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dc.contributor.authorZhou, Ming
dc.creatorZhou, Mingen_US
dc.date.accessioned2011-10-31T18:33:56Z
dc.date.available2011-10-31T18:33:56Z
dc.date.issued1995en_US
dc.identifier.urihttp://hdl.handle.net/10150/187241
dc.description.abstractIn this dissertation study, the problem of forming manufacturing cells is investigated with a focus on the cell formation methodology based on operation requirements and machine capability. Manufacturing economics, cell independence, within-cell layout and machine utilization are incorporated into the methodology. First, the formation of independent flow-line cells based on operation sequence is considered. Algorithms for sequence constrained part family formation are developed. A shortest path model with node-augmentation and an implicit enumeration algorithm are proposed to identify the optimal machine sequence and capacity, the result is compared with a greedy heuristic. Formation of general Group Technology (GT) cells without sequence constraints is then investigated. Parts are grouped into families according to the operation types required. A mathematical formulation is presented for machine group selection problem. This integer model takes into account fixed machine cost, variable production cost, setup cost, and intracell material handling cost. For the problems with moderate size, the model can be solved to optimality by a standard package such as Cplex. Heuristic procedures are also developed for machine group selection problem, which can identify good feasible solutions more efficiently. Finally, improvements on the methodology are made with an effort to integrate the decisions regarding part grouping and machine selection. Experimental results are provided to compare the performance and computational efficiency of different algorithms/heuristics. The effect of changing cost structure on the heuristic performance is also investigated for the case without sequence constraints through statistical experiment design. Future research is discussed at the conclusion of this dissertation.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.titleCell formation based on operation requirements and machine capability.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairAskin, Ronald G.en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberGoldberg, Jeffrey B.en_US
dc.contributor.committeememberCiarallo, Franken_US
dc.identifier.proquest9603387en_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-08-23T20:50:59Z
html.description.abstractIn this dissertation study, the problem of forming manufacturing cells is investigated with a focus on the cell formation methodology based on operation requirements and machine capability. Manufacturing economics, cell independence, within-cell layout and machine utilization are incorporated into the methodology. First, the formation of independent flow-line cells based on operation sequence is considered. Algorithms for sequence constrained part family formation are developed. A shortest path model with node-augmentation and an implicit enumeration algorithm are proposed to identify the optimal machine sequence and capacity, the result is compared with a greedy heuristic. Formation of general Group Technology (GT) cells without sequence constraints is then investigated. Parts are grouped into families according to the operation types required. A mathematical formulation is presented for machine group selection problem. This integer model takes into account fixed machine cost, variable production cost, setup cost, and intracell material handling cost. For the problems with moderate size, the model can be solved to optimality by a standard package such as Cplex. Heuristic procedures are also developed for machine group selection problem, which can identify good feasible solutions more efficiently. Finally, improvements on the methodology are made with an effort to integrate the decisions regarding part grouping and machine selection. Experimental results are provided to compare the performance and computational efficiency of different algorithms/heuristics. The effect of changing cost structure on the heuristic performance is also investigated for the case without sequence constraints through statistical experiment design. Future research is discussed at the conclusion of this dissertation.


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