检索结果-内蒙古大学图书馆

Risk aversion based interval stochastic programming approach for agricultural water management under uncertainty

stochastic ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2018年第3期32卷 715-732页

作者： Li, Q. Q. Li, Y. P. Huang, G. H. Wang, C. X. North China Elect Power Univ Sino Canada Resources & Environm Res Acad Beijing 102206 Peoples R China Beijing Normal Univ Sch Environm State Key Lab Water Environm Simulat Beijing 100875 Peoples R China Univ Regina Environm Syst Engn Program Fac Engn & Appl Sci Regina SK S4S 0A2 Canada

In this study, a risk aversion based interval stochastic programming (RAIS) method is proposed through integrating interval multistage stochastic programming and conditional value at risk (CVaR) measure for tackling uncertainties expressed as probability distributions and intervals within a multistage context. The RAIS method can reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. Using the CVaR measure, RAIS can effectively reflect system risk resulted from random parameters. When random events are occurred, the adjustable alternatives can be achieved by setting desired targets according to the CVaR, which could make the revised decisions to minimize the economic penalties. Then, the RAIS method is applied to planning agricultural water management in the Zhangweinan River Basin that is plagued by drought due to serious water scarcity. A set of decision alternatives with different combinations of risk levels employed to the objective function and constraints are generated for planning water resources allocation. The results can not only help decision makers examine potential interactions between risks under uncertainty, but also help generate desired policies for agricultural water management with a maximized payoff and a minimized loss.

关键词： Agricultural irrigation Conditional value at risk Crop planning Multistage stochastic programming Water resources

来源：评论

学校读者我要写书评

暂无评论

Chance-constrained stochastic programming under variable reliability levels with an application to humanitarian relief network design

引用

COMPUTERS & OPERATIONS RESEARCH 2018年 96卷 91-107页

作者： Elci, Ozgun Noyan, Nilay Bulbul, Kerem Sabanci Univ Ind Engn Program TR-34956 Istanbul Turkey

We focus on optimization models involving individual chance constraints, in which only the right-hand side vector is random with a finite distribution. A recently introduced class of such models treats the reliability levels / risk tolerances associated with the chance constraints as decision variables and trades off the actual cost / return against the cost of the selected reliability levels in the objective function. Leveraging recent methodological advances for modeling and solving chance-constrained linear programs with fixed reliability levels, we develop strong mixed-integer programming formulations for this new variant with variable reliability levels. In addition, we introduce an alternate cost function type associated with the risk tolerances which requires capturing the value-at-risk (VaR) associated with a variable reliability level. We accomplish this task via a new integer linear programming representation of VaR. Our computational study illustrates the effectiveness of our mathematical programming formulations. We also apply the proposed modeling approach to a new stochastic last mile relief network design problem and provide numerical results for a case study based on the real-world data from the 2011 Van earthquake in Turkey. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： stochastic programming Risk-averse Probabilistic constraints Chance constraints Variable reliability Bonferroni approximation Value-at-risk Network design Post-disaster Humanitarian logistics Accessibility Equity

来源：评论

学校读者我要写书评

暂无评论

Prepositioning emergency supplies to support disaster relief: a case study using stochastic programming

引用

INFOR 2018年第1期56卷 50-81页

作者： Klibi, Walid Ichoua, Soumia Martel, Alain Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada KEDGE Business Sch Ctr Excellence Supply Chain CESIT Talence France Embry Riddle Aeronaut Univ Coll Business Daytona Beach FL USA Univ Laval Fac Sci Adm Dept Operat & Systemes Decis Quebec City PQ Canada

This paper studies the strategic problem of designing emergency supply networks to support disaster relief over a planning horizon. The problem addresses decisions on the location and number of distribution centres needed, their capacity, and the quantity of each emergency item to keep in stock. It builds on a case study inspired by real-world data obtained from the North Carolina Emergency Management Division (NCEM) and the Federal Emergency Management Agency (FEMA). To tackle the problem, a scenariobased approach is proposed involving three phases: disaster scenario generation, design generation and design evaluation. Disasters are modelled as stochastic processes and a Monte Carlo procedure is derived to generate plausible catastrophic scenarios. Based on this detailed representation of disasters, a multi-phase modelling framework is proposed to design the emergency supply network. The two-stage stochastic programming model proposed is solved using a sample average approximation method. This scenario-based solution approach is applied to the case study to generate plausible scenarios, to produce alternative designs and to evaluate them on a set of performance measures in order to select the best design.

关键词： Humanitarian logistics relief network design risk modelling multi-hazards stochastic programming Monte Carlo scenarios

来源：评论

学校读者我要写书评

暂无评论

A two-stage stochastic programming approach for influence maximization in social networks

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2018年第3期69卷 563-595页

作者： Wu, Hao-Hsiang Kucukyavuz, Simge Univ Washington Ind & Syst Engn Seattle WA 98195 USA

We consider stochastic influence maximization problems arising in social networks. In contrast to existing studies that involve greedy approximation algorithms with a 63% performance guarantee, our work focuses on solving the problem optimally. To this end, we introduce a new class of problems that we refer to as two-stage stochastic submodular optimization models. We propose a delayed constraint generation algorithm to find the optimal solution to this class of problems with a finite number of samples. The influence maximization problems of interest are special cases of this general problem class. We show that the submodularity of the influence function can be exploited to develop strong optimality cuts that are more effective than the standard optimality cuts available in the literature. Finally, we report our computational experiments with large-scale real-world datasets for two fundamental influence maximization problems, independent cascade and linear threshold, and show that our proposed algorithm outperforms the basic greedy algorithm of Kempe et al. (Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD'03, New York, NY, USA, ACM, pp 137-146, 2003).

关键词： Social networks Independent cascade Linear threshold Influence maximization stochastic programming Submodularity

来源：评论

学校读者我要写书评

暂无评论

Managing Energy Storage in Microgrids: A Multistage stochastic programming Approach

引用

IEEE TRANSACTIONS ON SMART GRID 2018年第1期9卷 483-496页

作者： Bhattacharya, Arnab Kharoufeh, Jeffrey P. Zeng, Bo Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA

A microgrid is a small-scale version of a centralized power grid that generates, distributes and regulates electricity flow to local entities using distributed generation and the main grid. Distributed energy storage systems can be used to mitigate adverse effects of intermittent renewable sources in a microgrid in which operators dynamically adjust electricity procurement and storage decisions in response to randomlyevolving demand, renewable supply and pricing information. We formulate a multistage stochastic programming (SP) model whose objective is to minimize the expected total energy costs incurred within a microgrid over a finite planning horizon. The model prescribes the amount of energy to procure, store and discharge in each decision stage of the horizon. However, for even a moderate number of stages, the model is computationally intractable;therefore, we customize the stochastic dual dynamic programming (SDDP) algorithm to obtain high-quality approximate solutions. Computation times and optimization gaps are significantly reduced by implementing a dynamic cut selection procedure and a lower bound improvement scheme within the SDDP framework. An extensive computational study reveals significant cost savings as compared to myopic and non-storage policies, as well as policies obtained using a two-stage SP model. The study also demonstrates the scalability of our solution procedure.

关键词： Microgrid energy storage stochastic programming stochastic dual dynamic programming

来源：评论

学校读者我要写书评

暂无评论

A Linear stochastic programming Model for Optimal Leveraged Portfolio Selection

引用

COMPUTATIONAL ECONOMICS 2018年第4期51卷 1021-1032页

作者： Valladao, Davi Michel Veiga, Alvaro Street, Alexandre Pontif Catholic Univ Rio de Janeiro Dept Ind Engn Rio De Janeiro Brazil Pontif Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil

The literature of portfolio optimization is extensive and covers several important aspects of the asset allocation problem. However, previous works consider simplified linear borrowing cost functions that leads to suboptimal allocations. This paper aims at efficiently solving the leveraged portfolio selection problem with a thorough borrowing cost representation comprising a number lenders with different rates and credit limits. We propose a two-stage stochastic programming model for asset and debt allocation considering a CVaR-based risk constraint and a convex piecewise-linear borrowing cost function. We compare our model to its counterpart with the fixed borrowing rate approximation used in literature. Numerical results show our model significantly improves performance in terms of risk-return trade-off.

关键词： Leveraged portfolio selection Efficient frontier stochastic programming Conditional value-at-risk

来源：评论

学校读者我要写书评

暂无评论

A stochastic programming approach for the optimal management of aggregated distributed energy resources

引用

COMPUTERS & OPERATIONS RESEARCH 2018年 96卷 199-211页

作者： Beraldi, P. Violi, A. Carrozzino, G. Bruni, M. E. Univ Calabria Dept Mech Energy & Management Engn Arcavacata Di Rende CS Italy

The paper focuses on the optimal management of distributed energy resources aggregated within a coalition. The problem is analyzed from the viewpoint of an aggregator, seen as an entity called to optimize the available resources so to satisfy the aggregated demand by eventually trading in the Day-Ahead Electricity Market. Both a full and a residual perspective in the management of the integrated resources is investigated and compared. The inherent uncertainty affecting the optimal decision problem, mainly related to the demand profile, electricity prices and production from renewable sources, is dealt by adopting the two-stage stochastic programming paradigm. The proposed models (different for the full and residual case) present a bi-objective function, integrating the expected profit and a risk measure, the Conditional Value at Risk, to control undesirable effects caused by the random variations of the uncertain parameters. A broad numerical study has been carried out on real case study. The analysis of the results clearly shows the benefits deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Energy market stochastic programming Distributed energy resources Aggregator Risk management

来源：评论

学校读者我要写书评

暂无评论

A multi-stage fuzzy stochastic programming method for water resources management with the consideration of ecological water demand

引用

ECOLOGICAL INDICATORS 2018年第Part1期95卷 930-938页

作者： Li, Congcong Cai, Yanpeng Qian, Jinping Beijing Normal Univ State Key Lab Water Environm Simulat Sch Environm Beijing 100875 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities 1202 Res Dr Regina SK S4S 7H9 Canada Beijing Normal Univ Sch Environm Beijing Engn Res Ctr Watershed Environm Restorat Beijing 100875 Peoples R China Hebei Normal Univ Coll Resource & Environm Sci Hebei Key Lab Environm Change & Ecol Construct Shyiazhuang 050016 Hebei Peoples R China

In this paper, a multi-stage fuzzy stochastic programming (MFSP) method is introduced to deal with uncertainties presented as fuzzy sets and probability distributions. Moreover, it is able to reflect dynamics of uncertainties and the related decision processes through constructing a series of representative scenarios within a multi-stage context under a set of fuzzy a-cut levels. A management problem about long-term planning of water resources system has been studied to illustrate applicability of the proposed approach. With ecological water demand being considered, the framework solves the complex problems that can hardly be solved in previous individual model research and promotes sustainable development. The results indicate that the dynamic and complexity of water resources allocation can be reflected through the multilayer discrete context tree. Moreover, real-time correction for reducing the risk of water shortage and low economic penalty can be presented. They can also help identify satisfaction degree of the goal and feasibility degree of constraints in an interactive way, enabling decision makers to generate a series of alternatives under various system conditions. Overall, it can not only contribute to decision makers for in-depth analysis, but also for sustainable development of ecosystem.

关键词： Multi-stage stochastic programming Fuzzy sets Uncertainty Ecological water demand Water resources

来源：评论

学校读者我要写书评

暂无评论

A stochastic programming Model for Casualty Response Planning During Catastrophic Health Events

引用

TRANSPORTATION SCIENCE 2018年第2期52卷 437-453页

作者： Caunhye, Aakil M. Nie, Xiaofeng Natl Univ Singapore Singapore ETH Ctr Singapore 138602 Singapore Swiss Fed Inst Technol Zurich Switzerland Univ Buffalo Dept Ind & Syst Engn Buffalo NY 14260 USA

Catastrophic health events are natural or man-made incidents that create casualties in numbers that overwhelm the response capabilities of healthcare systems. Proper response planning for such events requires community-based surge solutions such as the location of alternative care facilities and ways to improve coordination by considering triage and the movement of self-evacuees. In this paper, we construct a three-stage stochastic programming model to locate alternative care facilities and allocate casualties in response to catastrophic health events. Our model integrates casualty triage and the movement of self-evacuees in a systemic response framework that treats uncertainties involved in such large-scale events as probabilistically distributed scenarios. Solution times being instrumental to the practicality of the model, we propose an algorithm, based on Benders decomposition, to generate good solutions fast. We derive new valid inequalities, which we add to the Benders decomposition master problem to reduce the number of weak feasibility cuts generated. Because our algorithm can also be ineffective if the number of scenarios is large, we propose a two-stage approximation model that attempts to guess good third-stage solutions without third-stage decision variables and constraints. Our model, algorithm, and two-stage approximation are implemented in the case study of an earthquake situation in California based on the realistic ShakeOut Scenario data.

关键词： catastrophic health events alternative care facilities triage stochastic programming cuts

来源：评论

学校读者我要写书评

暂无评论

Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming

引用

APPLIED ENERGY 2019年 253卷 113535-000页

作者： Xu, Bin Zhu, Feilin Zhong, Ping-an Chen, Juan Liu, Weifeng Ma, Yufei Guo, Le Deng, Xiaoliang Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China Nanjing Hydraul Res Inst 223 Guangzhou Rd Nanjing 210029 Jiangsu Peoples R China China Yangtze Power Co Ltd 1 Xiba Jianshe Rd2 Yichang 443002 Peoples R China State Grid Corp China Power Dispatching & Control Ctr Hunan Prov Changsha 410000 Hunan Peoples R China

Integrated operation of hydropower and wind power, which exploites the former's regulation flexibility to complement the uncertainty of the latter, enhances the utilization efficiency of wind power at the expense of deteriorating long-term hydropower energy production. This study identified the tradeoff effects of hydro-wind integrated operation by establishing a framework of coupling models. A martingale model that captures the evolution of forecasting uncertainty was used to generate synthetic scenarios of uncertain load demand. A stochastic programming model for integrated operation was established by tracking the influence of wind power uncertainty. A deterministic simulation model for independent operation was developed to derive independent operation strategies. By comparing the differences in operation strategies systematically, we analyzed the optimization and influencing mechanisms through groups of numerical experiments. A hypothetical case study based on the operation of the electrical system of the Three Gorges Dam project in China during the drawdown season revealed the following. (1) The positive effect of reducing wind energy shortfall and curtailment is determined by the ability of regulated hydropower to track the uncertainty of wind power output. (2) The negative effect primarily reduces the end storage and the stored energy of hydropower, thereby increasing the risk of future water/energy shortages and reducing reliability. (3) The positive effect on wind power presents a varied regime, whereas the negative effect on hydropower increases (decreases) with uncertainty level and inflow level (as the initial reservoir storage increases). The proposed methodology provides new insights into quantifying the effects of hybrid hydro-wind operation to inform decision-making.

关键词： Hydropower operation Wind energy Power output uncertainty stochastic programming Effect evaluation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：