(Enter summary)
Abstract: In many industrial applications, large centralized software systems are not as effective as distributed networks
of relatively simpler computerized agents. For example, to compete effectively in today's markets, manufacturers
must be able to design, implement, reconfigure, resize, and maintain manufacturing facilities rapidly and
inexpensively. Because modern manufacturing depends heavily on computer systems, these same requirements
apply to manufacturing control software, and are more easily... (Update)
Context of citations to this paper: More
.... Planning and Production Scheduling In recent years, Multi Agent System (MAS) 1] has been an active research topic in manufacturing arena [2]. Due to the difficulties in solving process planning and production scheduling problems using traditional centralized problem solving...
.... approaches to factory control have since been explored and implemented, many of which deal with the shop floor scheduling problem [Par96]. Market mechanisms [KVM96] have been used, where processor agents build schedules by submitting appropriate bids for customer orders and...
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BibTeX entry: (Update)
H. V. D. Parunak. 1995. Applications of distributed artificial intelligence in industry. In Foundations of Distributed Artificial Intelligence (eds. G. M. P. O'Hare and N. R. Jennings), Wiley. http://citeseer.ist.psu.edu/parunak94applications.html More
@incollection{ parunak95applications,
author = "H. V. D. Parunak",
title = "Applications of Distributed Artificial Intelligence in Industry",
booktitle = "Foundations of Distributed {AI}",
publisher = "John Wiley \& Sons",
editor = "G. M. P. O'Hare and N. R. Jennings",
year = "1995",
url = "citeseer.ist.psu.edu/parunak94applications.html" }
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