A flexible ANN-GA-multivariate algorithm for assessment and optimization of machinery productivity in complex production units. (April 2015)

• Record Type: Journal Article
• Authors: Azadeh, A.; Mianaei, H. Shams; Asadzadeh, S.M.; Saberi, M.; Sheikhalishahi, M.
• Is Part Of: Journal of manufacturing systems. Volume 35(2015)
• Page Start: 46
• Page End: 75
• Publication Date: 2015-04
• Subjects: Machinery productivity -- Optimization -- Artificial neural network (ANN) -- Genetic algorithm (GA) -- Data envelopment analysis (DEA) -- Principal component analysis (PCA) -- Numerical taxonomy (NT) -- Complex production units (CPUs); Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2014.11.007
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A flexible algorithm handling complexity by ANN, GA and multivariate analysis. Performance optimization of production units by machinery productivity indicators. DEA, PCA and numerical taxonomy are used for verification and validation. Indicators are categorized into availability, stoppage, failure and value added. It may be easily extended to other units for optimization of machinery productivity. Abstract: This paper presents a flexible algorithm based on artificial neural networks (ANNs), genetic algorithms (GAs), and multivariate analysis for performance assessment and optimization of complex production units (CPUs) with respect to machinery productivity indicators (MPIs). Multivariate techniques include data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT). Two case studies are considered to show the applicability of the proposed approach. In the first case, the machinery productivity indicators are categorized into four standard classes as availability, machinery stoppage, random failure and value added and production value. In the second case, the productivity of production units in terms of health, safety, environment and ergonomics indicators is evaluated. The flexible algorithm is capable of handling both linearity and complexity of data sets. Moreover, ANN and GA are efficiently applied to cover nonlinearity and complexity of CPUs. The results are also validated and verified by the internal mechanism of the algorithm. The algorithm is applied to a large set of production units to show its superiority and applicability over conventional approaches. Results show that, in the case of having non-linear data sets, ANN outperforms GA and conventional approaches. The flexible algorithm of this study may be easily extended to other units for assessment and optimization of CPUs with respect to machinery indicators.