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An Investigation to Manufacturing Analytical Services Composition Using the Analytical Target Cascading Method

Abstract : As cloud computing is increasingly adopted, the trend is to offer software functions as modular services and compose them into larger, more meaningful ones. The trend is attractive to analytical problems in the manufacturing system design and performance improvement domain because (1) finding a global optimization for the system is a complex problem; and (2) sub-problems are typically compartmentalized by the organizational structure. However, solving sub-problems by independent services can result in a sub-optimal solution at the system level. This paper investigates the technique called Analytical Target Cascading (ATC) to coordinate the optimization of loosely-coupled sub-problems, each may be modularly formulated by differing departments and be solved by modular analytical services. The result demonstrates that ATC is a promising method in that it offers system-level optimal solutions that can scale up by exploiting distributed and modular executions while allowing easier management of the problem formulation.
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Kai-Wen Tien, Boonserm Kulvatunyou, Kiwook Jung, Vittaldas Prabhu. An Investigation to Manufacturing Analytical Services Composition Using the Analytical Target Cascading Method. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2016, Iguassu Falls, Brazil. pp.469-477, ⟨10.1007/978-3-319-51133-7_56⟩. ⟨hal-01615791⟩

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