Project Schedule Risk Assessment Based on Bayesian Nets

2016 ◽  
Vol 39 (1) ◽  
pp. 9-16
Author(s):  
Hongsuk Sung ◽  
◽  
Chulsoon Park
Keyword(s):  
Risk Assessment ◽  
Project Schedule ◽  
Schedule Risk

scholarly journals Construction Schedule Risk Assessment and Management Strategy for Foreign General Contractors Working in the Ethiopian Construction Industry

2021 ◽  
Vol 13 (14) ◽  
pp. 7830
Author(s):  
Min-Yuan Cheng ◽  
Mohammadzen Hasan Darsa
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Construction Industry ◽  
Management Strategy ◽  
Assessment Model ◽  
Project Schedule ◽  
Schedule Delay ◽  
General Contractors ◽  
Schedule Risk ◽  
Construction Schedule

Construction project schedule delay is a worldwide concern and especially severe in the Ethiopian construction industry. This study developed a Construction Schedule Risk Assessment Model (CSRAM) and a management strategy for foreign general contractors (FGCs). 94 construction projects with schedule delay were collected and a questionnaire survey of 75 domain experts was conducted to systematically select 22 risk factors. In CSRAM, the artificial neural network (ANN) inference model was developed to predict the project schedule delay. Integrating it with the Garson algorithm (GA), the relative weights of risk factors with rankings were calculated and identified. For comparison, the Relative Importance Index (RII) method was also applied to rank the risk factors. Management strategies were developed to improve the three highest-ranked factors identified using the GA (change order, corruption/bribery, and delay in payment), and the RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as inputs for the trained ANN to conduct a sensitivity analysis. The findings of this study indicate that improvements in the factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as an important reference for FGCs who plan to enter or work in the Ethiopian construction industry.

Analysis of Project Shedule Risk Indexes in PERT Network Using Monte Carlo Simulation

2013 ◽  
Vol 760-762 ◽  
pp. 2205-2211 ◽  
Author(s):  
Yuan Liu ◽  
Zhuo Fu Wang
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Risk Index ◽  
Critical Index ◽  
Project Schedule ◽  
Schedule Risk ◽  
Schedule Risk Analysis ◽  
Construction Schedule

It is convenient and effective to use Monte Carlo simulation (MCS) technique in project schedule risk analysis and assessment, but at the same time, the indexes put forward by scholars up to now is quite few, leaving construction schedule risk assessment still difficult to carry out. Therefore, based on PERT network assumption, the shortcomings of current project schedule risk indexes are summarized and new project schedule risk index is put forward to estimate the criticality of each activity and path to provide more information for project schedule controllers. In the case studied, with the application of the new index, the critical index of each activity is given and divided into five levels, and the new index put forward in this paper shows great superiority over the classic indexes.

scholarly journals Integrating Schedule Risk Analysis with Multi-Skilled Resource Scheduling to Improve Resource-Constrained Project Scheduling Problems

2021 ◽  
Vol 11 (2) ◽  
pp. 650
Author(s):  
Muritala Adebayo Isah ◽  
Byung-Soo Kim
Keyword(s):  
Risk Analysis ◽  
Project Scheduling ◽  
Resource Scheduling ◽  
Scheduling Problems ◽  
Project Schedule ◽  
Risk And Uncertainty ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Schedule Risk ◽  
Schedule Risk Analysis

Construction projects are planned in a complex and dynamic environment characterized by high risks and uncertainties amidst resource constraints. Assessing construction schedule risk facilitates informed decision-making, especially in a resource-constrained situation, and allows proactive actions to be taken so that project objectives are not jeopardized. This study presents a stochastic multiskilled resource scheduling (SMSRS) model for resource-constrained project scheduling problems (RCSPSP) considering the impact of risk and uncertainty on activity durations. The SMSRS model was developed by integrating a schedule risk analysis (SRA) model (developed in MS Excel) with an existing multiskilled resource scheduling (MSRS) algorithm for the development of a feasible and realistic schedule. The computational experiment carried out on three case projects using the proposed SMSRS model revealed an average percentage deviation of 10.50%, indicating the inherent risk and uncertainty in activity durations of the project schedule. The core contribution of the proposed SMSRS model is that it: (1) presents project practitioners with a simple tool for assessing the risks and uncertainty associated with resource-constrained project schedules so that necessary response actions can be taken to ensure project success; (2) provides the small-scale construction businesses with an affordable tool for evaluating schedule risk and developing a feasible and realistic project schedule.

Quantitative Risk Assessment for Projects Schedules

2012 ◽  
Author(s):  
Sherif S. Hassanien ◽  
Jason B. Skow
Keyword(s):  
Risk Assessment ◽  
Time Allocation ◽  
Market Research ◽  
Epistemic Uncertainty ◽  
Project Planning ◽  
Quantitative Risk Assessment ◽  
Success Rates ◽  
Project Schedule ◽  
Planning Stage ◽  
Expected Benefits

Despite vigilant efforts in project scheduling and planning by engineers and project managers, recent market research reported a marked decrease in project success rates. The market research tracked projects across a broad range of industries and concluded the primary failure causes to be a lack of sufficient detail in the project planning stage, poor or no risk analysis, scope creep and poor communication. This paper focuses on a strategy to minimize the first cause. Specifically, how to obtain sufficient schedule and resource estimates to better predict time allocation and expected costs with a focus on capital pipeline projects. To this end, a quantitative risk assessment (QRA) methodology is applied to project schedules allowing the uncertainty of key task durations and/or costs to be fully accounted for in the schedule. Projects managers will be able to quantify the uncertainty in their projects and support decision makers with a more accurate prediction of the likelihood of being on time and on budget. This paper introduces a systematic approach for both aleatory and epistemic uncertainty quantification. In addition, the expected benefits of adopting QRA for projects schedules are discussed through a hypothetical simple project schedule from the pipeline industry.

scholarly journals Analyzing Integrated Cost-Schedule Risk for Complex Product Systems R&D Projects

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhe Xu ◽  
Jing Yu ◽  
Hongbo Li
Keyword(s):  
Risk Assessment ◽  
Risk Estimation ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Complex Product ◽  
Product Systems ◽  
Complex Product Systems ◽  
Schedule Risk ◽  
Integrated Cost ◽  
The Relationship

The vast majority of the research efforts in project risk management tend to assess cost risk and schedule risk independently. However, project cost and time are related in reality and the relationship between them should be analyzed directly. We propose an integrated cost and schedule risk assessment model for complex product systems R&D projects. Graphical evaluation review technique (GERT), Monte Carlo simulation, and probability distribution theory are utilized to establish the model. In addition, statistical analysis and regression analysis techniques are employed to analyze simulation outputs. Finally, a complex product systems R&D project as an example is modeled by the proposed approach and the simulation outputs are analyzed to illustrate the effectiveness of the risk assessment model. It seems that integrating cost and schedule risk assessment can provide more reliable risk estimation results.

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