Work rate modeling of building construction projects using system dynamic to optimize project cost and time performance
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Cited by 3 publications
References 53 publications
System Dynamics (SD) applications in high volume production operations is ubiquitous, helping to define decision rules to reduce costs associated with the variance in planning orders and inventory. The exploitation of SD in engineer-to-order (ETO) project-oriented supply chains, e.g., in construction, shipbuilding, and capital goods, is less well established. Hence, this research reviews papers which take a systematic ETO perspective modelling construction project, exploiting SD approaches. To comprehensively identify and filter previously published papers, we use a keyword searching method using Web of Science and Scopus databases. After applying relevant exclusion criteria, 145 papers are finally selected. While there have been previous reviews of ETO literature more generally, this paper contributes to the body of knowledge by specifically reviewing SD applications in ETO industries and providing insights by creating a categorization system by which to determine where existing gaps reside. Papers are categorized into the classic four phases of a project: aggregated planning, pre-project planning, project execution, and post-delivery phase. Analyses of the methods, attributes and applications of SD are undertaken for each phase. Findings indicate that SD research covers the range of ETO industries of which construction is the most dominant, demonstrating SD's high applicability. The wealth of case-orientated research in the construction field provides a solid foundation for further SD studies in the ETO field. Further research should focus on 1) developing a general ETO archetype used for performance benchmarking and strategy development in construction projects, 2) introducing analytical tools, such as control theoretic approaches as found in manufacturing production planning and control design, to improve understanding of the ETO systems' dynamic behaviors, and 3) developing cross-phase, cross-project, design production integrated, aggregated planning models via hybrid techniques modelling, which can contribute to a better understanding of an ETO system's performance.
System Dynamics (SD) applications in high volume production operations is ubiquitous, helping to define decision rules to reduce costs associated with the variance in planning orders and inventory. The exploitation of SD in engineer-to-order (ETO) project-oriented supply chains, e.g., in construction, shipbuilding, and capital goods, is less well established. Hence, this research reviews papers which take a systematic ETO perspective modelling construction project, exploiting SD approaches. To comprehensively identify and filter previously published papers, we use a keyword searching method using Web of Science and Scopus databases. After applying relevant exclusion criteria, 145 papers are finally selected. While there have been previous reviews of ETO literature more generally, this paper contributes to the body of knowledge by specifically reviewing SD applications in ETO industries and providing insights by creating a categorization system by which to determine where existing gaps reside. Papers are categorized into the classic four phases of a project: aggregated planning, pre-project planning, project execution, and post-delivery phase. Analyses of the methods, attributes and applications of SD are undertaken for each phase. Findings indicate that SD research covers the range of ETO industries of which construction is the most dominant, demonstrating SD's high applicability. The wealth of case-orientated research in the construction field provides a solid foundation for further SD studies in the ETO field. Further research should focus on 1) developing a general ETO archetype used for performance benchmarking and strategy development in construction projects, 2) introducing analytical tools, such as control theoretic approaches as found in manufacturing production planning and control design, to improve understanding of the ETO systems' dynamic behaviors, and 3) developing cross-phase, cross-project, design production integrated, aggregated planning models via hybrid techniques modelling, which can contribute to a better understanding of an ETO system's performance.
Preliminary cost forecasting and optimization for the construction of steel pedestrian bridges in Egypt
PurposeThis research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.Design/methodology/approachThis study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.FindingsIn this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.Practical implicationsThe implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.Social implicationsThe model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.Originality/valueThe authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.
Enhancing Project Performance Forecasting using Machine Learning Techniques
Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.
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