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Job shop scheduling with the availability of more than one machine to perform an operation, also known as the flexible job shop scheduling problem, is computationally NP-hard. An efficient scheduling method is proposed here, using a genetic algorithm that incorporates heuristic rules. The scheduler's goal is to minimize mean tardiness. There are two types of decision making required: job selection and machine selection. Combinations of five job selection and five machine selection heuristics are examined. Numerical experiments show that the combination of Yoda et al.'s (SL/RPN)+SPT rule for job selection and Eguchi et al.'s (WINQ+RPT+PT)×PT rule for machine selection provide the best performance under different shop conditions when incorporated into the genetic algorithm. It is also found that applying genetic algorithm only for either job selection or machine selection can generate good schedules, depending on conditions.

This paper considers an e 伍 cient optimization method fbr job shop scheduling with altemative machines ． There are some types of optimization techniques such as exact optimization methods ， meta − heuristic methods and priority rule based sgheduling simulation ． Exact optimization methods such as branch and bound methods are di価 cult to apply to ［ arge scale realistic pmblems ；the most practical approach is to use priority rule based scheduling simulation ． In recent years ' increase of computer capacity ， meta − heuristic methods suoh as genetic algorit s have also becomc a promising approach ． We have proposed a genetic algorithm incorporating prlority rules forjob sequencing ( ： ) ． This paper reports the experimental results of apPlying this apProach not only ｛

This paper considers flow shop scheduling problems with sequence dependent setup time. The makespan criterion has been considered. In this paper presented a comparison of three heuristics for solves this problem. The memetic algorithm, genetic algorithm and NEH heuristic have been compared. In the experimental, the result from memetic algorithm is maximum the best solution. Therefore, the MA heuristic outperforms other heuristic.

This paper considers single machine scheduling problem. The objective is to determine sum of earliness and tardiness cost has been minimized. The memetic algorithm is developed to solve this problem. To evaluate performance of memetic algorithm, the solution of propose method is compare with the optimal solution. The results show that the average percentage deviation is less than 10. Here, the computational time required by the MA is significantly less than the time required by the optimal solution method. This result is more emphasized as the problem size is getting larger.