by integrating scenario optimisation and subsequent deterministic reoptimisation. In the scenario optimisation phase we represent data uncertainty by a robust chance optimisation model obtaining a so-called barycentric value with respect to selected decision variables. The successive reoptimisation model based on this barycentric solution allows planning a part of the risk of a wrong decision, reducing the negative consequences deriving from it.
Decision making under extreme uncertainty: blending quantitative modeling and scenario planning
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Sometimes the best view of the future requires an odd pair of bifocals: quantitative modeling and scenario planning. Quantitative modeling is designed to support relatively near-term, tactical decision making, while scenario planning is a planning tool to develop insights about the longer term. These two analytical approaches to decision making are typically not used together. But some planning challenges justify using both to get a better read on possible discontinuity. For example, this case study of a subsidiary of a multinational automotive firm operating in extremely uncertain market conditions shows how the two approaches were successfully blended-producing some important lessons during the process.
Scenario analysis: a review of methods and applications for engineering and environmental systems
Environment Systems & Decisions, 2013
Changing environment, uncertain economic conditions, and socio-political unrest have renewed interest in scenario analysis, both from theoretical and applied points of view. Nevertheless, neither the processes for scenario analysis (SA) nor evaluation criteria and metrics have been regularized. In this paper, SA-reported applications and implementation methodology are discussed in the context of an extensive literature review covering papers published between 2000 and 2010. Over 340 papers were identified through a series of queries in the web of science database. The papers were classified based on the North American Industrial Classification System and SA application goals (environmental, business, and social). SA methodology used in each paper was assessed based on four main criteria: coverage, consistency, uncertainty assessment, and efficiency. We find a significant increase in SA applications, especially in the environmental field. Theoretical developments in the field represent a small fraction of published studies and do not increase in time. The methods used to develop different scenarios vary widely across the academic literature and applications reviewed. Similarly, the methods and data used to characterize the scenarios and develop response strategies are extremely diverse and are limited by factors such as computational tractability and available time and resources. Based on this review, we recommend a regular process for scenario analysis that includes the steps of analysis, scenario definition, and evaluation.
Quantifying Uncertainty to Plan in Dynamic Environments
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Uncertainty is defined by the lack of information to know the future state of a system. This definition highlights the importance of information in an era of constant change and turbulence. It is not surprising that greater the uncertainty in the environment, the greater the value and importance that the management of information takes. Even so, information, being such a valuable resource, is treated empirically and qualitatively although there are formulas to quantify it. This article seeks to provide tools to quantify uncertainty so that it can be included in the planning process and scenario projections.
A DSS for water resources management under uncertainty by scenario analysis
Environmental Modelling and Software, 2005
Abstract: In this paper we present a scenario analysis approach to perform water system planning and management under climatic and hydrological uncertainty friendly data - input phase and results analysis Different generation techniques can be used to set up and analyze a number of scenarios modeled by a scenario - tree in a multistage environment, which includes different possible configurations
Scenario Analysis in Water Resources Management Under Data Uncertainty
2000
In water resources management problems, uncertainty is mainly associated with the value of hydrological exogenous inflows and demand patterns. Deterministic models are inadequate to represent these problems and traditional stochastic optimization models cannot be used if there is insufficient statistical information to support the model. In this paper the uncertainty is modelled by a scenario approach in a multistage environment
An approach to deal with uncertainty in energy and environmental planning: the MARKAL case
Environmental Modeling and …, 2000
This paper presents a new concept to include uncertainty management in energy and environmental planning models developed in algebraic modeling languages. SETSTOCH is a tool for linking algebraic modeling languages with specialized stochastic programming solvers. Its main role is to retrieve from the modeling language a dynamically ordered core model (baseline scenario) that is sent automatically to the stochastic solver. The case presented herein concerns such a study realized with the IEA-MARKAL model used by many research teams around the world.
Directions in scenario planning literature-A review of the past decades
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Annals of Operations Research, 1991
Uncertainty in the parameters of a mathematical program may present a modeller with considerable difficulties. Most approaches in the stochastic programming literature place an apparent heavy data and computational burden on the user and as such are often intractable. Moreover, the models themselves are difficult to understand. This probably explains why one 9 seldom sees a fundamentally stochastic model being solved using stochastic programming techniques. Instead, it is common practice to solve a deterministic model with different assumed scenarios for the random coefficients. In this paper we present a simple approach to solving a stochastic model, based on a particular method for combining such scenario solutions into a single, feasible policy. The approach is computationally simple and easy to understand. Because of its generality, it can handle multiple competing objectives, complex stochastic constraints and may be applied in contexts other than optimization. To illustrate our model, we consider two distinct, important applications: the optimal management of a hydro-thermal generating system and an application taken from portfolio optimization.
A formal framework for scenario development in support of environmental decision-making
2009
Scenarios are possible future states of the world that represent alternative plausible conditions under different assumptions. Often, scenarios are developed in a context relevant to stakeholders involved in their applications since the evaluation of scenario outcomes and implications can enhance decision-making activities. This paper reviews the state-of-the-art of scenario development and proposes a formal approach to scenario development in environmental decision-making.