检索结果-内蒙古大学图书馆

stochastic programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains

ENERGIES 2023年第10期16卷 4070-4070页

作者： Keith, Kolton Castillo-Villar, Krystel K. Univ Texas San Antonio Mech Engn Dept One UTSA Circle San Antonio TX 78249 USA Univ Texas San Antonio Texas Sustainable Energy Res Inst One UTSA Circle San Antonio TX 78249 USA

Biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for biomass conversion. In the pyrolysis process, the biomass decomposes into three byproducts: bio-oil (60-75%), biochar (15-25%), and syngas (10-20%), depending on the feedstock and its composition. The energy required to convert the biomass varies depending on the levels of cellulose, hemicellulose, and lignin. This work proposes a novel two-stage stochastic model that designs an efficient biomass supply chain mindful of the trade-offs between pyrolysis byproducts (bioethanol and biochar). Remarkably, the model integrates biomass quality-related costs associated with moisture and ash content such as the energy consumption of preprocessing equipment and boiler maintenance due to excess ash. Biomass quality directly affects the production yield as well as the total cost of production and distribution. The results from our case study indicate a shortage of biomass from the suppliers to fulfill the demand for biochar from the power plants and bioethanol from the cities. Furthermore, the bioethanol price has the most impact on the total supply chain according to our sensitivity analysis.

关键词： optimization stochastic programming supply chains biofuels biomass

来源：评论

学校读者我要写书评

暂无评论

Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply

引用

RENEWABLE ENERGY 2022年第0期186卷 378-393页

作者： Guo, Changqiang Hu, Hao Wang, Shaowen Rodriguez, Luis F. Ting, K. C. Lin, Tao Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China Univ Illinois Dept Geog & Geog Informat Sci Champaign IL 61801 USA Univ Illinois Dept Agr & Biol Engn Urbana IL 61801 USA Zhejiang Univ Int Campus Haining 314400 Zhejiang Peoples R China

Spatiotemporal uncertainties in the collection of crop residues pose great challenges to the development of a long-term and economic biomass-to-biofuel supply chain network (BSCN). A multiperiod stochastic programming (SP) model considering uncertain collectible corn stover removal and farmer participation rates is developed. The SP model is compared with the deterministic programming for the expected scenario (DPES) model to provide decision-making support for BSCN in two different periods. With the statistical results of separate deterministic programming models for each scenario generated randomly based on the normal distribution as a reference, the economic performance of the SP and DPES models is compared in the model development period and then confirmed in the model validation period. A county-level case study with a 10-year development and a 3-year validation period is applied. The economic performance of the SP model is comparable to that of the DPES model in the development period, and the SP model achieves much higher cost savings in the validation period. Although biomass transportation cost is the most unstable cost component, the variation in bioethanol production cost is largely consistent with that in biomass purchase cost. The SP model demonstrates stronger robustness to uncertainty than the DPES model.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Biomass-to-bioethanol supply chain network Supply uncertainty stochastic programming Spatiotemporal variability Model development and validation

来源：评论

学校读者我要写书评

暂无评论

Improving constants of strong convexity in linear stochastic programming

引用

OPERATIONS RESEARCH LETTERS 2022年第1期50卷 76-83页

作者： Claus, Matthias Spuerkel, Kai Univ Duisburg Essen Fac Math Campus EssenThea Leymann Str 9 D-45127 Essen Germany

We derive formulas for constants of strong convexity (CSCs) of expectation functions encountered in two-stage stochastic programs with linear recourse. One of them yields a CSC as the optimal value of a certain quadratically constrained quadratic program, another one in terms of the thickness of the feasibility polytope of the dual problem associated to the recourse problem. CSCs appear in Hoelder-type estimates relating the distance of optimal solution sets of stochastic programs to a suitable distance of underlying probability distributions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： stochastic programming Linear recourse Strong convexity

来源：评论

学校读者我要写书评

暂无评论

Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and stochastic programming

Controlling Large Electric Vehicle Charging Stations via Use...

引用

2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Puech, Alban Rigaut, Tristan Templier, William Tournoud, Maud Schneider Electric Schneider Digital - Ai Hub Grenoble France Lausanne Switzerland

ISBN: (纸本)9789531842976

This paper introduces an Electric Vehicle Charging Station (EVCS) model that incorporates real-world constraints, such as slot power limitations, contract threshold overruns penalties, or early disconnections of electric vehicles (EVs). We propose a formulation of the problem of EVCS control under uncertainty, and implement two Multi-Stage stochastic programming approaches that leverage user-provided information, namely, Model Predictive Control and Two-Stage stochastic programming. The model addresses uncertainties in charging session start and end times, as well as in energy demand. A user's behavior model based on a sojourn-time-dependent stochastic process enhances cost reduction while maintaining customer satisfaction. The benefits of the two proposed methods are showcased against two baselines over a 22-day simulation using a real-world dataset. The two-stage approach demonstrates robustness against early disconnections by considering a wider range of uncertainty scenarios for optimization. The algorithm prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline. Additionally, the algorithm striking the best balance between cost and user satisfaction exhibits a mere 3% relative cost increase compared to the theoretically optimal baseline - for which the nonanticipativity constraint is relaxed - while attaining 94% and 84% of the user satisfaction performance in the two used satisfaction metrics. © 2024 IEEE.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Gaining-Sharing Knowledge Based Algorithm for Solving stochastic programming Problems

引用

Computers, Materials & Continua 2022年第5期71卷 2847-2868页

作者： Prachi Agrawal Khalid Alnowibet Ali Wagdy Mohamed Yogananda School of Artificial Intelligence Computers&Data ScienceShoolini UniversitySolan173229India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt

This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.

关键词： Gaining-sharing knowledge based algorithm metaheuristic algorithms stochastic programming stochastic transportation problem

来源：评论

学校读者我要写书评

暂无评论

Optimal Operation of Multi-energy Microgrids: A Hybrid Methodology Using stochastic programming and Generalized Nash Bargaining

Optimal Operation of Multi-energy Microgrids: A Hybrid Metho...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Liu, Hualong Tang, Wenyuan North Carolina State University Department of Electrical and Computer Engineering Raleigh United States

ISBN: (纸本)9798350376067

With the increasing penetration of distributed energy resources (DERs) in the power system, the microgrid (MG) as a relatively independent system has been widely used and developed. The MG can smooth the output fluctuation of renewables through the interaction of the main grid, renewables, loads, and energy storage systems, which is conducive to friendly access and local consumption of renewables. The interconnection of adjacent MGs to form a MG cluster system can effectively improve the reliability, economy, and low carbon of the system. In order to optimize the operation of the whole MG alliance and distribute the benefits reasonably among MGs, based on stochastic programming and generalized Nash bargaining (GNB) theory, an optimal cooperative operation model of the multi-agent multi-energy MG system considering uncertainty and carbon trading is proposed in this paper. First, carbon capture systems (CCSs) and power-to-gas (P2G) devices are integrated into traditional combined heat and power (CHP) units. Second, using statistical programming to deal with uncertainty, the optimal operation model of the MG alliance is established. The model is decomposed into two subproblems: the social welfare maximization subproblem and the energy trading payment bargaining subproblem. Finally, to protect the privacy of each agent, two algorithms for solving the subproblems are proposed based on the alternating direction method of multipliers (ADMM). The asymmetric bargaining quantifying contributions is employed to achieve fair distribution of benefits. The effectiveness, superiority, and scalability of the proposed model is verified through case studies. © 2024 IEEE.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Two-stage stochastic programming under multivariate risk constraints with an application to humanitarian relief network design

引用

MATHEMATICAL programming 2022年第1期191卷 7-45页

作者： Noyan, Nilay Merakli, Merve Kucukyavuz, Simge Sabanci Univ Ind Engn Program Istanbul Turkey Northwestern Univ Ind Engn & Management Sci Evanston IL USA

In this study, we consider two classes of multicriteria two-stage stochastic programs in finite probability spaces with multivariate risk constraints. The first-stage problem features multivariate stochastic benchmarking constraints based on a vector-valued random variable representing multiple and possibly conflicting stochastic performance measures associated with the second-stage decisions. In particular, the aim is to ensure that the decision-based random outcome vector of interest is preferable to a specified benchmark with respect to the multivariate polyhedral conditional value-at-risk or a multivariate stochastic order relation. In this case, the classical decomposition methods cannot be used directly due to the complicating multivariate stochastic benchmarking constraints. We propose an exact unified decomposition framework for solving these two classes of optimization problems and show its finite convergence. We apply the proposed approach to a stochastic network design problem in the context of pre-disaster humanitarian logistics and conduct a computational study concerning the threat of hurricanes in the Southeastern part of the United States. The numerical results provide practical insights about our modeling approach and show that the proposed algorithm is computationally scalable.

关键词： stochastic programming Multicriteria optimization Risk-averse two-stage Multivariate risk Conditional value-at-risk stochastic dominance Benders decomposition Branch-and-cut Network design Pre-disaster Humanitarian relief

来源：评论

学校读者我要写书评

暂无评论

Data-driven scenario generation for two-stage stochastic programming

引用

CHEMICAL ENGINEERING RESEARCH & DESIGN 2022年 187卷 206-224页

作者： Bounitsis, Georgios L. Papageorgiou, Lazaros G. Charitopoulos, Vassilis M. UCL Sargent Ctr Proc Syst Engn Dept Chem Engn Torrington Pl London WC1E 7JE England

Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, the efficient manipulation of large amount of data for the uncertain parameters constitutes a crucial condition for effectively tackling stochastic programming problems. In this context, this work proposes a new data-driven Mixed-Integer Linear programming (MILP) model for the Distribution & Moment Matching Problem (DMP). For cases with multiple uncertain parameters a copula -based simulation of initial scenarios is employed as preliminary step. Moreover, the in-tegration of clustering methods and DMP in the proposed model is shown to enhance computational performance. Finally, we compare the proposed approach with state-of-the-art scenario generation methodologies. Through a number of case studies we high-light the benefits regarding the quality of the generated scenario trees by evaluating the corresponding obtained stochastic solutions.(c) 2022 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Scenario generation stochastic programming Data -driven optimisation Moment Matching Problem Copulas

来源：评论

学校读者我要写书评

暂无评论

Efficient stochastic programming in Julia

引用

INFORMS JOURNAL ON COMPUTING 2022年第4期34卷 1885-1902页

作者： Biel, Martin Johansson, Mikael Kungliga Tekniska Hogskolan Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst SE-10044 Stockholm Sweden

We present stochastic ***, a user-friendly and powerful open-source framework for stochastic programming written in the Julia language. The framework includes both modeling tools and structure-exploiting optimization algorithms. stochastic programming models can be efficiently formulated using an expressive syntax, and models can be instantiated, inspected, and analyzed interactively. The framework scales seamlessly to distributed environments. Small instances of a model can be run locally to ensure correctness, whereas larger instances are automatically distributed in a memory-efficient way onto supercomputers or clouds and solved using parallel optimization algorithms. These structure-exploiting solvers are based on variations of the classical L-shaped, progressive-hedging, and quasi-gradient algorithms. We provide a concise mathematical background for the various tools and constructs available in the framework along with code listings exemplifying their usage. Both software innovations related to the implementation of the framework and algorithmic innovations related to the structured solvers are highlighted. We conclude by demonstrating strong scaling properties of the distributed algorithms on numerical benchmarks in a multinode setup.

关键词： stochastic programming distributed computations Julia language

来源：评论

学校读者我要写书评

暂无评论

Designing master surgery schedules with downstream unit integration via stochastic programming

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期299卷 834-852页

作者： Santos, Daniel Marques, Ines Univ Lisbon Ctr Management Studies Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal

Surgical activity has a substantial impact in all areas of hospitals. Additionally, social concerns arise related to equity and speed of access. Therefore, operating room management is paramount in modern society. This work studies the master surgery scheduling problem which is the problem of assigning surgical specialties to operating room blocks, which represent a shift of an operating room. For a master surgery schedule to be applicable in practice, multiple considerations must be taken into account. The particular focus of this work is in the integration of downstream units (i.e., wards or the ICU). Although, in a tactical planning scenario, operational bed requirements are unknown, these may be estimated based on historical data. We propose a novel stochastic programming model that captures the uncertainty in the bed requirements, with a recourse function that penalizes the overutilization of beds. A solution approach based on Benders decomposition is developed and results for generated instances mimicking real-life data are presented. (c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in health services Master surgery scheduling Downstream units stochastic programming Benders decomposition

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：