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Heuristic solution approaches to the pharmaceutical R&D pipeline management problem

COMPUTERS & CHEMICAL ENGINEERING 2015年 74卷 34-47页

作者： Christian, Brianna Cremaschi, Selen Univ Tulsa Dept Chem Engn Tulsa OK 74104 USA

The paper presents two heuristic approaches, a shrinking horizon multiple two-stage stochastic programming (MTSSP) decomposition algorithm and a knapsack decomposition algorithm (KDA), for solving multistage stochastic programmes (MSSPs) with endogenous uncertainty, specifically focusing on pharmaceutical research and development (R&D) pipeline management problem. The MTSSP decomposition algorithm decomposes the problem into a series of two-stage stochastic programmes, which are solved as resources become available. The KDA decomposes the MSSP into a series of knapsack problems, which are created and solved at key decision points on a rolling horizon fashion. Based on the results of the six case studies, both the MTSSP decomposition algorithm and the KDA generate implementable solutions that are within three percent of the rigorous MSSP solution obtained by CPLEX 12.51. Both methods showed several orders of magnitude decrease in the CPU times compared to ones that were required to solve the rigorous MSSP. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Endogenous uncertainty multistage stochastic programming Pharmaceutical R&D pipeline management Knapsack decomposition

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Integrated multiperiod power generation and transmission expansion planning with sustainability aspects in a stochastic environment

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ENERGY 2015年 86卷 9-18页

作者： Seddighi, Amir Hossein Ahmadi-Javid, Amir Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper presents a multistage stochastic programming model to address sustainable power generation and transmission expansion planning. The model incorporates uncertainties about future electricity demand, fuel prices, greenhouse gas emissions, as well as possible disruptions to which the power system is subject. A number of sustainability regulations and policies are considered to establish a framework for the social responsibility of the power system. The proposed model is applied to a real-world case, and several sensitivity analyses are carried out to provide managerial insights into different aspects of the model. The results emphasize the important role played by sustainability policies on the configuration of the power grid. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Electrical power systems Generation expansion planning Transmission expansion planning Social responsibility Sustainable development multistage stochastic programming

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multistage stochastic programming Model of Portfolio Selection for Life Insurance Companies in China

Multistage Stochastic Programming Model of Portfolio Selecti...

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2nd International Conference on Business Intelligence and Financial Engineering

作者： Qu, Ran Qu, Zhenting Jilin Univ Sch Management Changchun 130023 Peoples R China Chinalife Insurance Co Branch of JiLin Changchun Peoples R China

ISBN: (纸本)9780769537054

In order to help Chinese life insurance companies effectively make their portfolio selection. A portfolio selection model was established by using the method of multistage stochastic programming in this paper. In this model the management and supervision reality of Chinese life insurance industry were transferred into constraints. The quarterly return rate data of Chinalife' investment and that of the assets in Chinese financial markets were gathered from 2004 to the first two quarters in 2008. A scenario tree was established by using these data. Benders Decomposition Algorithm was chosen to solve this model. Then the model was used to construct a series of portfolio for China life in 2009. The portfolio showed that China life should maintain over 70% of risk-free assets and less than 30% risk assets to achieve its objective of benefit and security. Also its insurance business should be concerned.

关键词： portfolio life insrance multistage stochastic programming scenario generation financial market

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Structure of risk-averse multistage stochastic programs

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OR SPECTRUM 2015年第3期37卷 559-582页

作者： Dupacova, Jitka Kozmik, Vaclav Charles Univ Prague Fac Math & Phys Dept Probabil & Math Stat Prague Czech Republic

We deal with risk-averse multistage stochastic programs with coherent risk measures such as multiperiod extensions of conditional value at risk or polyhedral risk measures. Their basic properties are discussed and applied to scenario-based input data. Using the contamination technique we quantify the influence of changes in the structure of the scenario-based approximation to the optimal value of the problem. stochastic dual dynamic programming algorithm is used to provide illustrative numerical comparisons for different choices of risk measures and changes of input data for a simple multistage risk-averse stock allocation problem with scenario trees based on log-normal distribution of the asset returns.

关键词： multistage stochastic programming Multiperiod risk measures stochastic dual dynamic programming Contamination

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Energy Scheduling in Power Market under stochastic Dependence Structure 30

Energy Scheduling in Power Market under Stochastic Dependenc...

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30th International Power System Conference (PSC)

作者： Farhadkhani, Mehdi NRI Elect Econ & Energy Res Grp Tehran Iran

ISBN: (纸本)9781509027057

Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling strategies under uncertainty. However, dependence structure between stochastic variables has been almost ignored in the literature. Copula function is a new concept in the probability and statistics field which has the capability to represent the dependence structure among stochastic variables. However, Copula function has recently taken into account in power system studies by some articles. In this article, self-scheduling strategy of a generation utility owning thermal units is investigated while the dependence structure among stochastic load and market price variables is taking into account. We assume that the generation utility is a price-taker agent in a power market, and it also has to meet the load of a specific region as a retailer. The results indicates that as the stochastic dependence structure among load and price variables is considered in modeling load and price scenarios, the output of unit commitment problem changes so that the revenue of generation utility increases.

关键词： Unit Commitment stochastic Dependence Structure multistage stochastic programming Scenario Tree Construction Copula Function

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Úlohy vícestupňového stochastického programování - dekompozice

Úlohy vícestupňového stochastického programování - de...

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作者： Lapšanská, Alica Charles University of Prague

The thesis deals with a multistage stochastic model and its application to a number of practical problems. Special attention is devoted to the case where a random element follows an autoregressive sequence and the constraint sets correspond to the individual probability constraints. For this case conditions under which is the problem well-defined are specified. Further, the approximation of the problem and its convergence rate under the empirical estimate of the distribution function is analyzed. Finally, an example of the investment in financial instruments is solved, which is defined as a two-stage stochastic programming problem with the probability constraint and a random element following an autoregressive sequence. Powered by TCPDF (***)

关键词： multistage stochastic programming autoregressive sequences individual probability constraints empirical estimates stability

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Short-term optimal operation of water systems using ensemble forecasts

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ADVANCES IN WATER RESOURCES 2014年 71卷 200-208页

作者： Raso, L. Schwanenberg, D. van de Giesen, N. C. van Overloop, P. J. Delft Univ Technol Delft Netherlands IRSTEA F-34196 Montpellier France Deltares Fdn Delft Netherlands Univ Duisburg Essex Duisburg Germany

Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate. Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts' availability and accuracy raises the question on how to use them for operational management. The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a multistage stochastic programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance. TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Operational water management Short term reservoir management Model Predictive Control Ensemble forecast multistage stochastic programming Robustness

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The value of flexibility for pulp mills investing in energy efficiency and future biorefinery concepts

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2014年第14期38卷 1864-1878页

作者： Svensson, Elin Berntsson, Thore Stromberg, Ann-Brith Chalmers Environm & Energy Dept SE-41296 Gothenburg Sweden Chalmers Dept Math Sci SE-41296 Gothenburg Sweden Univ Gothenburg SE-41296 Gothenburg Sweden

Changing conditions in biomass and energy markets require the pulp and paper industry to improve energy efficiency and find new opportunities in biorefinery implementation. Considering the expected changes in the pulp mill environment and the variety of potential technology pathways, flexibility should be a strong advantage for pulp mills. In this context, flexibility is defined as the ability of the pulp mill to respond to changing conditions. The aim of this article is to show the potential value of flexibility in the planning of pulp mill energy and biorefinery projects and to demonstrate how this value can be incorporated into models for optimal strategic planning of such investments. The paper discusses the requirements on the optimization models in order to adequately capture the value of flexibility. It is suggested that key elements of the optimization model are multiple points in time where investment decisions can be made as well as multiple scenarios representing possible energy price changes over time. The use of a systematic optimization methodology that incorporates these model features is illustrated by a case study, which includes opportunities for district heating cooperation as well as for lignin extraction and valorization. A quantitative valuation of flexibility is provided for this case study. The study also demonstrates how optimal investment decisions for a pulp mill today are influenced by expected future changes in the markets for energy and bioproducts. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： energy efficiency forest biorefinery process integration investment under uncertainty multistage stochastic programming energy price scenarios

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A new decomposition algorithm for multistage stochastic programs with endogenous uncertainties

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COMPUTERS & CHEMICAL ENGINEERING 2014年 62卷 62-79页

作者： Gupta, Vijay Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

In this paper, we present a new decomposition algorithm for solving large-scale multistage stochastic programs (MSSPs) with endogenous uncertainties. Instead of dualizing all the initial non-anticipativity constraints (NACs) and removing all the conditional NACs to decompose the problem into scenario subproblems, the basic idea relies on keeping a subset of NACs as explicit constraints in the scenario group subproblems while dualizing or relaxing the rest of the NACs. It is proved that the algorithm provides a dual bound that is at least as tight as the standard approach. Numerical results for process network examples and oilfield development planning problem are presented to illustrate that the proposed decomposition approach yields significant improvement in the dual bound at the root node and reduction in the total computational expense for closing the gap. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： multistage stochastic programming Endogenous uncertainties Non-anticipativity constraints Lagrangean decomposition Process networks Oil and gas explorationa

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Optimal scheduling for enhanced coal bed methane production through CO₂ injection

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APPLIED ENERGY 2014年 113卷 1475-1483页

作者： Huang, Yuping Zheng, Qipeng P. Fan, Neng Aminian, Kashy Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA W Virginia Univ Dept Petr & Nat Gas Engn Morgantown WV 26506 USA

Enhanced coal bed methane production with CO2 injection (CO2-ECBM) is an effective technology for accessing the natural gas embedded in the traditionally unmineable coal seams. The revenue via this production process is generated not only by the sales of coal bed methane, but also by trading CO2 credits in the carbon market. As the technology of CO2-ECBM becomes mature, its commercialization opportunities are also springing up. This paper proposes applicable mathematical models for CO2-ECBM production and compares the impacts of their production schedules on the total profit. A novel basic deterministic model for CO2-ECBM production including the technical and chemical details is proposed and then a multistage stochastic programming model is formulated in order to address uncertainties of natural gas price and CO2 credit. Both models are nonlinear programming problems, which are solved by commercial nonlinear programming software BARON via GAMS. Numerical experiments show the benefits (e.g., expected profit gain) of using stochastic models versus deterministic models. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Coal bed methane production CO2 injection multistage stochastic programming Nonlinear programs Optimal scheduling

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