Project Work Breakdown Structure and Activity Planning Using Neural Networks

In this paper, we show how the neural networks could be used to plan the appropriate work breakdown structures and activities of a given project. we have used the main concepts of ampm methodology, including project control work breakdown structure (pcwbs), functional work breakdown structure (fwbs) and relational work breakdown structure (rwbs), to construct the proposed model of neural network. the domain of projects which can be planned by the proposed model only depends to the data which are available from the executed previous projects as the main source of knowledge. in this regard, the main role of the proposed neural network is to relate between the project structures and activities and their attributes within the selected and limited project domain. the main advantage of the proposed method is its independency from the domain of projects which was one of the main problems in previous methods. the model and the related methodology were tested on a specific domain of projects and the results showed that it could support the primary planning of project structures and activities which could be finalized by further modifications.