(4) Diversifying the slack by solving an FNLP model: the emergence of ties may lead to incorrect scheduling results.
Section 2 is divided into four parts: a-cut operations, effective FBPN, fuzzified dispatching, and FNLP. First, the concepts of [alpha] cuts and [alpha]-cut operations are introduced.
The [alpha]-cut operations are applied to solve the FNLP problem.
Finally, the following FNLP problem is to be solved:
The proposed FNLP problem is intractable and may need to be converted into an equivalent NLP problem to be solved.
Finally, the following NLP model is optimized instead of the original FNLP problem:
After the fuzzy remaining cycle time estimate had been fed into the fuzzy four-objective fluctuation smoothing rule, an FNLP problem was solved to determine the values of the five parameters in the rule, so as to optimize the scheduling performance.
This study demonstrates that an FNLP approach can consider such uncertainties and optimize the performance of multiobjective scheduling in a wafer fabrication factory.
To reduce the number of ties, the slacks of jobs need to be diversified, which results in an FNLP problem.