检索结果-内蒙古大学图书馆

distributionally robust optimization for the Multi-Period Multi-Item Lot-Sizing Problems Under Yield Uncertainty

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11731-11752页

作者： Metzker, Paula Thevenin, Simon Adulyasak, Yossiri Dolgui, Alexandre CNRS UMR 6004 LS2N MT Atlantique F-44307 Nantes France HEC Montreal Montreeal PQ H3T 2A7 Canada GERAD Montreal PQ H3T 1J4 Canada

Yield uncertainty is an important issue in various industries such as agriculture, food, and textile where the production output relies on uncontrollable factors and fluctuating raw material quality. To systematically leverage data to deal with uncertainty cost-effectively, distributionally robust optimization combines the strengths of stochastic programming and robust optimization by optimizing the expected costs against an ambiguity set that defines possible distributions. In this work, we leverage a data-driven robust optimization framework and formulate a mixed-integer distributionally robust multi-item lot-sizing model with uncertain production yield to determine a robust production plan. To this end, we use a scenario-wise formulation that partitions the available data into scenarios that define different patterns influencing the quality of the product and production process. In addition, we apply the proposed approach to real-world data of a case study to demonstrate the effectiveness of the proposed framework in dealing with yield uncertainty. Our experimental results show that distributionally robust plans lead to more effective cost-saving strategies and decreased risk of stock-outs. Additionally, our findings suggest that the proposed model exhibits lower sensitivity to variations in production yield realizations and it is more proficient in incorporating historical data into the decision-making process. This results in a more effective response to challenges encountered within the production system under yield uncertainty.

关键词： Production Uncertainty optimization Stochastic processes Industries Automation Programming Probability distribution Costs Raw materials Lot-sizing production planning yield uncertainty distributionally robust optimization

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization for minimizing price fluctuations in quota system

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 193卷

作者： Xie, Chi Cui, Zheng Long, Daniel Zhuoyu Qi, Jin Beijing Inst Technol Sch Management Beijing 100081 Peoples R China Zhejiang Univ Sch Management Hangzhou 310058 Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Kowloon Hong Kong Peoples R China

Quota systems play a crucial role in regulating public-interest goods and controlling negative externalities, with a primary focus on social impacts rather than economic benefits. This paper examines the decision-making process for quota release, aiming to control growth rates and ensure price stability over time. We first develop a chance-constrained problem for quota systems, solving it using sample average approximation. Due to computational demands, alternative approximation methods are explored. We consider two types of quota systems: mature systems with known distributions and newly established systems with distributional ambiguity. For mature systems, Conditional Value-at-Risk (CVaR) is used to approximate the chance constraint, while for newly established systems, worst-case CVaR is employed within a robust optimization framework and the binary search algorithm is derived to efficiently solve the problem. The proposed models' effectiveness is validated through computational studies using data from Singapore's Vehicle Quota System. With known distributions, our CVaR sample average approximation (CVaR-SAA) model outperforms traditional models, reducing violation probability by more than 56.32%. With distributional ambiguity, worst-case CVaR approximation robust optimization (WCVaR-RO) model provides superior solutions, particularly in maximum violation probability (MVP). In the most notable case, WCVaR-RO reduces the MVP by over 53.37%. This research offers valuable insights into the management of quota systems.

关键词： Quota system distributionally robust optimization Price fluctuation Conditional value at risk

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization Under Distorted Expectations

引用

OPERATIONS RESEARCH 2025年第2期73卷 iii-viii, 583-1150, C2-C3页

作者： Cai, Jun Li, Jonathan Yu-Meng Mao, Tiantian Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada Univ Ottawa Telfer Sch Management Ottawa ON K1N 6N5 Canada Univ Sci & Technol China Int Inst Finance Sch Management Hefei 230026 Anhui Peoples R China

distributionally robust optimization (DRO) has arisen as an important paradigm for addressing the issue of distributional ambiguity in decision optimization. In the case in which a decision maker is not risk neutral, the most common scheme applied in DRO for capturing the risk attitude is to employ an expected utility functional. In this paper, we propose to address a decision maker's risk attitude in DRO by following an alternative scheme known as dual expected utility. In this scheme, a distortion function is applied to convert physical probabilities into subjective probabilities so that the resulting expectation, called a distorted expectation, captures the decision maker's risk attitude. Unlike an expected utility functional, which is linear in probability, in the dual scheme, the distorted expectation is generally nonlinear in probability. We distinguish DRO based on distorted expectations by coining the term "distributionally robust distortion risk optimization" (DRDRO) and show that DRDRO problems can be equally, if not more, tractable to solve as DRO problems based on utility functionals. Our tractability results hold for any distortion function, and hence, our scheme provides more flexibility in capturing more realistic forms of risk attitudes. These include, as an important example, the inverse S-shaped distortion functionals in cumulative prospect theory. We demonstrate through a numerical example that a production manager who overly weights "very good" and "very bad" outcomes may act as if the manager is risk averse when distributional ambiguity is considered.

关键词： distributionally robust optimization distortion risk measure convex risk measure convex envelope

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization OF THE VEHICLE ROUTING PROBLEM WITH UNCERTAIN CUSTOMERS

引用

JOURNAL OF INDUSTRIAL AND MANAGEMENT optimization 2025年第3期21卷 1983-2006页

作者： Wang, Xiaopeng Zhao, Jinling Univ Sci & Technol Beijing Sch Math & Phys 30 Xueyuan Rd Beijing 100083 Peoples R China

With the rise of e-commerce, the express logistics industry has been developing rapidly, and the Vehicle Routing Problem (VRP) has been widely concerned. In this paper, we focus on the Vehicle Routing Problem with Uncertain Customers (VRPUC), which introduces the uncertain customers set on the basis of the capacitated vehicle routing problem, where the customers demands are uncertain. We first filter out potential uncertain customers using historical data, and then introduce a distributionally robust optimization model based on the cross-moment ambiguity set that minimizes the Essential Riskiness Index (ERI) of all vehicles, which can help mitigate the magnitude and probability of vehicle overload. The model can be reformulated as a mixed integer semidefinite optimization problem. We employ a branch-and-cut algorithm to tackle a relaxed version of this problem. Once the optimal solution is identified, a callback procedure is initiated to verify whether the current optimal solution meets the positive semidefinite constraint. If it does, the solution is confirmed as optimal for the original problem. If not, a lazy constraint is introduced to exclude the solution from the feasible region. Numerical experiments demonstrate that our model outperforms stochastic programming models across various performance indicators.

关键词： Vehicle routing problem uncertain demands distributionally robust optimization essential riskiness index mixed integer semidefinite optimization

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization using optimal transport for Gaussian mixture models

引用

optimization AND ENGINEERING 2024年第3期25卷 1571-1596页

作者： Kammammettu, Sanjula Yang, Shu-Bo Li, Zukui Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

distributionally robust optimization (DRO) is an increasingly popular approach for optimization under uncertainty when the probability distribution of the uncertain parameter is unknown. Well-explored DRO approaches in literature, such as Wasserstein DRO, do not make any specific assumptions on the nature of the candidate distributions considered in the ambiguity set. However, in many practical applications, the uncertain parameter may be sourced from a distribution that can be well modeled as a Gaussian Mixture Model (GMM) whose components represent the different subpopulations the uncertain parameter may belong to. In this work, we propose a new DRO method based on an ambiguity set constructed around a GMM. The proposed DRO approach is illustrated on a numerical example as well as a portfolio optimization case study for uncertainty sourced from various distributions. The results obtained from the proposed DRO approach are compared with those from Wasserstein DRO, and are shown to be superior in quality with respect to out-of-sample performance.

关键词： distributionally robust optimization Gaussian mixture model Ambiguity set Optimal transport

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization of Photovoltaic Power With Lifted Linear Decision Rule for Distribution System Voltage Regulation

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2024年第2期15卷 758-772页

作者： Xu, Xiaoyuan Gao, Yuanhai Wang, Han Yan, Zheng Shahidehpour, Mohammad Tan, Zhenfei Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China IIT Galvin Ctr Elect Innovat Chicago IL 60616 USA

This article proposes a lifted linear decision rule (LLDR) as a photovoltaic (PV) power control strategy for distribution system voltage regulation. First, the active-reactive power control is designed as a linear function in a lifted space of PV power, which is presented as a piecewise linear function without auxiliary integer variables for benefiting the parameter optimization of control functions. Then, a hierarchical central-local voltage regulation framework is designed. In the central control stage, the constant part of LLDR determines the reactive power of PV units, which is also coordinated with other voltage regulation devices. In the local control stage, the adjustable part of LLDR would control the PV power in real time for reducing voltage deviations. Finally, optimization problems and their solution methods are developed to obtain LLDR parameters. In particular, robust optimization (RO) and distributionally robust optimization (DRO) problems are designed to obtain the affine coefficients of LLDR considering multiple PV power uncertainty models. The tractable formula of RO is derived based on the convex hull of PV power uncertainty sets in the lifted space, and Benders decomposition is combined with a distribution separation method to solve the DRO problem. The proposed method is tested on distribution systems with actual PV power data, and the results are compared with those of other linear and piecewise-linear control methods to validate the effectiveness of the proposed method in diminishing voltage deviations caused by variable PV power.

关键词： Voltage control Reactive power Power control optimization Uncertainty Power distribution Regulation Photovoltaic power uncertainty optimization of power distribution system voltage regulation lifted linear decision rule distributionally robust optimization convex hull

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization of electric-thermal-hydrogen integrated energy system considering source-load uncertainty

引用

ENERGY 2025年 316卷

作者： Ma, Miaomiao Long, Zijuan Liu, Xiangjie Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX USA Pohang Univ Sci & Technol Pohang 790330 South Korea

With the increasing penetration of renewable energy and the growing energy demand from users, the scheduling of integrated energy system (IES) faces significant challenges. A data-driven distributionally robust optimization (DRO) approach is proposed to solve the scheduling problem under source-load uncertainty. Firstly, conditional generative adversarial networks (CGAN) are utilized to generate scenarios for wind and solar power outputs as well as electrical and thermal loads. The K-medoids clustering algorithm is then used to obtain typical scenarios. Secondly, a comprehensive norm composed of 1-norm and infinity- norm is applied to constrain the typical scenarios to construct an uncertainty set. Finally, a two-stage DRO model of electric- thermal-hydrogen integrated energy system (ETH-IES) is established. The simulation results demonstrate that the proposed method effectively improves system economy, with a 2.1% reduction in operating cost compared to traditional robust optimization, while ensuring efficient model solving.

关键词： distributionally robust optimization Integrated energy system Deep learning Hydrogen storage system Multiple uncertainties

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization for pre-disaster facility location problem with 3D printing

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 193卷

作者： Sun, Peng Zhao, Dongpan Chen, Qingxin Yu, Xinyao Zhu, Ning Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Tianjin Univ Lab Computat & Analyt Complex Management Syst CACMS Tianjin 300072 Peoples R China Hefei Univ Technol Philosophy & Social Sci Lab Data Sci & Smart Soc G Minist Educ Hefei 230009 Anhui Peoples R China

The ongoing advancement of 3D printing technology provides an innovative approach to addressing challenges in disaster relief operations. By utilizing a variety of printing materials, 3D printers can produce essential disaster relief resources needed for disaster relief, effectively satisfying the varied demands that arise after disasters. This paper examines the joint optimization of pre-disaster and post-disaster humanitarian operations. Given the significant unpredictability of natural disasters, we introduce a two-stage distributionally robust optimization model to tackle the uncertainty in the demand for various relief resources. The first stage of the model involves decisions related to pre-disaster facility location, 3D printer deployment, and resource allocation. The second stage model addresses the post-disaster rescue activities, including decisions on the production and transportation decisions of relief resources. To address demand uncertainty, we propose an ambiguity set using the Wasserstein metric and reformulate the two-stage distributionally robust optimization model into a tractable formulation. To solve this problem, we employ a Benders decomposition algorithm with an acceleration strategy. The performance of our proposed model and algorithm is evaluated via a real-world case. Numerical experiments reveal that our distributionally robust optimization model outperforms the benchmark model across various metrics. Additionally, we conduct a series of effect analyses and provide managerial insights for decision-makers involved in disaster relief operations.

关键词： Disaster relief 3D printing Two-stage optimization distributionally robust optimization Wasserstein distance-based ambiguity set

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization of On-Orbit Resource Scheduling for Remote Sensing in Space-Air-Ground Integrated 6G Networks

引用

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第1期43卷 382-395页

作者： Sun, Jiachen Chen, Xu Jiang, Chunxiao Guo, Song Tsinghua Univ Dept Elect Engn State Key Lab Space Network & Commun Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

With the rapid development of on-board computing technology, on-orbit information processing has become a new direction for reducing service response delays and improving the quality of space-based information services. Especially in space-air-ground integrated applications in 6G networks, remote sensing image processing tasks are highly important because of their critical role in applications such as environmental monitoring and public safety. However, the fluctuations in data volume due to significant scene differences, along with the limitations in individual satellite capabilities caused by size and power constraints, present new challenges for on-orbit image processing. To address these challenges, we model a data-driven on-orbit resource scheduling problem for space-air-ground integrated networks based on distributionally robust optimization, aiming to minimize the average image processing delay. We first construct an ambiguity set based on the Wasserstein distance and the historical distribution of image data, which helps transform the original upper-bound expectation problem into an explicitly expressed mixed-integer nonlinear (MINLP) problem. Furthermore, to reduce complexity and expedite the solution process, we decouple the MINLP problem into three subproblems using the block coordinate descent method and designed an iterative solving algorithm. The numerical results demonstrate that our proposed method achieves better fitting accuracy than traditional methods and reduces the average image processing delay.

关键词： Remote sensing optimization Image processing Delays Scheduling Accuracy 6G mobile communication distributionally robust optimization on-orbit resource scheduling remote sensing 6G networks

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization for green hydrogen plant planning considering extreme scenarios

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 133卷 112-123页

作者： Oroya, Luis A. Cortez, Juan Carlos Terada, Lucas Zenichi Silva, Jessica A. A. Rider, Marcos J. Univ Estadual Campinas UNICAMP Sch Elect & Comp Engn FEEC BR-13083852 Campinas SP Brazil

As efforts to reduce carbon emissions increase, green hydrogen from sources like wind (WD) and photovoltaic (PV) power is a promising solution for industrial decarbonization. However, the variability of renewable energy sources (RES) challenges the planning and operation of green hydrogen plants. To address these challenges, this paper proposes a novel two-stage extreme distributionally robust optimization (X-DRO) model for sizing distributed energy resources efficiently for green hydrogen production and selling. The proposed model minimizes the total capital (CAPEX) and operating expenditures (OPEX) while ensuring the robustness of performance under an uncertain renewable energy supply. The methodology includes selecting representative and extreme scenarios to input into the model, representing the variability of RES. In the first stage, capacity planning decisions, including the sizing of PV and WD units, battery energy storage systems (BESS), hydrogen storage tanks (HSTs), and electrolyzers (ELs), are considered. The second stage addresses the operating decisions concerning power exchange with the grid, hydrogen production, and storage under worst-case scenario probabilities of RES generation. The column-and-constraint generation (C&CG) algorithm is applied to solve the X-DRO model. Simulations show that the proposed model balances economic efficiency and robustness compared to robust optimization (RO) and stochastic optimization (SO) models. A comparison between the X-DRO and DRO models highlights the importance of considering extreme cases for resilient planning.

关键词： Column-and-constraint generation algorithm distributionally robust optimization Extreme scenario Green hydrogen plant planning

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：