Large scale / mega projects are lengthy complex endeavors that require significant planning by management, engineers and construction personnel to ensure the success of the project. When we examine the state of mega projects today, we are faced with a real crisis. Companies, both client and contractor, are reporting significant cost and schedule overruns. Lack of project scope definition and planning are the primary characteristics of this problem. Computer simulation is a powerful tool for analyzing complex and dynamic scenarios. It provides an appealing approach for the analysis of repetitive processes. Simulation helps decision makers identify different possible options by analyzing enormous amounts of data. Hence, computer simulation can be used effectively to analyze the resource loading and manpower requirements needed to complete a task in a given time frame, based on current progress levels. This paper discusses a specialpurpose simulation (SPS) tool for optimization of manpower forecast loading and resource leveling. The simulation model is capable of optimizing resource requirements for a petrochemical project, based on standard discipline requirements and involvements. Tests of this simulation tool have produced exceptional results; currently, the system is being modified to incorporate historical data within the simulation.
Construction business is project oriented and that is why every construction organization is dependent on projects. Typically they undertake multiple projects with limited multiple resources and information. Most importantly they need to take continuous and quick decisions to keep it going. The reason behind this is lack of tools and structured approach that can efficiently deal with multi-project environment (PME). Resulting is problem of wrong project selection, project slippage and under/over utilization of scares resources. This paper presents a simulation model (OPMPE) for optimizing MPE. The model is capable of analyzing and predicting future problems, assessing the cumulative impact and generates valuable statistics and information for quick decision-making. It will work together with the available scheduling tools and will help strengthening the overall planning and execution system for MPE. The application and of the model is demonstrated using a collection of real project data for building construction.
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