A Hybrid Artificial Neural Networks And Knowledge Based Expert Systems Approach To Flexible Manufacturing System Scheduling

A Hybrid Artificial Neural Networks and Knowledge-based Expert Systems Approach to Flexible Manufacturing System Scheduling

A Hybrid Artificial Neural Networks and Knowledge-based Expert Systems Approach to Flexible Manufacturing System Scheduling

"Flexible manufacturing system (FMS) scheduling is a complex problem in nature that leads to a high level of uncertainty due to limited feasible solutions in an extensive search space. Heuristics involving dispatching rules have been widely utilized to obtain good solutions. This strategy has been recently enhanced by FMS scheduling researchers using knowledge-based expert systems as means of resolving scheduling problems. Unfortunately, the knowledge-based expert systems (KBESs) developed are limited in real-time performance due to cracks in their encoded knowledge or lack of adequate plans to address the changing environment. A framework is developed displaying the capabilities of automatic learning and self-improvement, providing the necessary adaptive scheme to respond to the dynamic nature of flexible manufacturing systems. This proposed framework uses a hybrid architecture that integrates artificial neural networks and knowledge-based expert systems to generate solutions for the real time scheduling of flexible manufacturing systems. In this framework, the artificial neural networks perform pattern recognition and, due to their inherent characteristics, support the implementation of automated knowledge acquisition and refinement strategies through a feedback mechanism. They enable the system to recognize patterns in the tasks to be solved in order to select the best scheduling rule according to different criteria. The knowledge-based expert systems, on the other hand, drive the inference strategy and interpret the constraints and restrictions imposed by the upper levels of the control hierarchy of the flexible manufacturing system. The level of self-organization thus achieved provides a system architecture with a higher probability of success than traditional approaches"--Abstract, leaf iii.

Encyclopedia of Operations Research and Management Science

Operations Research: 1934-1941," 35, 1, 143-152; "British The goal of the Encyclopedia of Operations Research and Operational Research in World War II," 35, 3, 453-470; Management Science is to provide to decision makers and "U. S. Operations Research in World War II," 35, 6, 910-925; problem solvers in business, industry, government and and the 1984 article by Harold Lardner that appeared in academia a comprehensive overview of the wide range of Operations Research: "The Origin of Operational Research," ideas, methodologies, and synergistic forces that combine to 32, 2, 465-475. form the preeminent decision-aiding fields of operations re search and management science (OR/MS). To this end, we The Encyclopedia contains no entries that define the fields enlisted a distinguished international group of academics of operations research and management science. OR and MS and practitioners to contribute articles on subjects for are often equated to one another. If one defines them by the which they are renowned. methodologies they employ, the equation would probably The editors, working with the Encyclopedia's Editorial stand inspection. If one defines them by their historical Advisory Board, surveyed and divided OR/MS into specific developments and the classes of problems they encompass, topics that collectively encompass the foundations, applica the equation becomes fuzzy. The formalism OR grew out of tions, and emerging elements of this ever-changing field. We the operational problems of the British and U. s. military also wanted to establish the close associations that OR/MS efforts in World War II.