Rule-based weight definition for multi-objective fuzzy scheduling with the OWA operator

We propose a rule-based dynamic definition of weight values for multi-objective fuzzy scheduling problems with the OWA operator. When we employ the scheme of the OWA operator for multi-objective fuzzy scheduling problems, we should assign the weight vector w in advance. Therefore, we propose a rule-based weight definition method in order to reduce the effort to define the weight values in the OWA operator. In our multi-objective fuzzy scheduling problems, we take account of the importance of each job. That is, we use a different unit reward for the satisfaction grade of each job. We employ a genetic algorithm to maximize the fitness function, which is the function with the OWA operator. Finally, we show that the introduction of the rule-based weight definition method is effective for finding solutions