## 1 Introduction  
Research in the field of Project scheduling mainly consisting of mathematical operations dates back to the late 1950s, and was used to approximately predict the times on when to start and when to finish activities of the project in terms of priority and constraints in resources, while simultaneously pursuing to achieve a particular optimized project objective. Subsequently, a stockpile of research was conducted, accounting for scheduling and planning of projects in different areas (see, for example [1; 2; 3; 4]). While extensive research has been carried out during the last two decades resulting in models of project scheduling with different features, the bulk of research has mainly focused on fundamental principles of project scheduling. This limitation can possibly be explained by the project schedule failure, due to limited capability, to overcome the uncertainty and different risks which characterize the actual and practical execution of the project. However, as a significant role in a project life cycle and in its failure or success, project scheduling (in combination with other new techniques) can improve the results [5]. In other words, a construction project schedule has to be considered as a predictive model that can be used with other techniques such as Earned Value (EV) indices to improve resource efficiency calculations, risk analysis, project planning and control and performance measurement. For more than fifty years, managers have been using Earned Value Management Systems (EVMS) to cope with the complicated task of controlling and adjusting project schedule baseline as the project is being executed, according to total project budget, timed delivery, and account project scope. There is a general consensus over using this well-known management system to use indices like Cost Performance Index (CPI) and Schedule Performance Index (SPI) to account for cost, schedule and technical performance integratively. Besides, this system allows for the calculation of costs, performance indices, schedule variances, forecast project costs and schedule duration. [PERSON] proposed previous and new formalism as well as a new notation to be used in earned value analysis [6]. Research in this area has grown slightly, while forecasting techniques and their applications in areas such as price forecasting have grown rapidly. It seems that the lack or inaccessibility of project data is a major barrier to utilizing new forecasting techniques in project management. Applying new forecasting methods requires periodic, progressive time series data, as well as sufficient historical data from similar projects. Hence, in many construction projects, this data is not sufficient to apply forecasting methods and risk analysis. Refer to [5; 7; 8] for more details. This paper contributes to serve two objectives. First, it mainly intends to review the literature on project management control research using earned value management and efforts to investigate project time and cost estimates. Although a large number of papers have proposed estimation methods, little attempt has been made to apply these methods to generate project time series data as daily progressive estimations in different project environments. Therefore, a new simulation approach as a novel framework is offered here to reflect the data regarding time-series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. The output can explain the future progress and financial situation of the project at different time scale. This simulator is a powerful tool that can provide daily progressive estimated information which reassures project managers of execution stages of the project although they lack historical data that could help researchers in generating general and customized formulae in further studies.