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Shaw, Michael J. Piramuthu, Selwyn 2011-05-07T12:43:31Z 2011-05-07T12:43:31Z 10000-01-01 1992 Decision Support Systems (DSSs) are necessary resources for most complex decision making situations. The environment under which the DSSs function are, for the most part, evolving and, hence the DSSs should have the capability to adapt themselves as per the changes in the characteristics of the environment. An adaptive DSS that can function in a dynamic environment is proposed in this thesis, incorporating simulation modeling and inductive learning, to improve the overall performance of the system. We develop an adaptive DSS incorporating learning in this thesis. We also attempt to refine the learned knowledge as is stored in the knowledge-base, thus reducing the effects of noise in the training examples on learning. The proposed framework is illustrated by scheduling a flexible flow system (FFS). Example of an application environment representing an FFS is the surface mount technology (SMT) facility used in printed circuit board (PCB) manufacturing. Scheduling in a PCB assembly facility involves decisions to be taken both at part-release and dispatching at machines stages. We develop a bi-level DSS to accomodate these interactions. The performance of the resulting system is shown to improve over systems using just one best heuristic for scheduling. Made available in DSpace on 2011-05-07T12:43:31Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9236571.pdf: 4139010 bytes, checksum: 90406ace579b2ae6f89b8d7cd06dd4c6 (MD5) Previous issue date: 1992 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:44:54Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:19:49-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9236571 (UMI)AAI9236571 http://hdl.handle.net/2142/20587 eng Copyright 1992 Piramuthu, Selwyn Business Administration, General Artificial Intelligence Computer Science Knowledge-based decision support system for scheduling in a flexible flow system text Business Administration Business Administration University of Illinois at Urbana-Champaign Dissertation Ph.D.