Fuzzy programming for multiobjective fuzzy job shop scheduling with alternative machines through genetic algorithms

The optimization of Job Shop scheduling is very important because of its theoretical and practical significance. Much research about it has been reported in recent years. But most of them were about classical Job Shop scheduling. The existence of a gap between scheduling theory and practice has been reported in literature. This work presents a robust procedure to solve multiobjective fuzzy Job Shop scheduling problems with some more realistic constraints such as fuzzy processing time, fuzzy duedate and alternative machine constraints for jobs. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, multiobjective fuzzy Job Shop scheduling problems have been formulated as three-objective ones which not only maximize the minimum agreement index but also maximize the average agreement index and minimize the maximum fuzzy completion time. By adopting two-chromosome representation, an extended G&T algorithm which is suitable for solving the fuzzy Job Shop scheduling with alternative machines has been proposed. Finally, numerical examples are given to illustrate the effectiveness of our proposed method that provides a new way to study planning and scheduling problems in fuzzy circumstances.