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|Title:||Knowledge-based decision support system for scheduling in a flexible flow system|
|Doctoral Committee Chair(s):||Shaw, Michael J.|
|Department / Program:||Business Administration|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Subject(s):||Business Administration, General
|Abstract:||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.
|Rights Information:||Copyright 1992 Piramuthu, Selwyn|
|Date Available in IDEALS:||2011-05-07|
|Identifier in Online Catalog:||AAI9236571|
This item appears in the following Collection(s)
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Business Administration