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Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment

This paper examines the high-dimensional dependence modelling problem in the context of project risk assessment. As the dimension of uncertain performance units (i.e., itemized costs and activity times) in a project increases, specifying a feasible correlation matrix and eliciting relevant pair-wise information, either from historical data or with expert judgement, becomes practically unattainable or simply not economical. This paper presents a factor-driven dependence elicitation and modelling framework with scalability to large-scale project risks. The multi-factor association model (MFAM) accounts for hierarchical relationships of multiple association factors and provides a closed-form solution to a complete and mathematically consistent correlation matrix. Augmented with the structured association (SA) technique for systematic identification of hierarchical association factors, the MFAM offers additional flexibility of utilizing the minimum information available in standardized, ubiquitous project plans (e.g., work breakdown structure, resource allocation, or risk register), while preserving the computational efficiency and the scalability to high dimensional project risks. Numerical applications and simulation experiments show that the MFAM, further combined with extended analytics (i.e., parameter calibration and optimization), provides credible risk assessments (with accuracy comparable to full-scale simulation) and further enhances the realism of dealing with high-dimensional project risks utilizing all relevant information.

© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

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Work Title Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment
Access
Open Access
Creators
  1. Byung Cheol Kim
Keyword
  1. Project management
  2. dependence modelling
  3. large-scale risks
  4. simulation
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Publisher
  1. European Journal of Operational Research
Publication Date May 6, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.ejor.2021.04.043
Deposited October 31, 2022

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  • Created

    October 31, 2022 14:18 by Researcher Metadata Database

  • Added BCKim_2021_Multi-factor_dependence_modelling_EJOR.pdf

    October 31, 2022 14:18 by Researcher Metadata Database

  • Added Creator Byung Cheol Kim

    October 31, 2022 14:18 by Researcher Metadata Database

  • Published

    October 31, 2022 14:18 by Researcher Metadata Database

  • Updated Work Title, Keyword, Description Show Changes

    December 06, 2022 15:17 by pmk5516

    Work Title
    • Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment
    • ! Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment
    Keyword
    • Project management, dependence modelling, large-scale risks, simulation
    Description
    • <p>This paper examines the high-dimensional dependence modelling problem in the context of project risk assessment. As the dimension of uncertain performance units (i.e., itemized costs and activity times) in a project increases, specifying a feasible correlation matrix and eliciting relevant pair-wise information, either from historical data or with expert judgement, becomes practically unattainable or simply not economical. This paper presents a factor-driven dependence elicitation and modelling framework with scalability to large-scale project risks. The multi-factor association model (MFAM) accounts for hierarchical relationships of multiple association factors and provides a closed-form solution to a complete and mathematically consistent correlation matrix. Augmented with the structured association (SA) technique for systematic identification of hierarchical association factors, the MFAM offers additional flexibility of utilizing the minimum information available in standardized, ubiquitous project plans (e.g., work breakdown structure, resource allocation, or risk register), while preserving the computational efficiency and the scalability to high dimensional project risks. Numerical applications and simulation experiments show that the MFAM, further combined with extended analytics (i.e., parameter calibration and optimization), provides credible risk assessments (with accuracy comparable to full-scale simulation) and further enhances the realism of dealing with high-dimensional project risks utilizing all relevant information.</p>
    • This paper examines the high-dimensional dependence modelling problem in the context of project risk assessment. As the dimension of uncertain performance units (i.e., itemized costs and activity times) in a project increases, specifying a feasible correlation matrix and eliciting relevant pair-wise information, either from historical data or with expert judgement, becomes practically unattainable or simply not economical. This paper presents a factor-driven dependence elicitation and modelling framework with scalability to large-scale project risks. The multi-factor association model (MFAM) accounts for hierarchical relationships of multiple association factors and provides a closed-form solution to a complete and mathematically consistent correlation matrix. Augmented with the structured association (SA) technique for systematic identification of hierarchical association factors, the MFAM offers additional flexibility of utilizing the minimum information available in standardized, ubiquitous project plans (e.g., work breakdown structure, resource allocation, or risk register), while preserving the computational efficiency and the scalability to high dimensional project risks. Numerical applications and simulation experiments show that the MFAM, further combined with extended analytics (i.e., parameter calibration and optimization), provides credible risk assessments (with accuracy comparable to full-scale simulation) and further enhances the realism of dealing with high-dimensional project risks utilizing all relevant information.
  • Updated Work Title Show Changes

    May 24, 2023 10:39 by pmk5516

    Work Title
    • ! Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment
    • Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment
  • Updated

    April 04, 2024 10:21 by [unknown user]