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A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing
journal contribution
posted on 2010-01-01, 00:00 authored by S C Neoh, N Morad, Chee Peng LimChee Peng Lim, Z AzizHigh-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.
History
Journal
IEEE transactions on systems, man, and cybernetics part A : systems and humansVolume
40Issue
1Pagination
133 - 146Publisher
IEEELocation
Piscataway, N. JISSN
1083-4427eISSN
1558-2426Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2009, IEEEUsage metrics
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No categories selectedKeywords
genetic algorithms (GAs)high-mix-low-volume (HMLV) manufacturingmultidecision optimizationparticle swarm optimization (PSO)product-mix planningScience & TechnologyTechnologyComputer Science, CyberneticsComputer Science, Theory & MethodsComputer SciencePARTICLE SWARM OPTIMIZATIONGENETIC ALGORITHMCONSTRAINTSTOC
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