A New Approach to Sol ve Flowshop Scheduling Problems by Artifical Immune Systems

n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımr prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir.

Akış Tipi Çizelgeleme Problemlerinin Yapay Bağışıklık Sistemleri ile Çözümünde Yeni Bir Yaklaşım

The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing.

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