English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A project schedule contains a network of activities, the activity durations,the early and late finish dates for each activity, and the associated totalfloat or slack times, the difference between the late and early dates. Here Ishow that the distribution of activity durations and total floats ofconstruction project schedules exhibit a power law scaling. The power lawscaling of the activity durations is explained by a historical process ofspecialization fragmenting old activities into new activities with shorterduration. In contrast, the power law scaling of the total floats distributionacross activities is determined by the activity network. I demonstrate that thepower law scaling of the activity duration distribution is essential to obtaina good estimate of the project delay distribution, while the actual total floatdistribution is less relevant. Finally, using extreme value theory and scalingarguments, I provide a mathematical proof for reference class forecasting forthe project delay distribution. The project delay cumulative distributionfunction is $G(z) = \exp( - (z_c/z)^{1/s})$, where $s>0$ and $z_c>0$ are shapeand scale parameters. Furthermore, if activity delays follow a lognormaldistribution, as the empirical data suggests, then $s=1$ and $z_c \simN^{0.20}d_{\max}^{1+0.20(1-\gamma_d)}$, where $N$ is the number of activities,$d_{\max}$ the maximum activity duration in units of days and $\gamma_d$ thepower law exponent of the activity duration distribution. These results offernew insights about project schedules, reference class forecasting and delayrisk analysis.

Emerge of scaling in project schedules