Dynamic project scheduling is a term used to refer to the dynamic nature of project management. It consists of three dimensions, known as baseline scheduling, schedule risk analysis and project control (Uyttewaal, 2005; Vanhoucke, 2012b). In previous studies, it has been shown that both the schedule risk analysis and project control should go hand in hand as useful tools to measure the project performance of a project in progress and to improve the project control process and the corrective action decision making process in case the project is in danger.

In an article in the International Journal of Project Management (Vanhoucke, 2012a), the project control dimension was highlighted based on a comparison between academic results obtained on fictitious project data and additional tests performed on a set of real-life data from 8 Belgian companies from various sectors. However, little or nothing has been said about the first dimension of dynamic scheduling: the construction of a project baseline schedule. In the current article, a similar approach to the previously published article will be followed. Based on the extensive knowledge of the algorithmic developments in the academic literature and on the past experience of using some of these (adapted) algorithms in a practical environment, it will be illustrated how and why research can contribute positively to practice when constructing a project baseline schedule. In doing so, we believe that the gap between academic results and practical relevance is bridged, gradually moving the dynamic project scheduling discipline to a higher level.