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- W649634872 abstract "Effective planning and scheduling has become increasingly important for the state departments of transportation to efficiently use resources and avoid project delays. Accurate estimates are needed for task durations and resource requirements. The predictive models for durations and resource requirements need to accurately reflect the agency's current business practices and requirements. Updating predictive models is thus important to the improvement of planning and scheduling. Updating predictive models for the Kansas Department of Transportation's (KDOT's) management system has two goals: prediction and description. Prediction involves using some variables in the data base to predict unknown values of interest, in this case, activity duration. Description involves finding regularities underlying the data, which are interpretable to the domain engineers. These two goals can be accomplished when the appropriate numerical relationships are induced from the data base. This report presents a method, combining machine learning technique and regression analysis, to automatically and intelligently update predictive models used in KDOT's internal project management system. Different predictive models are used in different projects. The predictive models used by KDOT consist of Planning factors (predictive equations) and base quantities (Scaling factors), which are applied to predict durations and resources of Functional Units (defined as subactivities). The method developed may be used for either task durations or resource requirements, but this report focuses on duration examples due to KDOT's priority in first updating these models. The method proposed is a three stage learning process. The first stage is concerned with data analysis to obtain new knowledge of each Functional Unit. The second stage is concerned with searching for Functional Units that behave similarly. The third stage is concerned with generating new planning factors and the base quantities used to scale a single planning factor for use to predict several Functional Unit activity durations." @default.
- W649634872 created "2016-06-24" @default.
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- W649634872 date "1997-04-01" @default.
- W649634872 modified "2023-09-27" @default.
- W649634872 title "DEVELOPMENT OF PROJECT ACTIVITY DURATION AND RESOURCE REQUIREMENT ALGORITHMS BASED ON HISTORICAL DATA" @default.
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