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Sequential process machine cell formation with hybrid particle swarm optimization
https://orcid.org/http://orcid.org/0000-0002-7225-0071
This paper deals with sequential machine cell formation problem (CFP) in cellular manufacturing system (CMS). CMS is the implementation of group technology (GT) in manufacturing systems. The objective of applying of GT is to create part groups called part families as well as machine groups called as machine cells in order to reduce total intercellular pass of parts as well as to capitalize the number of operations within a machine cell. Most of the earlier studies on machine cell formation problems focus on maximizing grouping efficacy by reducing outside elements and void elements in diagonal blocks for non-sequential CFP. Here, we present a meta-heuristic particle swarm optimization (i.e., hybrid particle swarm optimization-HPSO) to minimize the intercellular movements of parts for sequential machine CFP. Computational works were carried out on 7 standard problems from the literature. The outcome confirms that the proposed meta-heuristic method (HPSO) in terms of intercellular movements of parts has shown to produce solutions that are either improved or aggressive with other accessible algorithms.
Sequential process machine cell formation with hybrid particle swarm optimization
https://orcid.org/http://orcid.org/0000-0002-7225-0071
This paper deals with sequential machine cell formation problem (CFP) in cellular manufacturing system (CMS). CMS is the implementation of group technology (GT) in manufacturing systems. The objective of applying of GT is to create part groups called part families as well as machine groups called as machine cells in order to reduce total intercellular pass of parts as well as to capitalize the number of operations within a machine cell. Most of the earlier studies on machine cell formation problems focus on maximizing grouping efficacy by reducing outside elements and void elements in diagonal blocks for non-sequential CFP. Here, we present a meta-heuristic particle swarm optimization (i.e., hybrid particle swarm optimization-HPSO) to minimize the intercellular movements of parts for sequential machine CFP. Computational works were carried out on 7 standard problems from the literature. The outcome confirms that the proposed meta-heuristic method (HPSO) in terms of intercellular movements of parts has shown to produce solutions that are either improved or aggressive with other accessible algorithms.
Sequential process machine cell formation with hybrid particle swarm optimization
https://orcid.org/http://orcid.org/0000-0002-7225-0071
This paper deals with sequential machine cell formation problem (CFP) in cellular manufacturing system (CMS). CMS is the implementation of group technology (GT) in manufacturing systems. The objective of applying of GT is to create part groups called part families as well as machine groups called as machine cells in order to reduce total intercellular pass of parts as well as to capitalize the number of operations within a machine cell. Most of the earlier studies on machine cell formation problems focus on maximizing grouping efficacy by reducing outside elements and void elements in diagonal blocks for non-sequential CFP. Here, we present a meta-heuristic particle swarm optimization (i.e., hybrid particle swarm optimization-HPSO) to minimize the intercellular movements of parts for sequential machine CFP. Computational works were carried out on 7 standard problems from the literature. The outcome confirms that the proposed meta-heuristic method (HPSO) in terms of intercellular movements of parts has shown to produce solutions that are either improved or aggressive with other accessible algorithms.
Sequential process machine cell formation with hybrid particle swarm optimization
Int J Interact Des Manuf
Hazarika, Manash (author)
2025-02-01
13 pages
Article (Journal)
Electronic Resource
English
Group technology , Cellular manufacturing systems , Sequential cell formation problems , Intercellular movement of parts , Particle swarm optimization Information and Computing Sciences , Artificial Intelligence and Image Processing , Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
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