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

Published

September 4, 2016

Author(s)

Kai-wen Tien, Boonserm Kulvatunyou, Kiwook Jung, Vittaldas Prabhu

Abstract

As cloud computing is increasingly adopted, the trend is to offer software func-tions 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 compart-mentalized 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 co-ordinate the optimization of loosely-coupled sub-problems, each may be modular-ly formulated by differing departments and be solved by modular analytical ser-vices. 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 formula-tion.

Conference Dates

September 3-7, 2016

Conference Location

Iguassu Falls, BR

Conference Title

APMS 2016 International Conference, Advances in Production Management Systems - Production
Management Initiatives for Sustainable World

Pub Type

Conferences

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Keywords

Factory design and improvement, integration optimization, analytical target cascading, services composition

Citation

Tien, K. , Kulvatunyou, B. , Jung, K. and Prabhu, V. (2016), An Investigation to Manufacturing Analytical Services Composition using the Analytical Target Cascading Method, APMS 2016 International Conference, Advances in Production Management Systems - Production Management Initiatives for Sustainable World, Iguassu Falls, BR, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920909 (Accessed April 19, 2024)