تصميم نموذج خوارزمية جينية-عصبية لحل مشكلة جدولة ورش العمل المضببة في حالة الأهداف المتعددة : دراسة حالة

Abstract EN

This study adopted a methodology of work to build a hybrid model using the artificial intelligence systems, which is represented in Hopfield neural networks and the genetic algorithm .Resolving any Fuzzy Job Shop Scheduling Problem (FJSSP) is through fuzzing the processing times by a triple fuzzy number and fuzzing due date by a double fuzzy number.

Hopfield's neural networks are used to improve the performance of the genetic algorithm by generating an initial generation of P size, represents near-optimization solutions, used by the genetic algorithm to perform mating, crossover, and mutation.

The study was applied to Al-Ghadeer Printing and Publishing Co.

Ltd., where the fuzzy processing times and the fuzzy due date of the four different jobs were processed by eleven machines according to the nature of the job and based on the data in the company records.

Finally, the study was able to reach a set of conclusions, the most important of which is to achieve the hypothesis of the involved research.

The hybrid model proposed by the researcher will be better in obtaining the optimal jobs sequence, to reduce the finish time and to reach customer satisfaction by delivering the product at the due date through the method of fuzzing the neural networks and the method of fuzzing the genetic algorithm.