Intelligent systems in project performance measurement and evaluation

Abstract

Over the life cycle of a project, project costs and time estimations play important roles in baseline scheduling, schedule risk analysis and project control. Performance measurement is the ongoing, regular collection of information that can provide this controlling system. In this study, firstly, a new simulation approach is proposed to develop project progress time-series data, based on the complexity and specifications of the project as well as on the environment in which the project is executed. This simulator is capable of simulating fictitious projects, as well as real projects based on empirical data and helps project managers to monitor the project’s execution, despite the lack of historical data. Besides, this chapter compares the effects of different inputs on generated time series, as estimated results obtained on a fictitious dataset. Secondly, the validated outputs can provide researchers with an opportunity to generate general and customized formulae such as project completion time estimation. This study also implies four soft computing methods, Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Interface System (ANFIS), Emotional Learning based Fuzzy Interface System (ELFIS) and Conventional Regression to forecast the completion time of project. Core variables in proposed model are known parameters in Earned Value Management (EVM). Finally, the result of using intelligent models and their performances in modeling the expert emotions are compared.