Designing work breakdown structures using modular neural networks

doi:10.1016/j.dss.2007.03.013

Designing work breakdown structures using modular neural networks

2007-11-01

25

PRE

AI

A

Alireza Hashemi Golpayegani *

B

Bahram Emamizadeh

DOI：10.1016/j.dss.2007.03.013

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Abstract

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In this paper, a framework which employs neural networks to plan the work breakdown structure of projects has been introduced. Using the proposed framework, a modular neural network has been developed to plan the structures of a limited project domain. The main concepts of the Andishevaran Methodology of Project Management (AMPM), including project control work breakdown structure (PCWBS), functional work breakdown structure (FWBS) and relational work breakdown structure (RWBS), have used to form the outputs of the model and its modules. The nature of projects, which have been represented by a limited set of attributes, are considered as the main inputs of the model. The independency from project domains is the main advantage of the proposed framework. The framework has been tested on a sample domain, and results showed that the planned work breakdown structures and activities have satisfied the expectations with different levels of validity. Therefore the model outputs could be considered as the primary plan of project structures which could be improved by some modifications. (C) 2007 Elsevier B.V. All rights reserved.

Keywords:

project management

project activity planning

neural network

work breakdown structure

complex relationship

knowledge modeling

Designing work breakdown structures using modular neural networks

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