检索结果-内蒙古大学图书馆

A three-stage matheuristic for the blood stochastic inventory routing problem

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 200卷

作者： Yu, Vincent F. Salsabila, Nabila Yuraisyah Gunawan, Aldy Siswanto, Nurhadi Natl Taiwan Univ Sci & Technol Dept Ind Management 43 Sect 4Keelung Rd Taipei 10607 Taiwan Singapore Management Univ Sch Comp & Informat Syst 80 Stamford Rd Singapore 178902 Singapore Inst Teknol Sepuluh Nopember Dept Ind & Syst Engn Raya ITS Rd Surabaya 60111 Indonesia

This research introduces a blood distribution system under vendor-managed inventory that considers uncertain supply and demand. We present it as the Blood stochastic Inventory Routing Problem, formulating it as a two-stage stochastic programming model. To solve this problem, this study proposes a three-stage matheuristic that combines a perturbation heuristic, Adaptive Large Neighborhood Search, and an exact approach. From historical data of Surabaya Blood Center in Indonesia, six sets of new instances are generated under different settings. Computational results show that our proposed three-stage matheuristic outperforms CPLEX and a two-stage matheuristic by gaining optimal or better solutions within a significantly shorter computational time. Moreover, it is robust for solving large problems, as evidenced by its ability to find high-quality solutions within a reasonable time. Finally, managerial insights are derived by evaluating performance matrices under different uncertainty levels and scenarios. According to these insights, some practical strategies are suggested with respect to the decision-maker's risk preferences and demand characteristics.

关键词： Blood distribution Inventory routing problem stochastic programming Matheuristic

来源：评论

学校读者我要写书评

暂无评论

Distributed stochastic energy coordination for residential prosumers: Framework and implementation

引用

SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 38卷

作者： Dominguez, Juan A. Agbossou, Kodjo Henao, Nilson Nagarsheth, Shaival H. Campillo, Javier Rueda, Luis Univ Quebec Trois Rivieres Inst Rech Hydrogene Trois Rivieres PQ G9A 5H7 Canada Inst Rech Hydroquebec Lab Technol Energie Shawinigan PQ G9N 0C5 Canada Univ Tecnol Bolivar Cartagena 130001 Bolivar Colombia Inst Planning & Promot Energy Solut Noninterconnec Bogota 110131 Colombia

This paper addresses the problem of energy coordination among customers in a local energy market to meet heating needs. Accordingly, this paper proposes a demand response framework for the energy coordination of multiple residential prosumers in a hierarchical coordination game -theoretic scenario. That consists of a benevolent coordinator and multiple residential prosumers integrated with solar PV under demand uncertainties as a consequence of forecasting errors in weather data and non-shiftable load. The coordinator and customers are considered to be a leader and various followers, respectively. The coordinator employs a cost -shared strategy to reduce peak demand. Customers maximize individual welfare by employing stochastic programming through demand flexibility realized via electric baseboard heaters and electric thermal storage. Subsequently, the proposed strategy is implemented through a cyber-physical infrastructure incorporating a distributed computing platform with embedded systems and extensive simulations using accurate weather and real -life energy consumption data. A comparative analysis is performed using a deterministic baseline with perfect information, i.e. no forecasting errors. In addition, the proposed mechanism is tested on an adapted CIGRE low -voltage benchmark system. Simulation results demonstrate the techno-economical feasibility of the proposed solution in uncertain environments. Furthermore, they recommend practical strategies for short-term planning of grids highly penetrated by renewables and intelligent devices by increasing their flexibility and leveraging salient features of cyber-physical systems.

关键词： Demand response stochastic programming Electric thermal storage Predictive control Internet of things Cyber-physical infrastructure Solar photovoltaics

来源：评论

学校读者我要写书评

暂无评论

EFFICIENT ALGORITHMS FOR DISTRIBUTIONALLY ROBUST stochastic OPTIMIZATION WITH DISCRETE SCENARIO SUPPORT

引用

SIAM JOURNAL ON OPTIMIZATION 2021年第3期31卷 1690-1721页

作者： Zhang, Zhe Ahmed, Shabbir Lan, Guanghui Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Georgia Inst Technol Atlanta GA 30332 USA

Recently, there has been a growing interest in distributionally robust optimization (DRO) as a principled approach to data-driven decision making. In this paper, we consider a distributionally robust two-stage stochastic optimization problem with discrete scenario support. While much research effort has been devoted to tractable reformulations for DRO problems, especially those with continuous scenario support, few efficient numerical algorithms are developed, and most of them can neither handle the nonsmooth second-stage cost function nor the large number of scenarios K effectively. We fill the gap by reformulating the DRO problem as a trilinear min-max-max saddle point problem and developing novel algorithms that can achieve an O(1/E) iteration complexity which only mildly depends on K. The major computations involved in each iteration of these algorithms can be conducted in parallel if necessary. Besides, for solving an important class of DRO problems with the Kantorovich ball ambiguity set, we propose a slight modification of our algorithms to avoid the expensive computation of the probability vector projection at the price of an O( K) times more iterations. Finally, preliminary numerical experiments are conducted to demonstrate the empirical advantages of the proposed algorithms.

关键词： xEX pEP Key words stochastic programming convex optimization distributionally robust optimization smoothing bundle level primal-dual smoothing

来源：评论

学校读者我要写书评

暂无评论

Joint Computation Offloading and Resource Allocation for MEC-Enabled IoT Systems With Imperfect CSI

引用

IEEE INTERNET OF THINGS JOURNAL 2021年第5期8卷 3462-3475页

作者： Wang, Jun Feng, Daquan Zhang, Shengli Liu, An Xia, Xiang-Gen Shenzhen Univ Shenzhen Key Lab Digital Creat Technol Guangdong Prov Engn Lab Digital Creat Technol Shenzhen 518060 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

Mobile-edge computing (MEC) is considered as a promising technology to reduce the energy consumption (EC) and task accomplishment latency of smart mobile user equipments (UEs) by offloading computation-intensive tasks to the nearby MEC servers. However, the Quality of Experience (QoE) for computation highly depends on the wireless channel conditions when computation tasks are offloaded to MEC servers. In this article, by considering the imperfect channel-state information (CSI), we study the joint offloading decision, transmit power, and computation resources to minimize the weighted sum of EC of all UEs while guaranteeing the probabilistic constraint in multiuser MEC-enabled Internet-of-Things (IoT) networks. This formulated optimization problem is a stochastic mixed-integer nonconvex problem and challenging to solve. To deal with it, we develop a low-complexity two-stage algorithm. In the first stage, we solve the relaxed version of the original problem to obtain offloading priorities of all UEs. In the second stage, we solve an iterative optimization problem to obtain a suboptimal offloading decision. As both stages include solving a series of nonconvex stochastic problems, we present a constrained stochastic successive convex approximation-based algorithm to obtain a near-optimal solution with low complexity. The numerical results demonstrate that the proposed algorithm provides comparable performance to existing approaches.

关键词： Task analysis Servers Optimization Resource management Internet of Things Wireless communication Delays Computation offloading optimization resource allocation stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Constraint generation for risk averse two-stage stochastic programs

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期288卷 194-206页

作者： Minguez, R. van Ackooij, W. Garcia-Bertrand, R. ESCISSION Co Ciudad Real Spain EDF Elect France R&D 7 Blvd Gaspard Monge F-91120 Palaiseau France Univ Castilla La Mancha Dept Elect Engn Ciudad Real Spain

A significant share of stochastic optimization problems in practice can be cast as two-stage stochastic programs. If uncertainty is available through a finite set of scenarios, which frequently occurs, and we are interested in accounting for risk aversion, the expectation in the recourse cost can be replaced with a worst-case function (i.e., robust optimization) or another risk-functional, such as conditional value-at-risk. In this paper we are interested in the latter situation especially when the number of scenarios is large. For computational efficiency we suggest a (clustering and) constraint generation algorithm. We establish convergence of these two algorithms and demonstrate their effectiveness through various numerical experiments. (C) 2020 Elsevier B.V. All rights reserved.

关键词： stochastic programming Decision analysis under uncertainty CVaR Risk aversion

来源：评论

学校读者我要写书评

暂无评论

Quasi-Monte Carlo methods for two-stage stochastic mixed-integer programs

引用

MATHEMATICAL programming 2021年第1-2期190卷 361-392页

作者： Leovey, H. Roemisch, W. Axpo Struct Energy Management Team Baden Switzerland Humboldt Univ Inst Math Berlin Germany

We consider randomized QMC methods for approximating the expected recourse in two-stage stochastic optimization problems containing mixed-integer decisions in the second stage. It is known that the second-stage optimal value function is piecewise linear-quadratic with possible kinks and discontinuities at the boundaries of certain convex polyhedral sets. This structure is exploited to provide conditions implying that first and higher order terms of the integrand's ANOVA decomposition (Math. Comp. 79 (2010), 953-966) have mixed weak first order partial derivatives. This leads to a good smooth approximation of the integrand and, hence, to good convergence rates of randomized QMC methods if the effective (superposition) dimension is low.

关键词： stochastic programming Two-stage Mixed-integer Sampling Quasi-Monte Carlo Haar measure

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Resilience of Operational Microgrids Through a Two-Stage Scheduling Strategy Considering the Impact of Uncertainties

引用

IEEE ACCESS 2021年 9卷 18454-18464页

作者： Younesi, Abdollah Shayeghi, Hossein Siano, Pierluigi Safari, Amin Alhelou, Hassan Haes Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil *** Iran Univ Salerno Dept Innovat & Management Syst I-84084 Fisciano Italy Azerbaijan Shahid Madani Univ Dept Elect Engn Tabriz *** Iran Tishreen Univ Fac Mech & Elect Engn Dept Elect Power Engn Latakia 2230 Syria

This article deals with the stochastic scheduling of a microgrid (MG) to balance the economical and resilience metrics. In the proposed model, the MG resilience indices are integrated into the economic criteria to ensure the resilience of MG operation alongside the main MG actors' profit/loss. The MG fragility index, recovery efficiency index, MG voltage index, and lost load index are considered in the proposed planning model. Further, to make the results more realistic, the uncertainties associated with energy price and wind production, alongside with planning of energy storage systems and electric vehicles parking lots are considered. To achieve a better solution for the security-constraint operation of MG, AC network equations are included in the system modeling. Finally, a large-scale MG based on the IEEE-33 bus testbed is utilized to evaluate the effectiveness of the proposed stochastic scheduling program.

关键词： Resilience Indexes Uncertainty stochastic processes Mathematical model Microgrids Real-time systems HIR natural event microgrid resilience smart grid stochastic programming

来源：评论

学校读者我要写书评

暂无评论

On quantitative stability in infinite-dimensional optimization under uncertainty

引用

OPTIMIZATION LETTERS 2021年第8期15卷 2733-2756页

作者： Hoffhues, M. Roemisch, W. Surowiec, T. M. Philipps Univ Marburg Math & Informat FB12 Hans Meerwein Str 6 D-35032 Marburg Germany Humboldt Univ Inst Math Unter Linden 6 D-10099 Berlin Germany

The vast majority of stochastic optimization problems require the approximation of the underlying probability measure, e.g., by sampling or using observations. It is therefore crucial to understand the dependence of the optimal value and optimal solutions on these approximations as the sample size increases or more data becomes available. Due to the weak convergence properties of sequences of probability measures, there is no guarantee that these quantities will exhibit favorable asymptotic properties. We consider a class of infinite-dimensional stochastic optimization problems inspired by recent work on PDE-constrained optimization as well as functional data analysis. For this class of problems, we provide both qualitative and quantitative stability results on the optimal value and optimal solutions. In both cases, we make use of the method of probability metrics. The optimal values are shown to be Lipschitz continuous with respect to a minimal information metric and consequently, under further regularity assumptions, with respect to certain Fortet-Mourier and Wasserstein metrics. We prove that even in the most favorable setting, the solutions are at best Holder continuous with respect to changes in the underlying measure. The theoretical results are tested in the context of Monte Carlo approximation for a numerical example involving PDE-constrained optimization under uncertainty.

关键词： Stability stochastic programming Optimization under uncertainty Probability metrics PDE-constrained optimization Functional data analysis

来源：评论

学校读者我要写书评

暂无评论

An L-shaped method with strengthened lift-and-project cuts

引用

COMPUTATIONAL MANAGEMENT SCIENCE 2022年第4期19卷 539-565页

作者： Glushko, Pavlo Fabian, Csaba, I Koberstein, Achim European Univ Viadrina Grosse Scharrnstr 59 D-15230 Frankfurt Oder Germany John von Neumann Univ Dept Informat 10 Izsaki Ut H-6000 Kecskemet Hungary

Lift-and-project (L &P) cuts are well-known general 0-1 programming cuts which are typically deployed in branch-and-cut methods to solve MILP problems. In this article, we discuss ways to use these cuts within the framework of Benders' decomposition algorithms for solving two-stage mixed-binary stochastic problems with binary first-stage variables and continuous recourse. In particular, we show how L &P cuts derived for the master problem can be strengthened with the second-stage information. An adapted L-shaped algorithm and its computational efficiency analysis is presented. We show that the strengthened L &P cuts can significantly reduce the number of iterations and the solution time.

关键词： stochastic programming L-shaped method Lift-and-project cuts Benders' decomposition

来源：评论

学校读者我要写书评

暂无评论

Sketching the Best Approximate Quantum Compiling Problem 3

Sketching the Best Approximate Quantum Compiling Problem

引用

3rd IEEE International Conference on Quantum Computing and Engineering (QCE)

作者： Maddens, Liam Akhriev, Albert Simonetto, Andrea Univ Colorado Boulder CO 80309 USA IBM Res Europe IBM Quantum Dublin Ireland Inst Polytech Paris ENSTA Paris UMA F-91120 Palaiseau France

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

This paper considers the problem of quantum compilation from an optimization perspective by fixing a circuit structure of CNOTs and rotation gates then optimizing over the rotation angles. We solve the optimization problem classically and consider algorithmic tools to scale it to higher numbers of qubits. We investigate stochastic gradient descent and two sketch-and-solve algorithms. For all three algorithms, we compute the gradient efficiently using matrix-vector instead of matrix-matrix computations. Allowing for a runtime on the order of one hour, our implementation using either sketch-and-solve algorithm is able to compile 9 qubit, 27 CNOT circuits;12 qubit, 24 CNOT circuits;and 15 qubit, 15 CNOT circuits. Without our algorithmic tools, standard optimization does not scale beyond 9 qubit, 9 CNOT circuits, and, beyond that, is theoretically dominated by barren plateaus.

关键词： Optimization stochastic programming compilers

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：