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چکیده
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This paper introduces a novel methodology for project control, drawing inspiration from established control theory principles widely utilized in engineering disciplines (aeronautics, civil, mechanical, and electrical). This paper proposes a data-driven, quantitative framework grounded in utility function and risk tolerance concepts that is specifically tailored for project management applications. This framework introduces a second-order linear differential equation model that serves as a governing equation for project control. The model facilitates the evaluation of project inputs and their impact on project outcomes, offering a more objective alternative to traditional risk assessment methods that often rely heavily on subjective expert opinions. The core concept of this approach involves treating a project as a controllable system, akin to a robot. Project dynamics encompassing cost, schedule, and risk factors are translated into system variables. By leveraging established control theory tools such as state-space models, transfer functions can be defined in the Laplace domain. The proposed methodology enables proactive identification of potential project disruptions. This allows for timely adjustments to management decisions, aligning with the principles of project control/project management while offering a more scientific foundation for decision-making. This framework builds upon previous work by Seddiki (2022) and aims to overcome the limitations of subjective risk assessment by introducing a quantitative and data-driven approach. The author speculates that this methodology has the potential to significantly improve current project control and risk management practices in the field.
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