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

Year 2017, Volume: 1 Issue: 1, 31 - 48, 30.06.2017

Ahmet Sezer Küpeli , Orhan Engin , Batuhan Eren Engin

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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