检索结果-内蒙古大学图书馆

Decision support model for cybersecurity risk planning: A two-stage stochastic programming framework featuring firms, government, and attacker

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期291卷 349-364页

作者： Paul, Jomon A. Zhang, Minjiao Kennesaw State Univ Dept Econ Finance & Quantitat Anal 560 Parliament Garden Way Kennesaw GA 30144 USA

We study the decision-making problem in cybersecurity risk planning concerning resource allocation strategies by government and firms. Aiming to minimize the social costs incurred due to cyberattacks, we consider not only the monetary investment costs but also the deprivation costs due to detection and containment delays. We also consider the effect of positive externalities of the overall cybersecurity investment on an individual firm's resource allocation attitude. The optimal decision guides the firms on the countermeasure portfolio mix (detection vs. prevention vs. containment) and government intelligence investments while accounting for actions of a strategic attacker and firm budgetary limitations. We accomplish this via a two-stage stochastic programming model. In the first stage, firms decide on prevention and detection investments aided by government intelligence investments that improve detection effectiveness. In the second stage, once the attacker's actions are realized, firms decide on containment investments after evaluating the cyberattacks. We demonstrate the applicability of our model via a case study. We find that externality can reduce the government's intelligence investment and that the firm's detection investment receives priority over containment. We also note that while prevention effectiveness has a decreasing impact on intelligence, it is beneficial to spend more on intelligence given its increasing returns to the reduction of social costs related to cybersecurity. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Decision analysis Cybersecurity stochastic programming Intelligence investment Social cost Safeguards Externality

来源：评论

学校读者我要写书评

暂无评论

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

引用

NAVAL RESEARCH LOGISTICS 2021年第1期68卷 89-111页

作者： Shehadeh, Karmel S. Cohn, Amy E. M. Jiang, Ruiwei Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed-integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two-stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable)appointment order(AO)policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near-optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor-swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.

关键词： appointment scheduling mixed-integer programming Monte Carlo optimization OR in health services stochastic arrival stochastic programming

来源：评论

学校读者我要写书评

暂无评论

CHANCE CONSTRAINED stochastic programming IN DESIGN OF A FRAME STRUCTURE

引用

CIVIL ENGINEERING JOURNAL-STAVEBNI OBZOR 2025年第1期34卷 1-11页

作者： Mrazkova, Eva Brno Univ Technol Inst Math Fac Mech Engn Technicka 2 Brno 61669 Czech Republic

A civil engineering problem concerning the optimal design of a loaded frame structure with a random Young's modulus is discussed. The developed multi-criteria optimization model involves ODE-type constraints and also one chance constraint related to the structure's reliability. A computational scheme for this type of problem is proposed using the finite difference method for the approximation of the ODE constraint and the scenario-based approach for random variable approximation. The chance constraint is handled by two approaches-the analytical approach and penalty reformulation. A posteriori check of satisfying the chance constraint is made, and the upper bounds of the obtainable reliability are computed.

关键词： Optimal engineering design stochastic programming Chance constrained optimization Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

Two-Stage stochastic programming for Precast Module Water Transportation: A Case Study in Hong Kong

引用

APPLIED SCIENCES-BASEL 2024年第24期14卷 11987页

作者： Wang, Huiwen Lim, Ying Terk Xie, Shenming Yi, Wen Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg & Real Estate Hung HomKowloon Hong Kong 999077 Peoples R China

Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation.

关键词： modular integrated construction precast modules water transportation planning stochastic programming demand uncertainty

来源：评论

学校读者我要写书评

暂无评论

Network-constrained rail transportation and power system scheduling with mobile battery energy storage under a multi-objective two-stage stochastic programming

引用

INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18827-18845页

作者： Mirzaei, Mohammad Amin Hemmati, Mohammad Zare, Kazem Mohammadi-Ivatloo, Behnam Abapour, Mehdi Marzband, Mousa Razzaghi, Reza Anvari-Moghaddam, Amjad Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Aalborg Univ Dept Energy Technol Aalborg Denmark Northumbria Univ Dept Math Phys & Elect Engn Newcastle Upon Tyne Tyne & Wear England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah Saudi Arabia Monash Univ Dept Elect & Comp Syst Engn Clayton Vic Australia

By increasing environmental pollution and the energy crisis, the development of renewable energy sources (RESs) has become an essential option to ensure a sustainable energy supply. However, the inherent uncertainty of RESs poses significant technical challenges for independent system operators (ISOs). Transmission line congestion has become one of the significant challenges for ISOs to use the maximum power of RESs. The mobile battery-based energy storage systems can provide a promising solution for the transportation of the generated energy from RESs to load centers to mitigate the effects of line congestion on the power network operation. Hence, this article evaluates the impact of battery-based energy storage transport by a train called BESTrain in a unit commitment model from the economic, environmental, and technical aspects under a multi-objective mixed-integer linear programming framework. The uncertainties associated with wind power and electric demand are also handled through a two-stage stochastic technique. The main aim of the introduced model is to minimize the carbon emission and operational cost simultaneously by determining the hourly location and optimal charge/discharge scheme of the BESTrain, and optimal scheduling of power plants. The numerical results exhibit the reduction of operation cost and carbon emission by 6.8% and 19.3%, respectively, in the presence of the BESTrain.

关键词： battery-based energy storage transport multi-objective optimization railway transport network stochastic programming vehicle routing problem wind energy

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Cooperative Spectrum Sensing Based on stochastic programming in Dynamic Cognitive Radio Sensor Networks

引用

IEEE ACCESS 2021年 9卷 720-732页

作者： Kaschel, Hector Toledo, Karel Gomez, Jorge Torres Garcia, M. Julia Fernandez-Getino Univ Santiago Chile Dept Elect Engn Santiago 9160000 Chile Univ Tecn Federico Santa Maria Dept Elect Valparaiso 2390123 Chile TU Berlin Sch Elect Engn & Comp Sci D-10587 Berlin Germany Carlos III Univ Madrid Dept Signal Theory & Commun Leganes Campus Leganes 28911 Spain

Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.

关键词： Sensors Cascading style sheets Energy consumption stochastic processes programming Heuristic algorithms Dynamic programming Dynamic CRSN energy efficiency spectrum sensing stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Tutorial on risk neutral, distributionally robust and risk averse multistage stochastic programming

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期288卷 1-13页

作者： Shapiro, Alexander Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

In this tutorial we discuss several aspects of modeling and solving multistage stochastic programming problems. In particular we discuss distributionally robust and risk averse approaches to multistage stochastic programming, and the involved concept of time consistency. This tutorial is aimed at presenting a certain point of view on multistage stochastic optimization, rather than a complete survey of the topic. (c) 2020 Elsevier B.V. All rights reserved.

关键词： stochastic programming Distributional robustness Dynamic equations

来源：评论

学校读者我要写书评

暂无评论

A stochastic programming approach for chemotherapy appointment scheduling

引用

NAVAL RESEARCH LOGISTICS 2021年第1期68卷 112-133页

作者： Demir, Nur Banu Gul, Serhat Celik, Melih Wayne State Univ Dept Ind & Syst Engn Detroit MI USA TED Univ Dept Ind Engn TR-06420 Ankara Turkey Univ Bath Sch Management Bath Avon England

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in premedication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real-life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.

关键词： appointment scheduling chemotherapy progressive hedging scheduling stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Decomposition methods for multi-horizon stochastic programming

引用

COMPUTATIONAL MANAGEMENT SCIENCE 2024年第1期21卷 32-32页

作者： Zhang, Hongyu Grossmann, Ignacio E. Tomasgard, Asgeir Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Hogskoleringen 1 N-7491 Trondheim Norway Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structure of multi-horizon stochastic linear programming, and establish that it can be decomposed by Benders decomposition and Lagrangean decomposition. In addition, we propose parallel Lagrangean decomposition with primal reduction that, (1) solves the scenario subproblems in parallel, (2) reduces the primal problem by keeping one copy for each scenario group at each stage, and (3) solves the reduced primal problem in parallel. We apply the parallel Lagrangean decomposition with primal reduction, Lagrangean decomposition and Benders decomposition to solve a stochastic energy system investment planning problem. The computational results show that: (a) the Lagrangean type decomposition algorithms have better convergence at the first iterations to Benders decomposition, and (b) parallel Lagrangean decomposition with primal reduction is very efficient for solving multi-horizon stochastic programming problems. Based on the computational results, the choice of algorithms for multi-horizon stochastic programming is discussed.

关键词： stochastic programming Multi-horizon stochastic programming Lagrangean decomposition Benders decomposition Parallel computing Primal reduction

来源：评论

学校读者我要写书评

暂无评论

A two-stage stochastic programming model for bike-sharing systems with rebalancing

引用

EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2024年 13卷

作者： Cavagnini, Rossana Maggioni, Francesca Bertazzi, Luca Hewitt, Mike Rhein Westfal TH Aachen Deutsche Post Chair Optimizat Distribut Networks Aachen Germany Univ Bergamo Dept Management Informat & Prod Engn Bergamo Italy Univ Brescia Dept Econ & Management Brescia Italy Loyola Univ Chicago Quinlan Sch Business Dept Informat Syst & Operat Management Chicago IL USA

We study the problem of determining the target inventory level of stations in a bike-sharing system, when bikes can be rebalanced later during the day. We propose a two-stage stochastic programming formulation, where the target inventory decisions are made at the first stage, while the recourse decisions, related to rebalancing, are made at the second stage. In the literature, the problem of determining the target inventory levels is solved without taking into account the rebalancing problem, or these two problems are solved sequentially. We prove that more efficient bike-sharing systems can be obtained by integrating these two problems. Moreover, we show that our methodology provides better results than the deterministic formulation, and consider an effective matheuristic, based on the solution of the deterministic problem, to solve the stochastic program. Finally, we compare the solutions obtained by our approach with the actual allocation of bikes in the real bike-sharing system of the city of San Francisco. The results show the effectiveness of our approach also in a realistic setting.

关键词： Bike sharing Target inventory levels Rebalancing stochastic programming Analysis of the stochastic solution

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：