Simulation-Based Scheduling and Risk Assessment of Complex Projects Under Uncertainty

Shao

Nazir

et al. 2019

IJMPB

Purpose In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty. Design/methodology/approach This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system. Findings The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty. Research limitations/implications This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis. Practical implications These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice. Originality/value This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Section: Applications Of Ai Methodsmentioning

confidence: 99%

A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies

Shao

Nazir

et al. 2019

IJMPB

“…Fuzzy hybrid methods are popular because of addressing uncertainty and hidden complexity in the system, that other methods alone may not be able to address this issue. Among those hybrid methods, the Fuzzy Monte Carlo Simulation approach is popular for project risk re-evaluation (Shariat et al , 2019) and cost analysis by giving the most realistic results after addressing multiple iterations of specific scenario (Terstegen et al , 2016). This study has utilized an integrated approach of the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) for risk prioritization and Monte Carlo Simulation (MCS) for the cost-risk re-evaluation process under a high level of project uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Correspondingly, while assuming multiple-iterations within a specified range, MCS gives a significantly more far-reaching perspective of situation analysis for decision making (Zhang et al , 2017). In this way, MCS has emerged a promising technique among the various cost-risk analysis techniques in construction projects (Terstegen et al , 2016) to measure stochastic behavior of risk while expecting complex interdependencies between project activities and resources for cost estimation (Ou-Yang and Chen, 2017) as far as developing cost-risk contingency framework.…”

Section: Development Of a Cost-risk Contingency Frameworkmentioning

confidence: 99%

Cost-risk contingency framework for managing cost overrun in metropolitan projects: using fuzzy-AHP and simulation

Shao

Junaid

et al. 2020

IJMPB

PurposeRisk analysis plays a vital role in controlling and managing cost overruns in complex construction projects, particularly where uncertainty is high. This study attempts to address an important issue of cost overrun that encountered by metropolitan rapid transit projects in relation to the significance of risk involved under high uncertainty.Design/methodology/approachIn order to solve cost overrun problems in metropolitan transit projects and facilitate the decision-makers for effective future budgeting, a cost-risk contingency framework has been designed using fuzzy logic, analytical hierarchy process and Monte Carlo simulation.FindingsInitially, a hierarchical breakdown structure of important complexity-driven risk factors has been conceptualized herein using relative importance index. Later, a proposed cost-risk contingency framework has investigated the expected total construction cost in order to consider the additional budgeted cost required to mitigate the risk consequences for particular project activity. The results of cost-risk analysis imply that poor design issues, an increase in material prices and delays in relocating facilities show higher dependency and increase the risk of cost overrun in metropolitan transit projects.Practical implicationsThe findings and implication for project managers could possibly be achieved by assuming the proposed cost-risk contingency framework under high uncertainty of cost found in this research. Furthermore, this procedure may be used by experts from other engineering domains by replacing and considering the complex relationship between complexity-risk factors.Originality/valueThis study contributes to the body of knowledge by providing a practical contingency model to identify and evaluate the additional risk cost required to compute total construction cost for getting stability in future budgeting.

Complexity-based risk decision framework for cost overrun using fuzzy Bayesian network

Junaid

et al. 2023

Soft Comput

This study adheres to nd important complexity-risk interdependent causes of cost overrun in infrastructure transport projects rather considering an independent state of project risk. Aiming for addressing cost overrun problem to facilitate decision-makers, a hierarchical breakdown structure of complex elements and complexity-driven risk factors at different levels of severity is conceptualized along with their interdependency network of key relationships. In this work, an integrated approach of fuzzy logic with the Bayesian belief network is employed for cost-risk assessment while assuming linguistic scales of likelihood and consequences parameters. The simulated results of cost-risk decision framework imply that poor design issues, increase in material prices and delay in relocating facilities show higher complexity-risk dependency and increase the risk of cost overrun in complex projects. This study contributes to the body of knowledge by providing a practical hybrid risk decision framework to identify and evaluate the key complexity-risk interdependencies in underline relations to the cost overrun problem in construction.