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Erschienen in:
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction Kapitel lesen Erstes Kapitel lesen

2011 | OriginalPaper | Buchkapitel

15. Simulation-Based Innovization Using Data Mining for Production Systems Analysis

verfasst von : Amos H. C. Ng, Catarina Dudas, Johannes Nießen, Kalyanmoy Deb

Erschienen in: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Verlag: Springer London

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Abstract

This chapter introduces a novel methodology for the analysis and optimization of production systems. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. Although the innovization method is based on multi-objective optimization and post-optimality analyses of optimised solutions, it stretches the scope beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. By integrating the concept of innovization with discrete-event simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis, particularly suitable for production systems. The uniqueness of the integrated approach proposed in this chapter lies on applying data mining to the data sets generated from simulation-based multi-objective optimization, in order to automatically or semi-automatically discover and interpret the hidden relationships and patterns for optimal production systems design/reconfiguration. After describing the simulation-based innovization using data mining procedure and its difference from conventional simulation analysis methods, results from an industrial case study carried out for the improvement of an assembly line in an automotive manufacturer will be presented.

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Vorheriges Kapitel A Simulation Optimisation Framework for Container Terminal Layout Design
Nächstes Kapitel Multi-objective Production Systems Optimisation with Investment and Running Cost
Fußnoten
1
Cycle time, which is also called variously as manufacturing lead time, throughput time or sojourn time, is used in this paper to refer to the time from a job is released at the beginning of the line/system until it reaches its end (i.e., the time a part spends as WIP). This terminology follows the definition found in standard textbooks for manufacturing systems analysis, e.g., [36].
 
2
Defined in the shifting bottleneck detection method [41], active time is the time when the machine is working, changing tools or in repair and inactive time includes starving, blocked or waiting.
 
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Metadaten
Titel
Simulation-Based Innovization Using Data Mining for Production Systems Analysis
verfasst von
Amos H. C. Ng
Catarina Dudas
Johannes Nießen
Kalyanmoy Deb
Copyright-Jahr
2011
Verlag
Springer London
Buch
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Print ISBN: 978-0-85729-617-7

Electronic ISBN: 978-0-85729-652-8

Copyright-Jahr: 2011

DOI
https://doi.org/10.1007/978-0-85729-652-8_15

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