Abstract
Fashion products require a significant amount of customization due to differences in body measurements, diverse preferences on style and replacement cyele. It is necessary for today's apparel industry to be responsive to the ever-changing fashion market. Just-in-time production is a must-go direction for apparel manufacturing. Apparel industry tends to generate more production orders, which are split into smaller jobs in order to provide customers with timely and customized fashion products. It makes the difficult task of production planning even more challenging if the due times of production orders are fuzzy and resource competing. In this paper, genetic algorithms and fuzzy set theory are used to generate just-in-time fabric-cutting schedules in a dynamic and fuzzy cutting environment. Two sets of real production data were collected to validate the proposed genetic optimization method. Experimental results demonstrate that the genetically optimized schedules improve the internal satisfaction of downstream production departments and reduce the production cost simultaneously.
| Original language | English |
|---|---|
| Pages (from-to) | 341-354 |
| Number of pages | 14 |
| Journal | Journal of Intelligent Manufacturing |
| Volume | 17 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jun 2006 |
Keywords
- Apparel
- Fabric cutting
- Fuzzy set theory
- Genetic algorithms
- Parallel machine scheduling
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence