Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, range estimation of the parameters would not be very useful. Considering such uncertainties, finding a good situation-dependent dispatching rule would be more suitable than solving the RCPSP under uncertainties. In this paper we apply the Pitts approach, one of the genetic algorithms, to the situation-dependent dispatching rule acquisition. We compare the obtained rule with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rule in the problems with unpredictable wafer errors.
This paper deals with an approximate solution method by decomposing a search space dynamically combined with genetic algorithm(GA) and Lagrangian relaxation(LR) method for solving a job-shop scheduling problem. In this method a subspace of a search space is constructed by using information on partial processing order relations between operations which use same resources. We search these subspaces by using GA. We evaluate each subspace with lower bound obtained by using LR method and with minimum value of the set up cost obtained by the structure of subspace. We reduce the size of solution space gradually, and search a feasible solution in the subspace finally obtained. Some numerical experiments are included to evaluate the proposed method.
The scheduling of semiconductor wafer testing processes may be seen as a resource constraint project scheduling problem (RCPSP), but it includes uncertainties caused by wafer error, human factors, etc. Because uncertainties are not simply quantitative, estimating the range of the parameters is not useful. Considering such uncertainties, finding a good situationdependent dispatching rule is more suitable than solving an RCPSP under uncertainties. In this paper we apply machine learning approaches to acquiring situation-dependent dispatching rule. We compare obtained rules and examine their effectiveness and usefulness in problems with unpredictable wafer testing errors.
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