检索结果-内蒙古大学图书馆

作者： Herrala, Olli Aalto University

学位级别：硕士

Despite multi-stage decision problems being common in production planning, there is a class of such problems for which a general solution framework does not exist, namely problems with endogenous uncertainty. Methods from decision analysis and stochastic programming can be used, but both require significantly constraining assumptions. In order to overcome the current challenges, Decision programming combines approaches from these two fields, making it possible to acquire optimal strategies for different decision problems. Decision programming is strictly limited to problems in which uncertainty events and decisions are taken from a finite discrete set, reducing its applicability to problems with continuous decision spaces. Discretizing a continuous decision space increases the problem size and can lead to computational intractability. This thesis presents a problem decomposition approach extending the Decision programming framework. The decomposition approach allows for considering continuous decision and uncertainty spaces in problems with a suitable structure. The proposed framework was applied to three different problems, including a large-scale production planning problem from literature. The main example in this thesis is a novel approach on climate change mitigation cost-benefit analysis, where R&D is carried out simultaneously with the emissions abatement decisions. The R&D projects provide information on the climate damage severity and decrease the price of abatement. Problems with similar structure have not been discussed in the literature, and the extended Decision programming framework is able to solve the problem to optimality.

关键词： decision programming endogenous uncertainty stochastic programming decision analysis influence diagrams climate change mitigation

来源：评论

学校读者我要写书评

暂无评论

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming

arXiv

引用

arXiv 2025年

作者： Liu, Yu Oliveira, Fabricio Department of Mathematics and Systems Analysis Aalto University Espoo Finland

Two-stage stochastic programming (2SP) offers a basic framework for modelling decision-making under uncertainty, yet scalability remains a challenge due to the computational complexity of recourse function evaluation. Existing learning-based methods like Neural Two-Stage stochastic programming (Neur2SP) employ neural networks (NNs) as recourse function surrogates but rely on computationally intensive mixed-integer programming (MIP) formulations. We propose ICNN-enhanced 2SP, a method that leverages Input Convex Neural Networks (ICNNs) to exploit linear programming (LP) representability in convex 2SP problems. By architecturally enforcing convexity and enabling exact inference through LP, our approach eliminates the need for integer variables inherent to the conventional MIP-based formulation while retaining an exact embedding of the ICNN surrogate within the 2SP framework. This results in a more computationally efficient alternative that maintains solution quality. Comprehensive experiments reveal that ICNNs incur only marginally longer training times while achieving validation accuracy on par with their MIP-based counterparts. Across benchmark problems, ICNN-enhanced 2SP often exhibits considerably faster solution times than the MIP-based formulations while preserving solution quality, with these advantages becoming significantly more pronounced as problem scale increases. For the most challenging instances, the method achieves speedups of up to 100× and solution quality superior to MIP-based formulations. © 2025, CC BY.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Risk-averse two-stage stochastic programming model for gate assignment problem under arrival time uncertainty

引用

Computers & Industrial Engineering 2025年 207卷

作者： Chenliang Zhang Kam K.H. Ng Zhongyi Jin Xuting Sun Yichen Qin Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong SAR China SILC Business School Shanghai University Shanghai 201899 China School of Economics and Management Shanghai Maritime University Shanghai 201306 China

The gate assignment problem (GAP) is a crucial task in airport operations, aiming to allocate aircraft to terminal gates and aprons. Airport operations are subject to various uncertainties, which can affect the quality and feasibility of the gate assignment plans. Therefore, it is essential to consider these uncertainties and associated risks when constructing an effective gate assignment plan. This paper proposes a risk-averse two-stage stochastic programming (RA-TSSP) model for the GAP, where aircraft arrival times are modelled as uncertain parameters, and the conditional value at risk is adopted as the risk measure. The proposed model is reformulated as a mixed-integer linear programming model, and the sample average approximation method is employed to enhance tractability. Additionally, we propose a risk-averse multi-cut Benders-based branch-and-cut (RA-MC-BBC) method to solve the RA-TSSP model for the GAP efficiently. The performance of the proposed RA-TSSP model is validated through numerical experiments and a case study at Xiamen Gaoqi Airport in China. Results show that the proposed model offers valuable insights for airport decision-makers in managing the uncertainty and risk associated with gate assignment decision-making. Additionally, the results of the scalability analysis demonstrate significant statistical improvements in the RA-MC-BBC method.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

A two-stage stochastic programming approach for enhancing seismic resilience of water pipe networks

引用

Computers & Industrial Engineering 2025年 207卷

作者： Azam Boskabadi Uthman Abiola Kareem Jay Michael Rosenberger Mohsen Shahandashti Binaya Pudasaini Department of Finance and Management Sciences Washington State University Washington USA Industrial Manufacturing and Systems Engineering Department University of Texas at Arlington TX USA Civil Engineering Department University of Texas at Arlington TX USA Bridge EIT and HDR Oregon USA

Earthquakes are sudden and inevitable disasters that can cause enormous losses and suffering, and having accessible water is critically important for earthquake victims. To address this challenge, utility managers do preventive procedures on water pipes periodically to withstand future earthquake damage. The existing seismic vulnerability models usually consider simple methods to find the pipes to rehabilitate with highest priority. In this research, we develop an optimization approach to determine which water pipes to rehabilitate subject to a limited budget to achieve a network with highest post-disaster serviceability. We propose a two-stage stochastic mixed integer nonlinear program (MINLP). The MINLP model cannot be solved by commercial optimization software, like BARON, even for problems with a very small number of scenarios. Consequently, we propose piecewise linear functions (PLF) to approximate the nonlinearity in the MINLP. Therefore, we formulate a mixed integer linear program (MILP) to approximate the MINLP. The optimization of the MILP is still challenging to solve, so we introduce a sequential heuristic algorithm to mitigate this computational challenge and find bounds for the approximated optimal solution. We tested this method on multiple water pipe networks based on a standard network from the literature, and we show that the sequential algorithm yields a solution within 2 % of optimality.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Solving two stage stochastic programming problems using ADMM

Research Square

引用

Research Square 2021年

作者： Mohammed, Nouralden Ali, M. Montaz School of Computer Science and Applied Mathematics University of The Witwatersrand Johannesburg South Africa

In this paper, we have dealt with the solution of a two-stage stochastic programming problem using ADMM. We have formulated the problem into a deterministic three-block separable optimization problem, and then we applied ADMM to solve it. We have established the theoretical convergence of ADMM to the optimal solution based on the concept of lower semicontinuity and the Kurdyka-Lojasiewicz property. We have compared ADMM with Progressive Hedging in terms of performance criteria using five benchmark problems from the literature. The comparison shows that ADMM outperforms Progressive Hedging. © 2021, CC BY.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Forecasting and stochastic programming Models to Address Uncertainty in the Trauma System Configuration Problem

Forecasting and Stochastic Programming Models to Address Unc...

引用

作者： Joshi, Alakshendra Texas State University – San Marcos

学位级别：硕士

Trauma is an essential aspect that must be considered by governing bodies when providing and expanding healthcare services across their jurisdiction. This thesis focuses on analyzing and forecasting physical trauma sustained from accidents, in environments both personal and work related, pertaining to individual injuries and to formulate a stochastic programming model that utilizes recorded injuries as demands to place trauma centers in the most optimal location. The first part of the thesis is to better understand the limitations faced by the existing trauma healthcare infrastructure by forecasting the expected number of people requiring the services of trauma facilities for both rural and urban locations in Texas. Five types of forecasting methods were analyzed to determine the best option to utilize for forecasting for individual data sets. The aim is to identify which forecasting model performs the best for given data sets that can be used to forecast patient demand for a given location and determine the optimal locations for trauma network expansion. The second part of the thesis proposes a stochastic programming model that considers variable demand in a specific geographical location. Trauma care services are a vital part of all healthcare-based network as timely accessibility is important for citizens. Trauma care access is even more relevant when unexpected events such as the COVID19 pandemic overload the capacity of the hospitals. Research literature has highlighted that access to trauma care is not even for all populations, especially when comparing rural and urban groups. Historically, the configuration of a trauma system was often not considered as a whole but instead hinged on the designation and verification of individual hospitals as trauma care centers. Recognition of the benefits of an inclusive trauma system has precipitated a more holistic approach. The optimal geographic configuration of trauma care centers is key to maximizing accessibility whil

关键词： Trauma Forecasting ARIMA stochastic programming Facility location

来源：评论

学校读者我要写书评

暂无评论

Mixed-Integer programming decomposition for stochastic programming with quantiles

arXiv

引用

arXiv 2021年

作者： Gouvine, Gabriel

In this paper, we present a novel approach for solving a maintenance scheduling problem from the French electricity grid company RTE, as presented in the ROADEF 2020 challenge. Our approach combines constraint generation with a new family of cutting planes to address the nonconvexity of the problem. We provide mathematical proofs and separation algorithms for the cutting planes and study the practical impact of our approach on the challenge instances. This method is applicable to a wide range of problems involving the modeling of uncertainty, and is shown to improve results significantly on real-world *** Codes 90-08 (Primary) 90-04, 90B25, 90C11 (Secondary) © 2021, CC BY-SA.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Robust Performance-Based Resource Provisioning Using a Steady-State Model for Multi-Objective stochastic programming

引用

IEEE TRANSACTIONS ON CLOUD COMPUTING 2019年第4期7卷 1068-1081页

作者： Tarplee, Kyle M. Maciejewski, Anthony A. Siegel, Howard Jay Anderson Univ Dept Phys Sci & Engn Anderson IN 46012 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resources to fulfill their high-performance computing needs. With cloud computing it is possible to tailor your computing infrastructure to perform best for your particular type of workload by selecting the correct number of machines of each type. This paper presents an efficient algorithm to find the best set of computing resources to allocate to the workload. This research is applicable to users provisioning cloud computing resources and to data center owners making purchasing decisions about physical hardware. Studies have shown that cloud computing machines have measurable variability in their performance. Some of the causes of performance variability include small changes in architecture, location within the datacenter, and neighboring applications consuming shared network resources. The proposed algorithm models the uncertainty in the computing resources and the variability in the tasks in a many-task computing environment to find a robust number of machines of each type necessary to process the workload. In addition, reward rate, cost, failure rate, and power consumption can be optimized, as desired, to compute Pareto fronts.

关键词： Planning stochastic programming vector optimization high performance computing scheduling resource management bag-of-tasks many-task computing energy-aware heterogeneous computing linear programming

来源：评论

学校读者我要写书评

暂无评论

Day-ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第12期13卷 2517-2525页

作者： Han, Bing Lu, Shaofeng Xue, Fei Jiang, Lin Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

Electric vehicle (EV) as dynamic energy storage systems could provide ancillary services to the grids. The aggregator could coordinate the charging/discharging of EV fleets to attend the electricity market to get profits. However, the aggregator profits are threatened by the uncertainty of the electricity market. In this study, an EV aggregator bidding strategy in the day-ahead market (DAM) is proposed, both reserve capacity and reserve deployment are considered. The novelty of this study is that: (i) The uncertainty of the reserve developments is addressed in terms of both time and amount. (ii) Scenario-based stochastic programming method is used to maximise the average aggregator profits based on one-year data. The proposed method, jointly considers the reserve capacity in the DAM and the reserve deployment requirements in the real-time market (RTM). (iii) The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. (iv) An owner-aggregator contract is designed, which is used to mitigate the economic inconsistency issue between the EV owners and the aggregator. Results verify the performance of the proposed strategy, that is the average aggregator profits are guaranteed by maximising reserve deployment payments and mitigating the penalties in RTM and thus the reserve deployment requirements uncertainty is well managed.

关键词： power markets energy storage stochastic programming electric vehicles day-ahead electric vehicle aggregator bidding strategy uncertain reserve market dynamic energy storage systems ancillary services charging discharging electricity market EV aggregator day-ahead market DAM reserve capacity reserve developments Scenario-based stochastic programming method average aggregator profits real-time market deployed reserve shortage owner-aggregator contract EV owners reserve deployment payments reserve deployment requirements uncertainty

来源：评论

学校读者我要写书评

暂无评论

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

引用

IET RENEWABLE POWER GENERATION 2019年第5期13卷 774-784页

作者： Wang, Yue Liang, Hao Dinavahi, Venkata Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large-scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4-bus and 33-bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity.

关键词： computational complexity distributed power generation energy storage renewable energy sources stochastic programming photovoltaic power systems smart power grids domestic appliances demand side management parallel stochastic programming energy storage management probabilistic renewable generation load models renewable power generation enormous economical benefits environmental benefits future smart grid ES management significant technical challenges volatile nature renewable energy sources ES units increasing size complexity random usage patterns parallel decomposition method large-scale stochastic programming distribution system 33-bus test distribution systems photovoltaic generation electrical appliance usage data

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：