In previous discussions, we explored the benefits of employing Volume Lag and Point-to-Point dependencies for emulating activities that shift in parallel. However, due to the limited support for these features in many planning tools, some planners have suggested the utilisation of artificial activity splits as an alternative. It enables the application of Finish-to-Start dependencies without introducing lags between parallel activities. While this workaround resolves one issue, it potentially creates others.

Artificial Activity Split may cause two planning problems:

1. Splitting of activities that must be performed without interruption into separate segments can end in undesired results after resource levelling.
2. If parallel activities have duration uncertainties, Artificial Activity Split makes the result of the Monte Carlo Simulation unreliable.

Resource levelling Challenge

Let’s review a scheduling fragment with six activities. All dependencies are Finish-to-Start.

Activities A and B have a volume of 80 units (10 units per day). Activity B can run in parallel with activity A after 40 units are achieved. There are different ways to simulate such a scenario. Ideally, if a planning tool supports ‘volume scheduling’ and ‘point-to-point dependencies’, multiple point-to-point dependencies could be applied:

Start-Start (40v, 0v) and Start-Start (80v, 40v).

If a planning tool only supports duration lags, it could be simulated with two dependencies:

• Start-Start + Lag (4d) and Finish-to-Finish + Lag 4 days. Technically it takes 15 days to deliver the work package.
• This fragment requires the same ‘Resource 1’ for Activities A and D for the same days (Day 3-5). So, if there is only one ‘Resource 1’, resource levelling is required.

With limited resources, it takes 20 days to deliver the work package.

Now let’s review the same example but with Artificial Activity Split applied.

Activities A and B are split into two 4-days activities. It allows the application of Finish-to-Start dependencies without lags. Technologically activities (A1 and A2), (B1 and B2) must be performed without interruption. Due to the resource constraint (same as above), resource levelling is required.

A good levelling algorithm can find an opportunity to perform activity D ahead of activity A2 and deliver the fragment in 18 days instead of 20 days! This schedule meets all the specified planning conditions, but practically it is not feasible since activities A and B must be carried out without interruptions.

The second challenge associated with Monte Carlo Simulation we will review in a separate post.