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

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 naive 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.