NDLI: A knowledge-based giffler-thompson heuristic for rescheduling job-shops

Content Provider	IEEE Xplore Digital Library
Author	Aufenanger, M. Lipka, N. Klopper, B. Dangelmaier, W.
Copyright Year	2009
Description	Author affiliation: University of Weimar, Germany (Lipka, N.) \|\| Department of CIM, University of Paderborn, Germany (Aufenanger, M.; Klopper, B.; Dangelmaier, W.)
Abstract	Even today rescheduling in job-shop systems is still a challenge. There are approaches to solve the problem like analytical, heuristic and simulation ones. Analytical methods cannot meet the requirements of rescheduling regarding solution time, especially for large problem instances. Analytic approaches as well as simulation based systems need a long calculation time. To generate good solutions a lot of simulation runs have to be made. Thus, extensive research was done in heuristic rescheduling systems. Usually, dispatching rules are used. Their drawback is that it is impossible to define a superior dispatching rule for all situations in the workshop. To solve this problem, we intend to combine the Giffler/Thompson heuristic with a knowledge based system - a naïve bayes classifier with offline generated training data. This combination enables the selection of the best dispatching rule dynamically, depending on the system's state. The paper presents the concept of our approch; a first stage of research progress. Therefore, preliminary results on learning scheduling decisions are shown.
Starting Page	22
Ending Page	28
File Size	482033
Page Count	7
File Format	PDF
ISBN	9781424427574
DOI	10.1109/SCIS.2009.4927010
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Job shop scheduling Sections Knowledge acquisition Training data Optimal scheduling Production Control systems Dispatching Manufacturing systems
Content Type	Text
Resource Type	Article

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