Integrating case-based analysis and fuzzy optimization for selecting project risk response actions

Zhang, Yao; Zuo, Fei; Guan, Xin

doi:10.1016/j.physa.2019.123578

Cited by 15 publications

(8 citation statements)

References 32 publications

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“…It means that for underlying data, the approach with regard to the prediction horizon [10] is confined to about 7 steps. In contrast, standard polynomial predictors [8] show similar properties for only 1-2 steps.…”

Section: Problem Statementmentioning

confidence: 90%

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A new approach to identifying the local structure of multidimensional chaotic time series

Makshanov

Zhuravlev

Tyndykar

2021

J. Phys.: Conf. Ser.

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The paper is devoted to the solution of the problems of mathematical supply of decision making during multichannel monitoring of large-scaled systems. The work also deals with space-time dynamics of multidimensional time series of different origins. Highly dynamical chaotic processes whose fine structure cannot be revealed by standard spectral methods are regarded. Technologies for dimension reduction based on data matrix representation on the first singular basis and multiple regression in projections’ space are developed.

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Section: Problem Statementmentioning

confidence: 90%

“…It makes it possible to single out the segments that are at some sense close to Y and get predictions "by analogy" (precedent analysis [3,4,10,11]). The short-time predictors may be based on multiple regressions techniques [8,10,11]. Let Y be the sliding window of width L -the m L × (m=5) matrix.…”

Section: Problem Statementmentioning

confidence: 99%

A new approach to identifying the local structure of multidimensional chaotic time series

Makshanov

Zhuravlev

Tyndykar

2021

J. Phys.: Conf. Ser.

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“…e aim is to dispatch available resources, depending on risk criticality and action characteristics (impact and cost) [46]. Different methods exist: zonal-based method, studying actions for different zones of two-axis diagrams [47], WBSbased method [48], linking actions to WBS elements [48], tradeoff method, seeking for one objective while keeping another one under control [49], and optimization-based method [49,50]. However, the indirect impact or secondary effects of actions are not considered, resulting in undesired impacts like medication.…”

Section: Risk Response Planningmentioning

confidence: 99%

“…CST thus provides two improvements: 1/a classical decision-making strategy, but with an upgraded networkbased version of risk analysis [49,50], and 2/a more original strategy, considering actions interdependencies [50]. is is aimed at increasing global effectiveness and efficiency of risk response plan, notably by considering compatibility or Complexity incompatibility between the actions that are assembled in this plan.…”

Section: Risk Response Plan Decision-makingmentioning

confidence: 99%

An Assistance to Project Risk Management Based on Complex Systems Theory and Agile Project Management

Marle

2020

Complexity

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Project Risk Management is crucial in determining the future performance of a complex project. Increasing project complexity makes it more and more difficult to anticipate potential events that could affect the project and to make effective decisions to reduce project risk exposure. To tackle these conceptual and managerial issues, the proposed approach introduces Complex Systems Theory-based improvements into some PRM subprocesses and runs the global PRM process using Agile Project Management principles. We argue that these advanced techniques for managing project risk complexity, notably risk interdependencies, are coherent with the distributed, self-organized nature of agile teams. This new way of structuring and executing Project Risk Management offers the possibility to make decisions more frequently, when needed, with a more distributed authority, and with richer information about anticipation of events and consequences of actions. First results show an appropriation of this combined approach by project members due to agile principles that allows for getting the more reliable information promised by Complex Systems Theory.

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“…Until now, many scholars have proposed different methods (Zhang and Guan, 2018;Zhang and Fan, 2014) on project risk response, such as the trade-off method (Chapman and Ward, 2003;Datta and Mukherjee, 2001;Flanagan and Norman, 1993;Haimes, 2015;Klein, 1993;Elkjaer and Felding, 1999;Piney, 2002;Miller and Lessard, 2001), the optimization method (Ben-David et al, 2002;Fang et al, 2013;Fan et al, 2008;Ben-David and Raz, 2001;Kayis et al, 2007;Sherali et al, 2011;Wu et al, 2018;Zhang, 2016;Zhang and Fan, 2014;Zhang and Zuo, 2016;Zhang et al, 2020;Zuo and Zhang, 2018) and the decision tree method (Dey, 2002(Dey, , 2012Kujawski and Angelis, 2010;Marmier et al, 2014;Marmier et al, 2013). These studies have made significant contributions on risk response; however, they mainly focused on the selection of proper risk response strategies under a budget constraint on the premise that the cost of implementing each response strategy is known.…”

Section: Introductionmentioning

confidence: 99%

Budget allocation decisions for project risk response

Zhang

Guan

2021

Self Cite

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Purpose The purpose of this paper is to propose a method integrating fault tree analysis and optimization model to allocate response budget from the preventive and protective perspectives. Design/methodology/approach The proposed method consists of two main steps. The first step is to analyze and calculate the probability and the loss of the risk. The second step is to build an optimization model for allocating response budget. Findings First, there exists an optimal response budget. Second, risk protection is preferred to risk prevention when the total budget is limited. Third, the protective budget should be first invested for the consequence event with greatest expected loss. Fourth, the preventive budget should be first allocated to the risk cause with highest occurrence probability that belongs to the OR set in the fault tree. Practical implications Managerially, our results indicate that project managers (PMs) should make a tradeoff between the budget invested for risk response and reduced expected loss of the risk. Then, in the case of inadequate response budget, PMs should pay more attention to risk protection and cope with the event that can cause severe loss. In addition, under this circumstance, PMs had to better allocate the risk preventive budget in proper order. Originality/value Project risk response is a critical issue in project risk management as PMs can take actions actively to cope with project risks in this phase. Effective risk response, in general, requires financial support in practice, and reasonable allocation of the total budget among risk response strategies can produce better response effects.

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Integrating case-based analysis and fuzzy optimization for selecting project risk response actions

Cited by 15 publications

References 32 publications

A new approach to identifying the local structure of multidimensional chaotic time series

A new approach to identifying the local structure of multidimensional chaotic time series

An Assistance to Project Risk Management Based on Complex Systems Theory and Agile Project Management

Budget allocation decisions for project risk response

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