An artificial intelligence approach to precedence network generation for assembly line balancing
Identifieur interne : 003107 ( Main/Exploration ); précédent : 003106; suivant : 003108An artificial intelligence approach to precedence network generation for assembly line balancing
Auteurs : K. Sudhir [Inde] ; R. Rajagopalan [Inde]Source :
- Computers in Industry [ 0166-3615 ] ; 1991.
Abstract
Line balancing is a critical problem in continuous production systems. Ever since the problem was first isolated in 1954, a number of techniques—empirical, heuristic and mathematical —have been developed. Despite the fact that all these techniques are dependent on the existence of well developed precedence networks, not much attention has been paid to the development of algorithms for generating these networks. This negligence may be attributed to the fact that precedence network generation is a complex heuristic process for which there are no computationally efficient algorithms. Recent developments in artificial intelligence (AI) have made it possible to address this problem. This paper describes an AI approach to this problem and presents a prototype system (angel) built through this approach. The system has a frame-based hierarchical knowledge representation and a blackboard architecture. It uses knowledge sources called critics to solve this problem. The approach attempts to model the problem in a concise and uniform framework using a frame-based knowledge representation model. Many precedence relationships are due to underlying physical requirements. These physical requirements are captured using the physical critics and are generally true for all classes of products. Engineering considerations (both theoretical and empirical) that may be specific to a particular product or class may also be captured using the framework. With this knowledge and the product details, the system aids in the development of the precedence network with increasing levels of detail by using a hierarchical approach.
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DOI: 10.1016/0166-3615(92)90112-Z
Affiliations:
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Ever since the problem was first isolated in 1954, a number of techniques—empirical, heuristic and mathematical —have been developed. Despite the fact that all these techniques are dependent on the existence of well developed precedence networks, not much attention has been paid to the development of algorithms for generating these networks. This negligence may be attributed to the fact that precedence network generation is a complex heuristic process for which there are no computationally efficient algorithms. Recent developments in artificial intelligence (AI) have made it possible to address this problem. This paper describes an AI approach to this problem and presents a prototype system (angel) built through this approach. The system has a frame-based hierarchical knowledge representation and a blackboard architecture. It uses knowledge sources called critics to solve this problem. The approach attempts to model the problem in a concise and uniform framework using a frame-based knowledge representation model. Many precedence relationships are due to underlying physical requirements. These physical requirements are captured using the physical critics and are generally true for all classes of products. Engineering considerations (both theoretical and empirical) that may be specific to a particular product or class may also be captured using the framework. With this knowledge and the product details, the system aids in the development of the precedence network with increasing levels of detail by using a hierarchical approach.</div></front></TEI><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Sudhir, K" sort="Sudhir, K" uniqKey="Sudhir K" first="K." last="Sudhir">K. Sudhir</name></noRegion><name sortKey="Rajagopalan, R" sort="Rajagopalan, R" uniqKey="Rajagopalan R" first="R." last="Rajagopalan">R. Rajagopalan</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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