检索结果-内蒙古大学图书馆

An accelerated scenario updating heuristic for stochastic production planning with set-up constraints in sawmills

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第4期51卷 993-1005页

作者： Zanjani, Masoumeh Kazemi Ait-Kadi, Daoud Nourelfath, Mustapha Concordia Univ Montreal PQ Canada Univ Laval Dept Mech Engn Quebec City PQ Canada

In this article we propose an accelerated scenario updating heuristic to solve a real-world large-scale multi-stage stochastic mixed-integer model. Motivated from challenges we are facing in sawmills, the model corresponds to multi-period, multi-product production planning including setup constraints, with random yield and demand. While addressing real-life size instances of the problem, the resulting large-scale multi-stage stochastic mixed-integer model cannot be solved by commercial optimisation packages. Moreover, as the production planning model is a mixed-integer program without any special structure, developing decomposition and cutting plane algorithms to obtain good approximate solutions in reasonable time is not straightforward. We propose a successive approximation heuristic inspired from scenario updating heuristic which has been proposed for multi-stage stochastic models with partial recourse. The latter solves the problem by considering only a subset of scenarios which is updated at each iteration. We modify the scenario updating heuristic in two directions: (1) we modify the bounds on the optimal solution so as to make them valid for multi-stage stochastic models with full recourse, and (2) we propose a new scenario selection rule so as to increase the rate of convergence and the quality of solution. Computational experiments for a real-world large-scale sawmill production planning model verify the effectiveness of the proposed solution strategy in finding quickly good approximate solutions.

关键词： production planning stochastic mixed-integer programming scenario updating method sawmills

来源：评论

学校读者我要写书评

暂无评论

New algorithmic framework for conditional value at risk: Application to stochastic fixed-charge transportation

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期277卷 215-226页

作者： Fernandez, Elena Hinojosa, Yolanda Puerto, Justo Saldanha-da-Gama, Francisco Univ Cadiz Dept Estadist & Invest Operat Cadiz Spain Univ Seville IMUS Seville Spain Univ Seville Dept Econ Aplicada 1 Seville Spain Univ Seville Fac Matemat IMUS Seville Spain Univ Seville Fac Matemat Dept Estadist & Invest Operat Seville Spain Univ Lisbon Fac Ciencias Dept Estat & Invest Operac Lisbon Portugal Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentais & Invest Oper Lisbon Portugal

This paper introduces a new algorithmic scheme for two-stage stochastic mixed-integer programming assuming a risk averse decision maker. The focus is the minimization of the conditional value at risk for a hard combinatorial optimization problem. Some properties of a mixed-integer non-linear programming formulation for conditional value at risk are studied as well as their algorithmic implications. This yields to a procedure for obtaining lower and upper bounds on the optimal value of the problem that may lead to an optimal solution. The new developments are applied to a fixed-charge transportation problem with stochastic demand, and they are computationally tested. The corresponding results are thoroughly presented and discussed. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Transportation stochastic mixed-integer programming CVaR

来源：评论

学校读者我要写书评

暂无评论

Multi-Period Power System Risk Minimization Under Wildfire Disruptions

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第5期39卷 6305-6318页

作者： Yang, Hanbin Rhodes, Noah Yang, Haoxiang Roald, Line Ntaimo, Lewis Chinese Univ Hong Kong Shenzhen Sch Data Sci Shenzhen 518172 Peoples R China Univ Wisconsin Madison Elect & Comp Engn Madison WI 53706 USA Texas A&M Univ Ind & Syst Engn College Stn TX 77843 USA

Natural wildfire becomes increasingly frequent as climate change evolves, posing a growing threat to power systems, while grid failures simultaneously fuel the most destructive wildfires. Preemptive de-energization of grid equipment is effective in mitigating grid-induced wildfires but may cause significant power outages during natural wildfires. This paper proposes a novel two-stage stochastic program for planning preemptive de-energization and solves it via an enhanced Lagrangian cut decomposition algorithm. We model wildfire events as stochastic disruptions with random magnitude and timing. The stochastic program maximizes the electricity delivered while proactively de-energizing components over multiple time periods to reduce wildfire risks. We use a cellular automaton process to sample grid failure and wildfire scenarios driven by realistic risk and environmental factors. We test our method on an augmented version of the RTS-GLMC test case in Southern California and compare it with four benchmark cases, including deterministic, wait-and-see, and robust optimization formulations as well as a comparison with prior wildfire risk optimization. Our method reduces wildfire damage costs and load-shedding losses, and our nominal plan is robust against uncertainty perturbation.

关键词： Wildfires Climate change Lagrangian functions mixed integer linear programming Risk management stochastic processes Power system reliability Decomposition algorithm de-energization Lagrangian cut optimal power flow stochastic mixed-integer programming wildfire risk

来源：评论

学校读者我要写书评

暂无评论

Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach

引用

RENEWABLE ENERGY 2016年第0期93卷 383-403页

作者： Azadeh, Ali Arani, Hamed Vafa Univ Tehran Ctr Excellence Intelligent Based Expt Mech Sch Ind Engn Tehran 14174 Iran Univ Tehran Dept Engn Optimizat Res Coll Engn Tehran 14174 Iran

Development of biofuels causes reduction of environmental pollution, but certain limitations affect their production. In this research, a hybrid system dynamics-mathematical programming approach is developed to design and plan a biodiesel supply chain from biomass fields to consumption markets. The supply chain faces limitations in biodiesel production. Water resource limitations for biodiesel production, land limitations for biomass procurement, and technological issues are the most important limitations considered in the system dynamics model. In addition, competition between fossil fuels and biodiesel is taken into account. The proposed methodology, firstly, estimates the most important parameters in biodiesel supply chain in a given planning horizon. Then, estimated parameters are used as inputs of the mathematical model and the optimal supply chain decisions are made by means of a stochastic mixed-integer programming model. Besides, a scenario-based approach is used to model the disruption risks for links and biomass fields. Finally, a numerical experiment is presented to show the applicability of the methodology according to some interviews with experts in Iran. Results demonstrate the potential appropriate market of biodiesel in Iran while several resource and technology limitations and environmental pollution avoid growth of biodiesel market. Moreover, a sensitivity analysis is performed on risk preferences of decision makers and government policies adopted to improve the biodiesel market. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Biodiesel supply chain Optimization System dynamics stochastic mixed-integer programming Renewable energy

来源：评论

学校读者我要写书评

暂无评论

Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs

引用

MATHEMATICAL programming COMPUTATION 2018年第2期10卷 225-266页

作者： Kim, Kibaek Zavala, Victor M. Argonne Natl Lab Math & Comp Sci Div 9700 South Cass Ave Lemont IL 60439 USA Univ Wisconsin Dept Chem & Biol Engn 1415 Engn Dr Madison WI 53706 USA

We present algorithmic innovations for the dual decomposition method to address two-stage stochastic programs with mixed-integer recourse and provide an open-source software implementation that we call DSP. Our innovations include the incorporation of Benders-like cuts in a dual decomposition framework to tighten Lagrangian subproblems and aid the exclusion of infeasible first-stage solutions for problems without (relative) complete recourse. We also use an interior-point cutting-plane method with newtermination criteria for solving the Lagrangian master problem. We prove that the algorithm converges to an optimal solution of the Lagrangian dual problem in a finite number of iterations, and we also prove that convergence can be achieved even if the master problem is solved suboptimally. DSP can solve instances specified in C code, SMPS files, and Julia script. DSP also implements a standard Benders decomposition method and a dual decomposition method based on subgradient dual updates that we use to perform benchmarks. We present extensive numerical results using SIPLIB instances and a large unit commitment problem to demonstrate that the proposed innovations provide significant improvements in the number of iterations and solution times. The software reviewed as part of this submission has been given the Digital Object Identifier (DOI) https://***/10.5281/zenodo.998971.

关键词： stochastic mixed-integer programming Decomposition Parallel Large-scale Open-source software

来源：评论

学校读者我要写书评

暂无评论

ADP-based Home Energy Management System: A Case Study using DYNAMO

ADP-based Home Energy Management System: A Case Study using ...

引用

IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Faqiry, M. N. Wang, L. Wu, H. Krishnamurthy, D. Palmintier, B. Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA Natl Renewable Energy Lab Power Syst Engn Ctr Golden CO 80401 USA

ISBN: (纸本)9781538677032

This paper explores Home Energy Management System (HEMS) algorithms to minimize household cost while maintaining comfort when faced with uncertain weather, and demand. Specifically, we consider a HEMS that optimizes forward looking schedules for a home's heating, ventilation, and air conditioning (HVAC);water heater (WH);and electric vehicle (EV) charging while considering uncertainty in outside temperature, hot water usage, and non-controllable load (NCL). We adopt a Dynamic programming (DP) formulation and utilize the Dynamic programming for Adaptive Modeling and Optimization (DYNAMO) toolkit to implement DP and approximate dynamic programming (ADP) algorithms. Simulation results under a single tariff plan compare the quality of the solution generated by ADP to that of DP, and show significant improvement in computation time while maintaining acceptable solution accuracy.

关键词： Approximate dynamic programming demand response electric vehicle home energy management system stochastic mixed-integer programming

来源：评论

学校读者我要写书评

暂无评论

Chance and service level constraints for stochastic generation expansion planning

引用

NETNOMICS 2015年第3期16卷 169-191页

作者： Lee, Geun-Cheol Hohenrieder, Martin Watson, Jean-Paul Woodruff, David L. Konkuk Univ Coll Business Adm Seoul South Korea Accenture Zurich Switzerland Sandia Natl Labs Analyt Dept Albuquerque NM 87185 USA Univ Calif Davis Grad Sch Management Davis CA 95616 USA

We consider the problem of generation expansion planning (GEP) under uncertainty. Existing and planned deployment of demand response mechanisms necessitates extensions of GEP optimization models to minimize planned investment portfolio cost. We propose a combination of service level threshold and chance constraints to abstractly capture the impact of demand response mechanisms in generation expansion planning. We examine the computational properties of solutions from the proposed optimization model using real-world data from South Korea. Our results indicate that the proposed models can be solved to optimality in tractable runtimes, allowing for their use in decision support contexts and avoiding the need for heuristic solution procedures.

关键词： Generation expansion Chance constraints Service level thresholds stochastic mixed-integer programming

来源：评论

学校读者我要写书评

暂无评论

Models and Algorithms for Dominance-Constrained stochastic Programs with Recourse

Models and Algorithms for Dominance-Constrained Stochastic P...

引用

作者： Dimitri Drapkin University of Duisburg-Essen

学位级别：博士

We consider optimization problems with stochastic order constraints of first and second order posed on random variables coming from two-stage stochastic programs with recourse. We clarify the theoretical relevance of these specific problems, and contribute to improving their computational tractability. For the latter, we review and enhance mixed-integer linear programming (MILP) equivalents. These exist for either mixed-integer or continuous variables in the second stage. Algorithmically, our focus is on developing tailored cutting-plane decomposition methods for these models.

关键词： stochastic mixed-integer programming stochastic dominance decomposition methods cutting-plane methods risk aversion

来源：评论

学校读者我要写书评

暂无评论

Subgradient-based Decomposition Methods for stochastic mixed-integer Programs with Special Structures

Subgradient-based Decomposition Methods for Stochastic Mixed...

引用

作者： Beier, Eric Texas A&M University

学位级别：master

The focus of this dissertation is solution strategies for stochastic mixed-integer programs with special structures. Motivation for the methods comes from the relatively sparse number of algorithms for solving stochastic mixed-integer programs. Two stage models with finite support are assumed throughout. The first contribution introduces the nodal decision framework under private information restrictions. Each node in the framework has control of an optimization model which may include stochastic parameters, and the nodes must coordinate toward a single objective in which a single optimal or close-to-optimal solution is desired. However, because of competitive issues, confidentiality requirements, incompatible database issues, or other complicating factors, no global view of the system is *** iterative methodology called the nodal decomposition-coordination algorithm (NDC) is formally developed in which each entity in the cooperation forms its own nodal deterministic or stochastic program. Lagrangian relaxation and subgradient optimization techniques are used to facilitate negotiation between the nodal decisions in the system without any one entity gaining access to the private information from other nodes. A computational study on NDC using supply chain inventory coordination problem instances demonstrates that the new methodology can obtain good solution values without violating private information restrictions. The results also show that the stochastic solutions outperform the corresponding expected value *** next contribution presents a new algorithm called scenario Fenchel decomposition (SFD) for solving two-stage stochastic mixed 0-1 integer programs with special structure based on scenario decomposition of the problem and Fenchel cutting planes. The algorithm combines progressive hedging to restore nonanticipativity of the first-stage solution, and generates Fenchel cutting planes for the LP relaxations of the subproblems to recover integer

关键词： stochastic mixed-integer programming

来源：评论

学校读者我要写书评

暂无评论

A dynamic multi-stage design framework for staged deployment optimization of highly stochastic systems

引用

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2023年第7期66卷 162-162页

作者： Hamdan, Bayan Liu, Zheng Ho, Koki Buyuktahtakin, I. Esra Wang, Pingfeng Univ Illinois Dept Ind & Enterprise Syst Engn Champaign IL 61820 USA Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA USA Virginia Polytech Inst & State Univ Grad Dept Ind & Syst Engn Blacksburg VA USA

The need for staged design optimization for multidisciplinary systems with strong, cross-system links and complex systems has been acknowledged in various contexts. This is prominent in fields where decisions between subsystems are dependant, as well as in cases where tactical decisions need to be made in uncertain environments. The flexibility gained by incorporating evolutionary design options has been analyzed by discretizing the time-variant uncertainties into scenarios and considering the flexible decision variables in each scenario separately. However, these problems use existing information at the decision time step. This paper presents a dynamic multi-staged design framework to solve problems that dynamically incorporate updated system information and reformulate the problem to account for the updated parameters. The importance of considering staged decisions is studied, and the benefit of the model is evaluated in cases where the stochasticity of the parameters decreases with time. The impact of considering staged deployment for highly stochastic, large-scale systems is investigated through a numerical case study as well as a case study for the IEEE 30 bus system. The case studies presented in this paper investigate multi-disciplinary design problems for large-scale complex systems as well as operational planning for highly stochastic systems. The importance of considering staged deployment for multi-disciplinary systems that have decreasing variability of their parameters with time is highlighted and demonstrated through the results of numerical and engineering case studies.

关键词： Staged decisions Multi-stage Dynamic information stochastic mixed-integer programming

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：