Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics

被引:8
作者
Dunke, Fabian [1 ]
Nickel, Stefan [1 ]
机构
[1] Karlsruhe Inst Technol, Discrete Optimizat & Logist, Inst Operat Res, Kaiserstr 12, D-76131 Karlsruhe, Germany
关键词
Multi-criteria decision making; Sensitivity analysis; Simulation-based decision making; Infectious disease epidemic simulation; DISCRETE-EVENT SIMULATION; GLOBAL SENSITIVITY-ANALYSIS; MONTE-CARLO TECHNIQUES; PARAMETER SENSITIVITY; INFORMATION-THEORY; SYSTEM DYNAMICS; MODEL; CRITERIA; SUPPORT; OPTIMIZATION;
D O I
10.1007/s10479-021-04321-8
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Whenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol' sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives.
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页数:30
相关论文
共 94 条
  • [81] Modeling and analysis of global epidemiology of avian influenza
    Rao, Dhananjai M.
    Chernyakhovsky, Alexander
    Rao, Victoria
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2009, 24 (01) : 124 - 134
  • [82] Saltelli A., 2004, Sensitivity Analysis in Practice, DOI [10.1002/0470870958, DOI 10.1002/0470870958]
  • [83] Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
    Saltelli, Andrea
    Annoni, Paola
    Azzini, Ivano
    Campolongo, Francesca
    Ratto, Marco
    Tarantola, Stefano
    [J]. COMPUTER PHYSICS COMMUNICATIONS, 2010, 181 (02) : 259 - 270
  • [84] Discrete-event simulation is dead, long live agent-based simulation!
    Siebers, P. O.
    Macal, C. M.
    Garnett, J.
    Buxton, D.
    Pidd, M.
    [J]. JOURNAL OF SIMULATION, 2010, 4 (03) : 204 - 210
  • [85] Intelligent multicriteria decision support: Overview and perspectives
    Siskos, Y
    Spyridakos, A
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1999, 113 (02) : 236 - 246
  • [86] Sobol' I.M., 1993, MATH MODEL COMPUT EX, V1, P407, DOI DOI 10.18287/0134-2452-2015-39-4-459-461
  • [87] Variance-based sensitivity analysis of a forest growth model
    Song, Xiaodong
    Bryan, Brett A.
    Paul, Keryn I.
    Zhao, Gang
    [J]. ECOLOGICAL MODELLING, 2012, 247 : 135 - 143
  • [88] Sumari S.Ibrahim., 2013, INT J MANAGEMENT EXC, V1, P4, DOI [DOI 10.17722/IJME.V1I3.17, DOI 10.17722/ijme.v1i3]
  • [89] Tako A A., 2018, System dynamics: Soft and hard operational research, P261
  • [90] Simulation optimization: A comprehensive review on theory and applications
    Tekin, E
    Sabuncuoglu, I
    [J]. IIE TRANSACTIONS, 2004, 36 (11) : 1067 - 1081