Computers and Industrial Engineering
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm
Expert Systems with Applications: An International Journal
Two stages of case-based reasoning - Integrating genetic algorithm with data mining mechanism
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An improved approach to find membership functions and multiple minimum supports in fuzzy data mining
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach
Expert Systems with Applications: An International Journal
Heuristic algorithms for the two-stage hybrid flowshop problem
Operations Research Letters
A note on weighted completion time minimization in a flexible flow shop
Operations Research Letters
An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem
Expert Systems with Applications: An International Journal
Computers and Electronics in Agriculture
Parallel tabu search algorithm for the hybrid flow shop problem
Computers and Industrial Engineering
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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.