توصيفگر ها :
برنامه زمانبندي پروژه , مديريت پروژه , شبكهعصبي مصنوعي , يادگيري ماشين , هوش مصنوعي
چكيده انگليسي :
Today, the project schedule is recognized as one of the most important success factors of a project. These days, with the advancement of technology and the use of project management software, the project schedule is determined more accurately and completely based on the project's activities and its required resources. At the same time, despite the progress made in the field of project management, there are still problems in project scheduling that can affect the success of a project. To improve the project schedule, you can use project management methods such as changing the project structure, improving work processes, using advanced project management software, and also improving the skills of people in the field of project management. The more accurate the forecast of project time and cost, project managers can create more appropriate plans for resource allocation, agenda setting, change management, and risk management for their project. This will increase the probability of project success, reduce costs and delay times, and ultimately improve the quality of the project. In addition, accurate forecasting of project time and cost helps project teams to propose the best possible solutions for activities according to the time and resources required, and as a result, enjoy better project management. In general, accurate forecasting the time and cost of the project leads to quality improvement, better project management, reduction of costs and delay times, and finally, an increase in the success and profit of the project. Artificial intelligence models based on historical data and some features that traditional scheduling methods may not pay attention to them, have shown hopeful progress in improving accuracy of construction scheduling. In this research, artificial neural networks and neural fuzzy models were developed using historical data from construction projects scheduling. In this research, a hybrid models of ANFIS and MLP, so a LR and SVR model have been used and the performance of these models has been compared. The more accurate the forecast of project time and cost, project managers can create more appropriate plans for resource allocation, changes management, and risk management for their projects. Results of these models will increase the probability of project success, reduce costs and delay times, and improve the quality of the project. In addition, accurate forecasting of project time and cost helps the project teams to propose the best possible solutions according to the required time and resources and, as a result, have better project management. In general, accurate forecasting of project time and cost leads to quality improvement, better project management, reduction of costs and delay times, and finally, an increase in project success and profit. The results of the comparison of these different models show that the hybrid model is the best, because it has values of 0.108 in MAE, 0.025 in MSE, 0.159 in RMSE and 0.725 in R² compared to the SVR model with values of 0.181, 0.037 , 0.192 and 0.701 and the LR model with values of 0.142, 0.037, 0.192 and 0.660.
