Reference number : GC-2017-Q1-02274, amendment number : , agreement type : G, recipient legal name : Li, Simon (University of Calgary), 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 : The scope of this research program is the optimization of manufacturing systems to support product variety and sustainability. To stay competitive in the market, original manufacturers (OM) need to satisfy customers with more product options and meet the environmental regulations and societal expectations. In this context, while formal optimization models have been widely used in system design, they often face the challenges of model and computational complexity (e.g., high number of integer variables and convergence issues). Instead of employing optimization methods, this research will investigate and develop clustering and statistical techniques for the design of manufacturing systems. The novelty is to develop a bottom-up approach to address complex structural decisions systematically with the target of better performance on solution convergence and computing time, as well as comparable optimality performance with metaheuristic algorithms. 
The proposed research program is organized into two themes. The first theme is the development of clustering algorithms for cellular manufacturing systems (CMS). One research goal is to improve the capability of hierarchical clustering (HC) to search for optimal structural decisions in the design of CMS. The methodological approach includes statistical analysis of coupling distributions and reversible HC to avoid the lock-in of premature groups. Also, we will focus on the issues of robustness and modularity. Regarding robustness, the clustering approach is applied to concatenate relevant changes and deploy resources purposely to reduce unnecessary propagation effects. Regarding modularity, the research idea is to match the modularity of product architecture and manufacturing systems using clustering concepts to enhance system flexibility for market changes. 
The second theme is the design of a closed-loop manufacturing system for sustainability. The general goal is to support OM to participate in the product&rsquo;s end-of-life management. Three aspects are considered: assembly, disassembly, and reassembly. While &ldquo;assembly&rdquo; is referred to the forward production, &ldquo;disassembly&rdquo; and &ldquo;reassembly&rdquo; are referred to the backward loop that considers the end-of-life options and product recovery. In this application, the clustering approach is used to configure this network system to improve sustainability measures. 
From research to practice, we will make the developed methodology and knowledge accessible to the Canadian manufacturing sector in a form of practical know-how (e.g., how to minimize change propagation and design robust systems). For component suppliers, they can learn how to enhance their manufacturing flexibility for more contract opportunities. For OM, they can promote sustainability by designing a close-loop network that manages the flow of materials after their first usage., program purpose french : The scope of this research program is the optimization of manufacturing systems to support product variety and sustainability. To stay competitive in the market, original manufacturers (OM) need to satisfy customers with more product options and meet the environmental regulations and societal expectations. In this context, while formal optimization models have been widely used in system design, they often face the challenges of model and computational complexity (e.g., high number of integer variables and convergence issues). Instead of employing optimization methods, this research will investigate and develop clustering and statistical techniques for the design of manufacturing systems. The novelty is to develop a bottom-up approach to address complex structural decisions systematically with the target of better performance on solution convergence and computing time, as well as comparable optimality performance with metaheuristic algorithms. 
The proposed research program is organized into two themes. The first theme is the development of clustering algorithms for cellular manufacturing systems (CMS). One research goal is to improve the capability of hierarchical clustering (HC) to search for optimal structural decisions in the design of CMS. The methodological approach includes statistical analysis of coupling distributions and reversible HC to avoid the lock-in of premature groups. Also, we will focus on the issues of robustness and modularity. Regarding robustness, the clustering approach is applied to concatenate relevant changes and deploy resources purposely to reduce unnecessary propagation effects. Regarding modularity, the research idea is to match the modularity of product architecture and manufacturing systems using clustering concepts to enhance system flexibility for market changes. 
The second theme is the design of a closed-loop manufacturing system for sustainability. The general goal is to support OM to participate in the product&rsquo;s end-of-life management. Three aspects are considered: assembly, disassembly, and reassembly. While &ldquo;assembly&rdquo; is referred to the forward production, &ldquo;disassembly&rdquo; and &ldquo;reassembly&rdquo; are referred to the backward loop that considers the end-of-life options and product recovery. In this application, the clustering approach is used to configure this network system to improve sustainability measures. 
From research to practice, we will make the developed methodology and knowledge accessible to the Canadian manufacturing sector in a form of practical know-how (e.g., how to minimize change propagation and design robust systems). For component suppliers, they can learn how to enhance their manufacturing flexibility for more contract opportunities. For OM, they can promote sustainability by designing a close-loop network that manages the flow of materials after their first usage., agreement title english : Design of manufacturing systems for product variety using cluster and statistical analysis, agreement title french : Design of manufacturing systems for product variety using cluster and statistical analysis, agreement number : RGPIN, agreement value : 105000.0, 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