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dc.contributor.authorDadone, Paoloen_US
dc.date.accessioned2014-03-14T20:51:00Z
dc.date.available2014-03-14T20:51:00Z
dc.date.issued1997-12-05en_US
dc.identifier.otheretd-1898-212313en_US
dc.identifier.urihttp://hdl.handle.net/10919/36531
dc.description.abstract

Flexible manufacturing systems (FMS) are production systems consisting of identical multipurpose numerically controlled machines (workstations), automated material handling system, tools, load and unload stations, inspection stations, storage areas and a hierarchical control system. The latter has the task of coordinating and integrating all the components of the whole system for automatic operations. A particular characteristic of FMSs is their complexity along with the difficulties in building analytical models that capture the system in all its important aspects. Thus optimal control strategies, or at least good ones, are hard to find and the full potential of manufacturing systems is not completely exploited.

The complexity of these systems induces a division of the control approaches based on the time frame they are referred to: long, medium and short term. This thesis addresses the short-term control of a FMS. The objective is to define control strategies, based on system state feedback, that fully exploit the flexibility built into those systems. Difficulties arise since the metrics that have to be minimized are often conflicting and some kind of trade-offs must be made using "common sense". The problem constraints are often expressed in a rigid and "crisp" way while their nature is more "fuzzy" and the search for an analytical optimum does not always reflect production needs. Indeed, practical and production oriented approaches are more geared toward a good and robust solution.

This thesis addresses the above mentioned problems proposing a fuzzy scheduler and a reinforcement-learning approach to tune its parameters. The learning procedure is based on evolutionary programming techniques and uses a performance index that contains the degree of satisfaction of multiple and possibly conflicting objectives. This approach addresses the design of the controller by means of language directives coming from the management, thus not requiring any particular interface between management and designers.

The performances of the fuzzy scheduler are then compared to those of commonly used heuristic rules. The results show some improvement offered by fuzzy techniques in scheduling that, along with ease of design, make their applicability promising. Moreover, fuzzy techniques are effective in reducing system congestion as is also shown by slower performance degradation than heuristics for decreasing inter- arrival time of orders. Finally, the proposed paradigm could be extended for on-line adaptation of the scheduler, thus fully responding to the flexibility needs of FMSs.

en_US
dc.publisherVirginia Techen_US
dc.relation.haspartThesis.pdfen_US
dc.rightsI hereby grant to Virginia Tech or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.en_US
dc.subjectsoft computingen_US
dc.subjectschedulingen_US
dc.subjectreinforcement learningen_US
dc.subjectFuzzy logicen_US
dc.subjectdiscrete event dynamic systemsen_US
dc.titleFuzzy Control of Flexible Manufacturing Systemsen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical Engineeringen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairVanLandingham, Hugh F.en_US
dc.contributor.committeememberSherali, Hanif D.en_US
dc.contributor.committeememberBay, John S.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-1898-212313/en_US
dc.date.sdate1997-12-05en_US
dc.date.rdate1999-02-17
dc.date.adate1998-02-17en_US


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