Reference number : GC-2017-Q1-02426, amendment number : , agreement type : G, recipient legal name : Mohamed, Yasser (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Industrial construction projects represent a significant sector of the Canadian economy. These projects involve large investments from the energy sector and support thousands of jobs and are subject to competition from overseas construction companies. Modular construction is a common practice for industrial construction especially on Canadian oil-sand projects. In modular construction, most of the project is assembled from pre-fabricated components that are custom built off-site according to unique designs. These components include thousands of pipe spools, equipment and vessels, and structural steel elements. They are shipped to site either separately or pre-assembled in modular units for final installation. Industrial construction projects are often fast tracked and involve millions of man-hours provided by several parties along the engineering, procurement, fabrication, and site installation phases. The performance of operations in these phases is affected by the unique designs of the different components, design changes that happens during construction, deviations in material delivery by different suppliers, and availability of resources. This results in a fairly complex supply chain that can easily cause project cost and schedule overruns if not properly managed and optimized. Optimizing these operations is of great importance to retain the best value for investors in this sector and to maintain an advantage for Canadian contractors to be able to compete and export their know-how in an increasingly competitive global market. 
The objective of this proposed research is to investigate and develop models to represent operations in key industrial construction project delivery phases and suggest alternative solutions to optimize the performance during these phases. More specifically it will focus on 1) the analysis and representation of components data structures. These components are uniquely designed for each project but share some degree of repetition between projects. We aim at representing the data structures of these components in a way that allows us to analyze the similarities between projects and thus have better estimates of resource requirements and schedule expectations and develop methods for optimizing the packaging of these components into modules to minimize field work. The research will also target 2) the development of new process modeling approaches suitable for capturing the dynamics of site installation operations where out-of-sync component delivery, variation in trade man-hours availability, and congestion constrains are usually present and cannot be easily handled by traditional planning tools. Finally, the research will 3) suggest and test alternative methods for planning and predicting delivery dates during off-site fabrication and assembly operations using select data mining algorithms., program purpose french : Industrial construction projects represent a significant sector of the Canadian economy. These projects involve large investments from the energy sector and support thousands of jobs and are subject to competition from overseas construction companies. Modular construction is a common practice for industrial construction especially on Canadian oil-sand projects. In modular construction, most of the project is assembled from pre-fabricated components that are custom built off-site according to unique designs. These components include thousands of pipe spools, equipment and vessels, and structural steel elements. They are shipped to site either separately or pre-assembled in modular units for final installation. Industrial construction projects are often fast tracked and involve millions of man-hours provided by several parties along the engineering, procurement, fabrication, and site installation phases. The performance of operations in these phases is affected by the unique designs of the different components, design changes that happens during construction, deviations in material delivery by different suppliers, and availability of resources. This results in a fairly complex supply chain that can easily cause project cost and schedule overruns if not properly managed and optimized. Optimizing these operations is of great importance to retain the best value for investors in this sector and to maintain an advantage for Canadian contractors to be able to compete and export their know-how in an increasingly competitive global market. 
The objective of this proposed research is to investigate and develop models to represent operations in key industrial construction project delivery phases and suggest alternative solutions to optimize the performance during these phases. More specifically it will focus on 1) the analysis and representation of components data structures. These components are uniquely designed for each project but share some degree of repetition between projects. We aim at representing the data structures of these components in a way that allows us to analyze the similarities between projects and thus have better estimates of resource requirements and schedule expectations and develop methods for optimizing the packaging of these components into modules to minimize field work. The research will also target 2) the development of new process modeling approaches suitable for capturing the dynamics of site installation operations where out-of-sync component delivery, variation in trade man-hours availability, and congestion constrains are usually present and cannot be easily handled by traditional planning tools. Finally, the research will 3) suggest and test alternative methods for planning and predicting delivery dates during off-site fabrication and assembly operations using select data mining algorithms., agreement title english : Modeling and Automation of Industrial Construction Operations, agreement title french : Modeling and Automation of Industrial Construction Operations, agreement number : RGPIN, agreement value : 170000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada