A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory

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One of the most challenging production decisions in the semiconductor testing industry is to select the most appropriate dispatching rule which can be employed on the shop floor to achieve high manufacturing performance against a changing environment. Job dispatching in the semiconductor final testing industry is severely constrained by many resources conflicts and has to fulfil a changing performance required by customers and plant managers. In this study we have developed a hybrid knowledge discovery model, using a combination of a decision tree and a back-propagation neural network, to determine an appropriate dispatching rule using production data with noise information, and to predict its performance. We built an object-oriented simulation model to mimic shop floor activities of a semiconductor testing plant and collected system status and resultant performances of several typical dispatching rules, earliest-due-date (EDD) rule, first-come-first-served rule, and a practical dispatching heuristic taking set-up reduction into consideration. Performances such as work-in-process, set-up overhead, completion time, and tardiness are examined. Experiments have shown that the proposed decision tree found the most suitable dispatching rule given a specific performance measure and system status, and the back propagation neural network then predicted precisely the performance of the selected rule.