The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
G H Huang - One of the best experts on this subject based on the ideXlab platform.
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an interval parameter mean cvar two stage Stochastic Programming approach for waste management under uncertainty
Stochastic Environmental Research and Risk Assessment, 2014Co-Authors: C Dai, X H Cai, Y P Cai, Q Huo, G H HuangAbstract:In this research, approaches of interval mathematical Programming, two-stage Stochastic Programming and conditional value-at-risk (CVaR) are incorporated within a general modeling framework, leading to an interval-parameter mean-CVaR two-stage Stochastic Programming (IMTSP). The developed method has several advantages: (i) it can be used to deal with uncertainties presented as interval numbers and probability distributions, (ii) its objective function simultaneously takes expected cost and system risk into consideration, thus, it is useful for helping decision makers analyze the trade-offs between cost and risk, and (iii) it can be used for supporting quantitatively evaluating the right tail of distributions of waste generation rate, which can better quantify the system risk. The IMTSP model is applied to the long-term planning of municipal solid waste management system in the City of Regina, Canada. The results indicate that IMTSP performs better in its capability of generating a series of waste management patterns under different risk-aversion levels, and also providing supports for decision makers in identifying desired waste flow strategies, considering balance between system economy and environmental quality.
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a two stage inexact Stochastic Programming model for planning carbon dioxide emission trading under uncertainty
Applied Energy, 2010Co-Authors: W T Chen, G H Huang, Xuejiao ChenAbstract:In this study, a two-stage inexact-Stochastic Programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter Programming (IPP) and two-stage Stochastic Programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints.
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a two stage inexact Stochastic Programming model for planning carbon dioxide emission trading under uncertainty
Applied Energy, 2010Co-Authors: W T Chen, G H Huang, Xuejiao ChenAbstract:In this study, a two-stage inexact-Stochastic Programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter Programming (IPP) and two-stage Stochastic Programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints. (C) 2009 Elsevier Ltd. All rights reserved.
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an interval parameter fuzzy Stochastic Programming approach for air quality management under uncertainty
Environmental Engineering Science, 2008Co-Authors: G H Huang, L LiuAbstract:An interval-parameter fuzzy-Stochastic Programming (IPFSP) approach is developed for planning air quality management systems under uncertainty. Fuzzy sets theory is introduced to represent uncertainties existing in various operation costs under different loading conditions. Compared with the existing approaches, the proposed IPFSP performs uniqueness through two special features: one is it could provide more feasible control strategies under different upcoming pollutant amounts, which was seldom considered in the previous research efforts; the other is, as a result of interval-parameter Programming (IPP) and two-stage Stochastic Programming (TSP) being incorporated into the modeling framework, uncertain information expressed as discrete intervals and probability density functions can be effectively reflected. After formulating the model, a representative regional air quality management system is provided for demonstrating its applicability. The results indicate that reasonable solutions are obtained, and ...
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an interval parameter multi stage Stochastic Programming model for water resources management under uncertainty
Advances in Water Resources, 2006Co-Authors: G H Huang, Shaofang NieAbstract:In this study, an interval-parameter multi-stage Stochastic linear Programming (IMSLP) method has been developed for water resources decision making under uncertainty. The IMSLP is a hybrid methodology of inexact optimization and multi-stage Stochastic Programming. It has three major advantages in comparison to the other optimization techniques. Firstly, it extends upon the existing multi-stage Stochastic Programming method by allowing uncertainties expressed as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. Secondly, penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. Thirdly, it cannot only handle uncertainties through constructing a set of scenarios that is representative for the universe of possible outcomes, but also reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. The developed IMSLP method is applied to a hypothetical case study of water resources management. The results are helpful for water resources managers in not only making decisions of water allocation but also gaining insight into the tradeoffs between environmental and economic objectives.
Xuejiao Chen - One of the best experts on this subject based on the ideXlab platform.
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a two stage inexact Stochastic Programming model for planning carbon dioxide emission trading under uncertainty
Applied Energy, 2010Co-Authors: W T Chen, G H Huang, Xuejiao ChenAbstract:In this study, a two-stage inexact-Stochastic Programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter Programming (IPP) and two-stage Stochastic Programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints.
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a two stage inexact Stochastic Programming model for planning carbon dioxide emission trading under uncertainty
Applied Energy, 2010Co-Authors: W T Chen, G H Huang, Xuejiao ChenAbstract:In this study, a two-stage inexact-Stochastic Programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter Programming (IPP) and two-stage Stochastic Programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints. (C) 2009 Elsevier Ltd. All rights reserved.
Alexander Shapiro - One of the best experts on this subject based on the ideXlab platform.
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lectures on Stochastic Programming modeling and theory second edition
2014Co-Authors: Alexander Shapiro, Darinka Dentcheva, Andrzej RuszczynskiAbstract:Optimization problems involving Stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where Stochastic models are available. In Lectures on Stochastic Programming: Modeling and Theory, Second Edition, the authors introduce new material to reflect recent developments in Stochastic Programming, including: an analytical description of the tangent and normal cones of chance constrained sets; analysis of optimality conditions applied to nonconvex problems; a discussion of the Stochastic dual dynamic Programming method; an extended discussion of law invariant coherent risk measures and their Kusuoka representations; and in-depth analysis of dynamic risk measures and concepts of time consistency, including several new results. Audience: This book is intended for researchers working on theory and applications of optimization. It also is suitable as a text for advanced graduate courses in optimization.
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lectures on Stochastic Programming modeling and theory
2009Co-Authors: Alexander Shapiro, Darinka Dentcheva, Andrzej RuszczynskiAbstract:List of notations Preface to the second edition Preface to the first edition 1. Stochastic Programming models 2. Two-stage problems 3. Multistage problems 4. Optimization models with probabilistic constraints 5. Statistical inference 6. Risk averse optimization 7. Background material 8. Bibliographical remarks Bibliography Index.
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on a time consistency concept in risk averse multistage Stochastic Programming
Operations Research Letters, 2009Co-Authors: Alexander ShapiroAbstract:We discuss time consistency of multistage risk averse Stochastic Programming problems. The concept of time consistency is approached from an optimization point of view. That is, at each state of the system optimality of a decision policy should not involve states which cannot happen in the future.
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Stochastic Programming approach to optimization under uncertainty
Mathematical Programming, 2007Co-Authors: Alexander ShapiroAbstract:In this paper we discuss computational complexity and risk averse approaches to two and multistage Stochastic Programming problems. We argue that two stage (say linear) Stochastic Programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We discuss an extension of coherent risk measures to a multistage setting and, in particular, dynamic Programming equations for such problems.
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a Stochastic Programming approach for supply chain network design under uncertainty
European Journal of Operational Research, 2005Co-Authors: Tjendera Santoso, Shabbir Ahmed, Marc Goetschalckx, Alexander ShapiroAbstract:This paper proposes a Stochastic Programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the sample average approximation (SAA) scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale Stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the Stochastic model as well as the efficiency of the proposed solution strategy.
Richard P Oneill - One of the best experts on this subject based on the ideXlab platform.
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reserve requirements for wind power integration a scenario based Stochastic Programming framework
IEEE Transactions on Power Systems, 2011Co-Authors: Anthony Papavasiliou, Shmuel S Oren, Richard P OneillAbstract:We present a two-stage Stochastic Programming model for committing reserves in systems with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting Stochastic program. We test our scenario generation methodology on a model of California consisting of 122 generators, and we show that the Stochastic Programming unit commitment policy outperforms common reserve rules.
Avi Ostfeld - One of the best experts on this subject based on the ideXlab platform.
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limited multistage Stochastic Programming for water distribution systems optimal operation
Journal of Water Resources Planning and Management, 2016Co-Authors: Rafael Schwartz, Mashor Housh, Avi OstfeldAbstract:AbstractLeast-cost operation of water distribution systems (WDS) is a well-known problem in water distribution systems optimization. The formulation of the problem started with deterministic modeling, and the problem was subsequently handled with more sophisticated Stochastic models that incorporate uncertainties related to the problem’s parameters. This work applied a recently developed algorithm entitled limited multistage Stochastic Programming (LMSP) to deal with the Stochastic formulation of the least-cost operation of WDS and serves merely as a proof of concept on an illustrative network. The demand is considered as the uncertain parameter in the problem formulation. This algorithm reduces the complexity of the classical multistage Stochastic Programming (MSP) by adding constraints which result in a linear growth of the problem, as opposed to an exponential growth in the MSP problem. This is accomplished by clustering decision variables based on a postanalysis of the implicit Stochastic program of t...
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limited multi stage Stochastic Programming for managing water supply systems
Environmental Modelling and Software, 2013Co-Authors: Mashor Housh, Avi Ostfeld, Uri ShamirAbstract:Decision-making processes often involve uncertainty. A common approach for modeling uncertain scenario-based decision-making progressions is through multi-stage Stochastic Programming. The size of optimization problems derived from multi-stage Stochastic programs is frequently too large to be addressed by a direct solution technique. This is due to the size of the optimization problems, which grows exponentially as the number of scenarios and stages increases. To cope up with this computational difficulty, solution schemes turn to decomposition methods for defining smaller and easier to solve equivalent sub-problems, or through using scenario-reduction techniques. In our study a new methodology is proposed, titled Limited Multi-stage Stochastic Programming (LMSP), in which the number of decision variables at each stage remains constant and thus the total number of decision variables increases only linearly as the number of scenarios and stages grows. The LMSP employs a decision-clustering framework, which utilizes the optimal decisions obtained by solving a set of deterministic optimization problems to identify decision nodes, which have similar decisions. These nodes are clustered into a preselected number of clusters, where decisions are made for each cluster instead of for each individual decision node. The methodology is demonstrated on a multi-stage water supply system operation problem, which is optimized for flow and salinity decisions. LMSP performance is compared to that of classical multi-stage Stochastic Programming (MSP) method.
