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Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework

Topics: Engineering Applications; Engineering Applications; Engineering Applications; Industrial Automation and Robotics; Manufacturing Systems Engineering; Optimization Algorithms; Performance Evaluation and Optimization; Production Planning, Scheduling and Control; Quality Control and Management; Supply Chain and Logistics Engineering; Systems Modeling and Simulation; Systems Modeling and Simulation

Author: Wa-Muzemba Anselm Tshibangu

Affiliation: Morgan State University, United States

ISBN: 978-989-758-263-9

Keyword(s): Lean Six Sigma, Robust Design, Optimization, Doe, Fms, Variance Minimization, Simulation.

Related Ontology Subjects/Areas/Topics: Engineering Applications ; Industrial Automation and Robotics ; Industrial Engineering ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Manufacturing Systems Engineering ; Optimization Algorithms ; Performance Evaluation and Optimization ; Production Planning, Scheduling and Control ; Quality Control and Management ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; Supply Chain and Logistics Engineering ; Systems Modeling and Simulation

Abstract: From the performance view point, manufacturing strategy relates to the decision about where to focus concentration among quality, speed, dependability, flexibility and cost. This study analyzes a hypothetical flexible manufacturing system (FMS) and aims to illustrate an optimization procedure based on a variance reduction applied on two strategic performance measures, namely the Throughput Rate (TR) and the Mean Flow Time (MFT). The study uses a Taguchi robust design of experiments (DOE) methodology to model and simulate the hypothetical FMS, analyzes the output of the simulations, then proposes a unique and hybrid (empirical-analytical) methodology to quickly uncover the optimal setting of operating parameters. The robust design is used to guarantee the system stability necessary to improve the system and validate the outcomes. Using the key principle of the Six Sigma methodology that advocates a reduction of variability to improve quality and processes the proposed methodology quick ly reaches a near optimum by considering both the main and interaction effects of the control factors that will minimize the variability of the performances. Fine-tuned follow-up runs may be necessary to compromise and uncover the true optimum. (More)

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Paper citation in several formats:
Tshibangu, W. and Tshibangu, W. (2017). Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework.In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 295-303. DOI: 10.5220/0006436702950303

@conference{icinco17,
author={Wa{-}Muzemba Anselm Tshibangu. and Wa{-}Muzemba Anselm Tshibangu.},
title={Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={295-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006436702950303},
isbn={978-989-758-263-9},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework
SN - 978-989-758-263-9
AU - Tshibangu, W.
AU - Tshibangu, W.
PY - 2017
SP - 295
EP - 303
DO - 10.5220/0006436702950303

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