A hierarchical heuristic approach for machine loading problems in a partially grouped environment

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dc.contributor.advisor Malave, Cesar O. en_US
dc.creator Lee, Jong Hwan en_US
dc.date.accessioned 2004-09-30T01:47:08Z
dc.date.available 2004-09-30T01:47:08Z
dc.date.created 2003-12 en_US
dc.date.issued 2004-09-30T01:47:08Z
dc.identifier.uri http://handle.tamu.edu/1969.1/229
dc.description.abstract The loading problem in a Flexible Manufacturing System (FMS) lies in the allocation of operations and associated cutting tools to machines for a given set of parts subject to capacity constraints. This dissertation proposes a hierarchical approach to the machine loading problem when the workload and tool magazine capacity of each machine are restrained. This hierarchical approach reduces the maximum workload of the machines by partially grouping them. This research deals with situations where different groups of machines performing the same operation require different processing times and this problem is formulated as an integer linear problem. This work proposes a solution that is comprised of two phases. In the first phase (Phase I), demand is divided into batches and then operations are allocated to groups of machines by using a heuristic constrained by the workload and tool magazine capacity of each group. The processing time of the operation is different for each machine group, which is composed of the same identical machines; however, these machines can perform different sets of operations if tooled differently. Each machine and each group of machines has a limited time for completing an operation. Operations are allocated to groups based on their respective workload limits. In the second phase (Phase II), demand is divided into batches again and operations are assigned to machines based on their workload and tool magazine capacity defined by Longest Processing Time (LPT) and Multifit algorithms. In Phase II, like Phase I, partial grouping is more effective in balancing the workload than total grouping. In partial grouping, each machine is tooled differently, but they can assist one another in processing each individual operation. Phase I demonstrates the efficiency of allocating operations to each group. Phase II demonstrates the efficiency of allocating operations to each machine within each group. This two-phase solution enhances routing flexibility with the same or a smaller number of machines through partial grouping rather than through total grouping. This partial grouping provides a balanced solution for problems involving a large number of machines. Performance of the suggested loading heuristics is tested by means of randomly generated tests. en_US
dc.description.provenance Made available in DSpace on 2004-09-30T01:47:08Z (GMT). No. of bitstreams: 2 etd-tamu-2003C-INEN-LEE-1.pdf: 367341 bytes, checksum: b976c79876587dcfd00a3386239cbaeb (MD5) etd-tamu-2003C-INEN-LEE-1.pdf.txt: 133709 bytes, checksum: 6676d182269dce697e3e2159436cfc06 (MD5) en
dc.format.extent 367341 bytes
dc.format.extent 133709 bytes
dc.format.medium electronic en_US
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en_US en_US
dc.publisher Texas A&M University en_US
dc.subject Flexible Manufacturing Systems en_US
dc.subject Loading problem en_US
dc.subject Partial grouping en_US
dc.title A hierarchical heuristic approach for machine loading problems in a partially grouped environment en_US
thesis.degree.department Industrial Engineering en_US
thesis.degree.discipline Industrial Engineering en_US
thesis.degree.grantor Texas A&M University en_US
thesis.degree.name PHD en_US
thesis.degree.level Doctoral en_US
dc.contributor.committeeMember Banerjee, Amarnath en_US
dc.contributor.committeeMember Ding, Yu en_US
dc.contributor.committeeMember Hsieh, Sheng-Jen (Tony) en_US
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US

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