Wednesday 22nd of November 2017
 

An Effective Genetic Algorithm for Job Shop Scheduling with Fuzzy Degree of Satisfaction


Akeela M.Al-Atroshi, Sama T. Azez and Baydaa S.Bhnam

The present study suggests a hybrid new fuzzy-genetic algorithm for solving the job shop scheduling problem. Traditional scheduling method does not keep pace with the requirements of the development in the field of manufacturing. Therefore, the current proposed algorithm offers a hybrid intelligent solution between two approaches: genetic algorithm to arrange the jobs randomly, and applied fuzzy logic to build objective function for genetic algorithm. All these are to find optimal degree of satisfaction that achieves optimal chain to schedule a production order using function max(min(satisfaction degree)). The present study includes the modeling of the objective function and adopting a fuzzy logic to solve the issue of scheduling production orders. The matlab is used for Programmable fuzzy Logic, whereas the C++ is used for programming the genetic algorithm with mechanism for linking C++ and matlab. Finally, the algorithm is tested on instances of 10 working procedures (jobs) and 3 machines. The result shows that the hybrid fuzzy-genetic algorithm has been successfully applied to the job shop scheduling problems.

Keywords: Fuzzy Logic, Genetic Algorithm, Job-shop Scheduling, Degree of Satisfaction

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ABOUT THE AUTHORS

Akeela M.Al-Atroshi
intrest in computer sc. , IT and production management

Sama T. Azez
intrest in computer sc., A.I (Genetic Alg., Ant Alg.), security (cryptology, hiding), database and IT

Baydaa S.Bhnam
intrest in computer sc., A.I (fuzzy logic and GA)


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