A biobjective slack-diversifying nonlinear fluctuation-smoothing rule (biSDNFS) is proposed in the present work to improve the scheduling performance of a wafer fabrication factory. This rule was derived from a one-factor bi-objective nonlinear fluctuation-smoothing rule (1f-biNFS) by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several previous studies. The efficacy of the biSDNFS was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future. 1. Introduction Semiconductor manufacturing is undoubtedly one of the most noticeable high-technology industries because semiconductor products have widespread applications. However, the life cycles of new semiconductor products are getting shorter. Therefore, semiconductor manufacturers are facing pressure to meet the various needs of customers within shorter time spans. Manufacturers consider rapid product development, agile production, shortened response times, and similar strategies to be viable. All of these strategies compress the cycle times of related processes. Of the various types of cycle times, production cycle time is particularly important because it determines the time of delivery to customers. In other words, if the production cycle time is shortened, the delivery to customers will be faster. To this end, shortening the production cycle time through effective job dispatching is an important task . Much research has been done concerning semiconductor shop floor control as a special type of supervisory control , particularly in the domains of deterministic scheduling and job dispatching. However, Chen and Lin , Chen and Wang , and Chen  have noted that for semiconductor factories, job dispatching is very difficult. Theoretically, this is an NP-hard problem. In practice, many semiconductor factories suffer from lengthy cycle times and thus are not able to make favorable promises to their customers. This study discusses how to determine the sequence of jobs to be processed on each machine in a semiconductor factory so as to shorten the cycle times of jobs. To this end, an innovative dispatching rule is proposed, which involves the applications of fuzzy logic, artificial neural networks, and mathematical programming. In this field, some innovative dispatching rules considering job parameters have been proposed recently. For example, Chen  reported a nonlinear fluctuation smoothing rule that uses the divisor
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