Author:
Wa-Muzemba Anselm Tshibangu
Affiliation:
Morgan State University, United States
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 quic
kly 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.
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