Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/38078
Title: Intelligent apparel production planning for optimizing manual operations using fuzzy set theory and evolutionary algorithms
Authors: Mok, PY 
Keywords: Apparel production planning and learning curve effects
Evolutionary computing and genetic algorithm
Fuzzy set
Issue Date: 2011
Source: 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), 11-15 April 2011, Paris, p. 103-110 (CD) How to cite?
Abstract: Effective and accurate production planning is essential for garment manufacturers to survive in today's competitive apparel industry. Varying customer demands, shorter lifecycles and changing fashion trends are amongst the factors that make accurate production planning important. Manufacturers strive to fulfil requirements such as on-time completion, short production lead time and effective allocation of job order to specific production lines. However, effective production planning is difficult to achieve because the apparel manufacturing environment is fuzzy and dynamic. This paper suggests the use of intelligent production planning algorithms, based on fuzzy set theory, genetic algorithms (GA) and multi-objective genetic algorithms (MOGA), to achieve optimal solutions for apparel production planning.
URI: http://hdl.handle.net/10397/38078
ISBN: 978-1-61284-049-9
ISSN: 1944-9925
DOI: 10.1109/GEFS.2011.5949496
Appears in Collections:Conference Paper

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