A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing

Neoh, Siew-Chin, Morad, Norhashimah, Lim, Chee-Peng and Aziz, Zalina Abdul 2010, A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing, IEEE transactions on systems, man, and cybernetics part A : systems and humans, vol. 40, no. 1, pp. 133-146.

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Title A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing
Author(s) Neoh, Siew-Chin
Morad, Norhashimah
Lim, Chee-Peng
Aziz, Zalina Abdul
Journal name IEEE transactions on systems, man, and cybernetics part A : systems and humans
Volume number 40
Issue number 1
Start page 133
End page 146
Total pages 14
Publisher IEEE
Place of publication Piscataway, N. J
Publication date 2010-01
ISSN 1083-4427
1558-2426
Keyword(s) genetic algorithms (GAs)
high-mix-low-volume (HMLV) manufacturing
multidecision optimization
particle swarm optimization (PSO)
product-mix planning
Summary High-mix-low-volume (HMLV) production is currently a worldwide manufacturing trend. It requires a high degree of customization in the manufacturing process to produce a wide range of products in low quantity in order to meet customers' demand for more variety and choices of products. Such a kind of business environment has increased the conversion time and decreased the production efficiency due to frequent production changeover. In this paper, a layered-encoding cascade optimization (LECO) approach is proposed to develop an HMLV product-mix optimizer that exhibits the benefits of low conversion time, high productivity, and high equipment efficiency. Specifically, the genetic algorithm (GA) and particle swarm optimization (PSO) techniques are employed as optimizers for different decision layers in different LECO models. Each GA and PSO optimizer is studied and compared. A number of hypothetical and real data sets from a manufacturing plant are used to evaluate the performance of the proposed GA and PSO optimizers. The results indicate that, with a proper selection of the GA and PSO optimizers, the LECO approach is able to generate high-quality product-mix plans to meet the production demands in HMLV manufacturing environments.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048097

Document type: Journal Article
Collection: Institute for Frontier Materials
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