Bulanık Öğrenme Etkili Akış Tipi Çizelgeleme Problemlerinin Paralel Kanguru Algoritması İle Çözümü

Abstract

Öğrenme etkili çizelgeleme problemlerinin daha gerçekçi sonuçlar verdiği bilinmektedir. Bu çalışmada, insan faktöründen kaynaklanan öğrenme etkisini göz önüne alan akış tipi çizelgeleme problemleri ele alınmıştır. Belirsiz işlem sürelerine çizelgeleme problemlerinde sıklıkla karşılaşılır. Bu nedenle işlem süreleri bulanıklaştırılmıştır. Bulanık mantık literatürde bu problemlerin çözümü için sıkça kullanılan bir yöntemdir. Paralel Kanguru Algoritması çözüm metodu olarak kullanılmıştır. Çalışma diğer çalışmalardaki sonuçlarla kıyaslanarak sonuçlandırılmıştır.

Keywords

• Akış tipi çizelgeleme,Bulanık mantık,Öğrenme etkisi,Paralel Kanguru Algoritması

USING PARALLEL KANGAROO ALGORITHM TO SOLVE FLOW SHOP SCHEDULING PROBLEMS WITH FUZZY LEARNING EFFECT

Abstract

Knowing that the scheduling problems considering learning ability can give more realistic results, this paper deals with flow shop scheduling problem considering learning effects due to the human factor. Uncertain processing time is frequently encountered in scheduling problems; therefore, the processing times are fuzzed. Fuzzy logic is a frequently used method to solve this problem in the literature. Parallel Kangaroo Algorithm is used as a solution method. The study is concluded with comparing the results found in other studies.

Keywords

• Flow shop scheduling,Fuzzy logic,Learning effects,Parallel Kangaroo Algorithm

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