PREDICTION OF AVERAGE TOTAL PROJECT DURATION USING ARTIFICIAL NEURAL NETWORKS, FUZZY LOGIC, AND REGRESSION MODELS

The prediction of project‘s expectancy life is an important issue for entrepreneurs since it helps them to avoid the expiration time of projects. To properly address this issue, Neural Network-based approach, fuzzy logic and regression methods are used to predict the necessary time that can be consumed to put an end to the targeted project. Before applying the three aforementioned approaches, the modeling and simulation of the activities network are introduced for calculating the total average time of project. Then, comparatively speaking, the neural network, fuzzy logic and regression method approach are compared in terms prediction's accuracy. The generated error from the three methods is compared, namely different types of errors are calculated. Basically, the input variables consist of the probability of success (PS), the coefficient of improvement (Coef_PS) and the coefficient of learning (CofA), while the output variable is the average total duration of the project (DTTm). The Predicted mean square error (MSE) values are purposefully used to compare the three models. Interestingly, the results show that the optimum prediction model is the fuzzy logic model with accurate results. It is noteworthy to say that the application in this paper can be applied on a real case study.