Abstract: Re-entrant scheduling is an important search problem
with many constraints in the flow shop. In the literature, a number of
approaches have been investigated from exact methods to
meta-heuristics. This paper presents a genetic algorithm that encodes
the problem as multi-level chromosomes to reflect the dependent
relationship of the re-entrant possibility and resource consumption.
The novel encoding way conserves the intact information of the data
and fastens the convergence to the near optimal solutions. To test the
effectiveness of the method, it has been applied to the
resource-constrained re-entrant flow shop scheduling problem.
Computational results show that the proposed GA performs better than
the simulated annealing algorithm in the measure of the makespan
Abstract: This paper develops a quality estimation method with
the application of fuzzy hierarchical clustering. Quality estimation is
essential to quality control and quality improvement as a precise
estimation can promote a right decision-making in order to help
better quality control. Normally the quality of finished products in
manufacturing system can be differentiated by quality standards. In
the real life situation, the collected data may be vague which is not
easy to be classified and they are usually represented in term of fuzzy
number. To estimate the quality of product presented by fuzzy
number is not easy. In this research, the trapezoidal fuzzy numbers
are collected in manufacturing process and classify the collected data
into different clusters so as to get the estimation. Since normal
hierarchical clustering methods can only be applied for real numbers,
fuzzy hierarchical clustering is selected to handle this problem based
on quality standards.