Many industrial production realities, where it is of fundamental relevance to define management policies having as a main goal quality and lean production policies, requires that continuous improvement methodologies are implemented in order to increase flexibility and saturation of every production facilities, even with small sized batch production policies. For these tasks cluster analysis rules allow us to standardize several production cycle steps, giving as the possibility of sharing machines and facilities for a group of components instead for a single one. About these topics several articles are available in literature but, during our analysis, we have found that none of these are able to identify tools that allow to define the main characteristics of a production system, giving the possibility of a dynamic optimization based on the real features of each production facility. In this paper we propose a new methodology to define a cluster rule based on Theory of Constraint (TOC) principles; this is a theory based on the rule that, for each system, must be a constraint (bottleneck) which boundary its performances. Our approach is structured with a Knowledge Based System (KBS) that is used by an inferential engine to cluster products and machines based on one of the constraints of the production system. The machine-part matrix representation is totally revised with respect to the available shop floor data (e.g. time required for each machine to realize production mix, maximum time available for a specific machine, machine cutting capacity, etc.) formed with KBS data. The main features of the proposed approach is given by its easy capability to cluster different items without requiring traditional cluster tools based on binary machine-parts incidence matrixes. The cluster inferential engine is structured in order to self update the production system configuration with respect to its constraint data, approximating its operation to the reasoning the and expert scheduler manager can do in the same conditions.. The prototype system has been applied into a mechanical firm where we was able to re-arrange successfully shop floor layout and the related production cycles. As the most relevant experimentation results we have been able to minimize the lead time (about 14%) of the two main products of the analyzed firm and, at the same time, to increase machines production capability (about 15%).

Job Shop Optimization in a Constraints Environment Production with Clustering Techniques

Savino M;
2002

Abstract

Many industrial production realities, where it is of fundamental relevance to define management policies having as a main goal quality and lean production policies, requires that continuous improvement methodologies are implemented in order to increase flexibility and saturation of every production facilities, even with small sized batch production policies. For these tasks cluster analysis rules allow us to standardize several production cycle steps, giving as the possibility of sharing machines and facilities for a group of components instead for a single one. About these topics several articles are available in literature but, during our analysis, we have found that none of these are able to identify tools that allow to define the main characteristics of a production system, giving the possibility of a dynamic optimization based on the real features of each production facility. In this paper we propose a new methodology to define a cluster rule based on Theory of Constraint (TOC) principles; this is a theory based on the rule that, for each system, must be a constraint (bottleneck) which boundary its performances. Our approach is structured with a Knowledge Based System (KBS) that is used by an inferential engine to cluster products and machines based on one of the constraints of the production system. The machine-part matrix representation is totally revised with respect to the available shop floor data (e.g. time required for each machine to realize production mix, maximum time available for a specific machine, machine cutting capacity, etc.) formed with KBS data. The main features of the proposed approach is given by its easy capability to cluster different items without requiring traditional cluster tools based on binary machine-parts incidence matrixes. The cluster inferential engine is structured in order to self update the production system configuration with respect to its constraint data, approximating its operation to the reasoning the and expert scheduler manager can do in the same conditions.. The prototype system has been applied into a mechanical firm where we was able to re-arrange successfully shop floor layout and the related production cycles. As the most relevant experimentation results we have been able to minimize the lead time (about 14%) of the two main products of the analyzed firm and, at the same time, to increase machines production capability (about 15%).
9810483643
Group Technology; Automation; Cell system
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12070/11149
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