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Taxonomy of Manufacturing Flexibility at Manufacturing Companies Using Imperialist Competitive Algorithms, Support Vector Machines and Hierarchical Cluster Analysis
- Source :
- Engineering, Technology & Applied Science Research, Vol 7, Iss 2 (2017), Publons, Engineering, Technology & Applied Science Research, Vol 7, Iss 2, Pp 1559-1566 (2017)
- Publication Year :
- 2017
- Publisher :
- D. G. Pylarinos, 2017.
-
Abstract
- Manufacturing flexibility is a multidimensional concept and manufacturing companies act differently in using these dimensions. The purpose of this study is to investigate taxonomy and identify dominant groups of manufacturing flexibility. Dimensions of manufacturing flexibility are extracted by content analysis of literature and expert judgements. Manufacturing flexibility was measured by using a questionnaire developed to survey managers of manufacturing companies. The sample size was set at 379. To identify dominant groups of flexibility based on dimensions of flexibility determined, Hierarchical Cluster Analysis (HCA), Imperialist Competitive Algorithms (ICAs) and Support Vector Machines (SVMs) were used by cluster validity indices. The best algorithm for clustering was SVMs with three clusters, designated as leading delivery-based flexibility, frugal flexibility and sufficient plan-based flexibility.
- Subjects :
- Engineering
0211 other engineering and technologies
02 engineering and technology
support vector machines
hierarchical cluster analysis
Set (abstract data type)
taxonomy
Taxonomy (general)
0502 economics and business
lcsh:Technology (General)
Cluster analysis
imperialist competitive algorithm
Flexibility (engineering)
021103 operations research
lcsh:T58.5-58.64
business.industry
lcsh:Information technology
05 social sciences
Imperialist competitive algorithm
Hierarchical clustering
dominant groups
Support vector machine
lcsh:TA1-2040
lcsh:T1-995
manufacturing flexibility
Companies Act
business
lcsh:Engineering (General). Civil engineering (General)
Algorithm
050203 business & management
Subjects
Details
- Language :
- English
- ISSN :
- 17928036 and 22414487
- Volume :
- 7
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Engineering, Technology & Applied Science Research
- Accession number :
- edsair.doi.dedup.....80bcba769e42c254785993fc5bd450b4