Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: A fuzzy approach
In this paper, an efficient algorithm is presented to solve flexible flow-shop problems using fuzzy approach. The goal is to minimize the total job tardiness. We assume parallel machines with different operation times. In this algorithm, parameters like "due date" and "operation time" follow a triangular fuzzy number. We used data mining technique as a facilitator to help in finding a better solution in such combined optimization problems. Therefore, using a combination of genetic algorithm and an attribute-deductive tool such as data mining, a near optimal solution can be achieved. According to the structure of the presented algorithm, all of the feasible solutions for the flexible flow-shop problem are considered as a database. Via data mining and attribute-driven deduction algorithm, hidden relationships among reserved solutions in the database are extracted. Then, genetic algorithm can use them to seek an optimum solution. Since there are inherited properties in the solutions provided by genetic algorithm, future generation should have the same behavioral models more than preliminary ones. Data mining can significantly improve the performance of the genetic algorithm through analysis of near-optimal scheduling programs and exploration of hidden relationships among pre-reached solutions. (C) 2010 Elsevier Ltd. All rights reserved.
genetic algorithm data mining attribute-driven deduction flexible flow-shop scheduling fuzzy sets