EVALUATING UNCERTAINTY IN RISK-BASED INTERDEPENDENCY MODELING WITH INTERVAL ARITHMETIC

被引:14
|
作者
Barker, Kash [1 ]
Rocco S, Claudio M. [2 ]
机构
[1] Univ Oklahoma, Sch Ind Engn, Norman, OK 73019 USA
[2] Cent Univ Venezuela, Fac Ingn, Caracas, Venezuela
基金
美国国家科学基金会;
关键词
Inoperability input-output model; Interval arithmetic; Sensitivity; Uncertainty; INPUT-OUTPUT MODEL; DECISION-MAKING; SMARTE METHODOLOGY; ELECTRIC UTILITIES; INOPERABILITY; VARIABILITY; DISRUPTIONS; IGNORANCE; SECTORS;
D O I
10.1080/09535314.2011.572064
中图分类号
F [经济];
学科分类号
02 ;
摘要
Several sources of uncertainty exist in the effort to quantify the efficacy of preparedness decision-making in interdependent systems. For the Inoperability Input-Output Model (IIM), a risk-based extension of the traditional Leontief model, which describes the propagation of inoperability throughout interconnected economic sectors, uncertainty is manifested in parameters describing the strength of interdependencies among sectors and in parameters describing the adverse impacts of a disruptive event, among others. As the model is used to evaluate preparedness options to reduce the impact of these disruptive events, such uncertainty can impact decision-making efforts. This paper introduces interval arithmetic as an approach for dealing with uncertainties in the IIM when probability distributions are not known and only variable bounds are available. Illustrative examples highlight the use of the approach as well as a means to improve the evaluation and comparison of risk management strategies in interdependent systems when only intervals are known.
引用
收藏
页码:213 / 232
页数:20
相关论文
共 50 条
  • [1] Modeling Uncertainty in the Wings Method Using Interval Arithmetic
    Michnik, Jerzy
    Grabowski, Artur
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2020, 19 (01) : 221 - 240
  • [2] Modeling uncertainty of expert elicitation for use in risk-based optimization
    Teter, Michael D.
    Royset, Johannes O.
    Newman, Alexandra M.
    ANNALS OF OPERATIONS RESEARCH, 2019, 280 (1-2) : 189 - 210
  • [3] Modeling uncertainty of expert elicitation for use in risk-based optimization
    Michael D. Teter
    Johannes O. Royset
    Alexandra M. Newman
    Annals of Operations Research, 2019, 280 : 189 - 210
  • [4] On Risk-Based Expression of Hydrographic Uncertainty
    Calder, Brian R.
    MARINE GEODESY, 2015, 38 (02) : 99 - 127
  • [5] Evaluating risk-based capital regulation
    Hogan, Thomas L.
    Meredith, Neil R.
    Pan, Xuhao
    REVIEW OF FINANCIAL ECONOMICS, 2018, 36 (02) : 83 - 96
  • [6] An uncertainty power flow algorithm based on interval and affine arithmetic
    Ding, Tao
    Cui, Hantao
    Gu, Wei
    Wan, Qiulan
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2012, 36 (13): : 51 - 55
  • [7] Uncertainty analysis of water quality modeling and risk-based decision-making based on DRAM
    Zhang, Qing-Qing
    Xu, Yue-Ping
    Zhang, Xu-Jie
    Xu, Xiao
    Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2012, 46 (12): : 2231 - 2236
  • [8] A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty
    Xu, Ye
    Huang, Guohe
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2016, 2016
  • [9] A Mixed Interval Arithmetic/Affine Arithmetic Approach for Robust Design Optimization With Interval Uncertainty
    Wang, Shaobo
    Qing, Xiangyun
    JOURNAL OF MECHANICAL DESIGN, 2016, 138 (04)
  • [10] Risk-based analysis justifies expanding maintenance interval
    Perdomo, Jorge J.
    Medina, Robinson J.
    2000, Gulf Publ Co, Houston, TX, USA (83):