This paper aims to discuss the use of the Artificial Neural Networks (ANN) to model aspects of the project budget where traditional algorithms and formulas are not available or not easy to apply. Neural networks use a process analogous to the human brain, where a training component takes place with existing data and subsequently, a trained neural network becomes an “expert” in the category of information it has been given to analyse. This “expert” can then be used to provide projections given new situations based on an adaptive learning.
The use of the Line of Balance Scheduling Method has been increasing, especially on the construction industry companies of Brazil, Finland and Australia. The method addresses to the particularities of construction projects more effectively than the Critical Path Method does. In order to model the schedule, the paper demonstrates the “start-finish” relation and its contributions for the two approaches for the modelling: Network and Linear Scheduling Approach.
The objective of this paper is to present a non-conventional approach that is being currently implemented at the United Nations Office for Project Services, when selecting new projects globally, in order to include, as project selection criteria, social, environmental and economic sustainability aspects in humanitarian and development projects.
The objective of this paper is to present, discuss and apply a mathematical model based on the use of Monte Carlo simulation in conjunction with researches on project success/failure rates of projects to develop a 10 step model to calculate the mathematical return on investment (ROI) for the Project Office implementation.
The objective of this paper is to propose a mathematical process to turn the results of a qualitative risk analysis into numeric indicators to support better decisions regarding risk response strategies.
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