• Forecasting the project duration average and standard deviation from deterministic schedule information

      Ballesteros-Pérez, Pablo; Cerezo-Narváez, Alberto; Otero-Mateo, Manuel; Pastor-Fernández, Andrés; Zhang, Jingxiao; Vanhoucke, Mario (Applied Sciences, 2020)
      Most construction managers use deterministic scheduling techniques to plan construction projects and estimate their duration. However, deterministic techniques are known to underestimate the project duration. Alternative methods, such as Stochastic Network Analysis, have rarely been adopted in practical contexts as they are commonly computer-intensive, require extensive historical information, have limited contextual/local validity and/or require skills most practitioners have not been trained for. In this paper, we propose some mathematical expressions to approximate the average and the standard deviation of a project duration from basic deterministic schedule information. The expressions’ performance is successfully tested in a 4100-network dataset with varied activity durations and activity durations variability. Calculations are quite straightforward and can be implemented manually. Furthermore, unlike the Project Evaluation and Review Technique (PERT), they allow drawing inferences about the probability of project duration in the presence of several critical and subcritical paths with minimal additional calculation.
    • Performance comparison of activity sensitivity metrics in schedule risk analysis

      Ballesteros-Pérez, Pablo; Cerezo-Narváez, Alberto; Otero-Mateo, Manuel; Pastor-Fernández, Andrés; Vanhoucke, Mario (Automation in Construction, 2019)
      In Schedule Risk Analysis (SRA), activity sensitivity metrics measure the importance of activities in a project schedule. Highly sensitive activities are those more likely to increase project duration variability and/or cause project duration extensions. Several activity sensitivity metrics have been proposed over the years, but a comparison of all of them has never been made. This has made it difficult to know which metrics perform better and under what circumstances. In this paper, an extensive comparison of all relevant SRA activity sensitivity metrics is performed using a set of 4100 artificial projects. Unlike previous studies, the comparison framework is decoupled from corrective actions (e.g. activity crashing) which allows the merits of each metric to be assessed individually. Additionally, a new metric that performs better for overall sensitivity ranking is proposed. Results show that most sensitivity metrics do not perform well unless they are applied iteratively (the sensitivity of the remaining scheduled activities has to be recalculated whenever the duration variability of at least one activity has been restricted). However, if applied iteratively, most metrics can enhance project monitoring and control, while significantly shortening project duration.