Knowledge-based decision support system for scheduling in a flexible flow system
Piramuthu, Selwyn
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https://hdl.handle.net/2142/20587
Description
Title
Knowledge-based decision support system for scheduling in a flexible flow system
Author(s)
Piramuthu, Selwyn
Issue Date
1992
Doctoral Committee Chair(s)
Shaw, Michael J.
Department of Study
Business Administration
Discipline
Business Administration
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Business Administration, General
Artificial Intelligence
Computer Science
Language
eng
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.
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