Bu çalışmada Akış tipi çizelgeleme problemlerinin Genetik algoritma ile çözümünde çözüm süresi ve kalitesi üzerinde etkin olan üreme operatörü belirlenmiştir. Literatürde kullanılan Akış zamanlı rulet çemberi ve Yapay seçim yöntemi ile yeni geliştirilen Kısmi yapay seçim, Makine verimli rulet çemberi ve Ters yapay seçim üreme yöntemleri farklı büyüklükteki 10 problem üzerinde denenmiştir. İşlem süreleri, üniform dağılıma uygun olarak ?1-25? dakika arasında rassal olarak üretilen problemler üzerinde yapılan toplam 1250 adet deney sonucunda, akış tipi çizelgeleme problemlerinin Genetik algoritma ile çözümünde, iki makine problemleri için kısmı yapay seçim; çok makine problemleri için akış zamanlı rulet çemberi iyi performans göstermiştir Anahtar Kelimeler: Çizelgeleme, Genetik Algoritma, üreme yöntemi, parametre optimizasyonu.In this study reproduction operators of genetic algorithms are tested for solving flowshop scheduling problems which are in NP-hard class and the most effective operator is determined. In addition to flowtime roulette wheel and artificial reproduction method, three new developed reproduction methods namely, partial artificial reproduction, machine utility roulette wheel and inverse artificial reproduction are tested on different scaled flowshop scheduling problems with a Genetic algorithm program coded in Turbo Pascal. Processing times of the jobs in machines are generated randomly between ?1- 25? minutes according to uniform distribution. Problems are examined in two categories: 2-machine and multi-machine problems. In 2-machine problems the optimal solutions are determined with Johnson Algorithm and then compared with the solutions obtained with the Genetic Algorithms for different reproduction operators in six different scaled problems. In multi-machine problems the same reproduction operators are tested for 3-machine x 10-job, 4-machine x 10-job, 5-machine x 10-job and 7-machine x 15-job problems. The most effective reproduction operator is determined for both categories according to the results of 1250 experiments. As a result, partial artificial reproduction is determined to be the best performing reproduction operator for 2-machine problems and flowtime roulette wheel for multi-machine problems depending on the best makespan values. Keywords: Scheduling, Genetic Algorithm, reproduction operator, parameter optimization
