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.
| Original language | English |
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| Title of host publication | IEEE SSCI 2011 |
| Subtitle of host publication | Symposium Series on Computational Intelligence - GEFS 2011: 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems |
| Pages | 103-110 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 12 Aug 2011 |
| Event | Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS 2011 - Paris, France Duration: 11 Apr 2011 → 15 Apr 2011 |
Conference
| Conference | Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS 2011 |
|---|---|
| Country/Territory | France |
| City | Paris |
| Period | 11/04/11 → 15/04/11 |
Keywords
- Apparel Production Planning and Learning Curve Effects
- Evolutionary computing and genetic algorithm
- Fuzzy set
ASJC Scopus subject areas
- Discrete Mathematics and Combinatorics
- Logic