Öğr. Gör. Dr. Semin PAKSOY, Yrd. Doç. Dr. Arzu UZUN

Genetik Algoritma ile Kaynak Kısıtlı Proje Çizelgeleme

Bu çalışmada kaynak kısıtlı proje çizelgeleme problemlerinin genetik algoritma yaklaşımı ile çözümü ele alınmıştır Başlangıçta genetik algoritmanın temel kavramlarına yer verilerek proje çizelgeleme şemaları ve çizelgelemede göz önünde bulundurulması gereken unsurlar özetlenmiştir Daha sonra Delphi 6 0 da geliştirilen genetik algoritmanın otuz faaliyetli ve dört kaynak kullanan standart test problemlerindeki sonuçlarına yer verilmiştir Geliştirilen algoritmanın iterasyon sayısı çaprazlama oranı ve öncelik kuralları açısından davranışları test edilmeye çalışılmıştır Son olarak çizelgeleme problemlerinin faaliyet sayıları basit ya da karmaşık olma özelliklerinin algoritma üzerinde etkileri araştırılmaya çalışılmıştır Çalışma sonucunda geliştirilen genetik algoritma ile elde edilen çözümlerin genel olarak optimuma yakın çözümler olduğu görülmektedir Anahtar Kelimeler: Genetik algoritma Proje çizelgeleme Çizelge oluşturma şemaları Öncelik kuralları

Anahtar Kelimeler:

Genetik algoritma, Proje çizelgeleme, Çizelge oluşturma şemaları, Öncelik kuralları

Genetik Algoritma ile Kaynak Kısıtlı Proje Çizelgeleme

In this paper we consider genetic algorithm approach for the resource constrained project scheduling problems Starting with the summarizing the basic components of genetic algorithm approaches we describe the generating project schemes and its necessities Subsequently we present the results of our computational study which is depend on the standart set of test instances These instances have only four reources and thirty activities The results are obtained from a computer program that is developed by Delphi 6 0 Moreover the behaviour of the developed genetic algorithm is analyzed with respect to its main components such as iteration number crossover rate and priority rules Finally the influence of the problem characteristics like that simplicity complexity and size is examined on the performance of the genetic algorithm At the end of the study the results show that the solutions produced from the developed genetic algorithm is generally near the optimum solution Keywords: Genetic algorithm Project scheduling Schedule generation schemes Priority rules

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