Öğretme-Öğrenme algoritmasını kullanarak iki yönlü karışık modelli montaj hattı dengeleme

Yıl 2018, Cilt: 24 Sayı: 4, 682 - 691, 17.08.2018

Alper Hamzadayı

Öz

Öğretme-Öğrenme-Tabanlı
Eniyileme (ÖÖTE) algoritması, diğer popülasyon-tabanlı algoritmalar kadar etkin
olduğu ortaya konmuş, popülasyon-tabanlı bir eniyileme algoritmasıdır. Bu
makalenin temel amacı, ÖÖTE algoritmasını kullanarak iki yönlü karışık modelli
montaj hattı dengeleme problemini ilk defa çözmektir. Yakın zamanda, stokastik
iki yönlü tek modelli montaj hattı dengeleme problemini çözmek için [1]’de
melez öğretme-öğrenme-tabanlı eniyileme (MÖÖTE) algoritması önerilmiştir.
[1]’de MÖÖTE algoritması en iyi bilinen 10 farklı meta-sezgisel algoritma ile
karşılaştırılmıştır. Yapılan testler MÖÖTE algoritmasının diğer algoritmalara
göre daha üstün bir performans sergilediğini ortaya koymuştur. Bu makalede
ayrıca, MÖÖTE algoritması iki yönlü karışık modelli montaj hattı dengeleme
problemini çözmek için adapte edilmiş ve algoritmanın performansı test
edilmiştir. Bu çalışmanın amacı önceden tanımlanmış çevrim süresinde karşılıklı
eşleşen istasyon sayısını ve toplam istasyon sayısını en aza indirmektir.
Literatürden alınan test problem grupları üzerinden kapsamlı bir deneysel
çalışma gerçekleştirilmiştir ve algoritmaların performansları var olan yaklaşımlarla
karşılaştırılmıştır. Deneysel çalışmalar ÖÖTE algoritmasının karşılaştırılan
diğer en iyi bilinen sezgisel algoritmalara karşı göze çarpan bir potansiyele
sahip olduğunu ve problemin çözümünde MÖÖTE algoritmasının bilinen en iyi
sezgisel algoritmalar kadar iyi performans sergilediğini ortaya koymuştur.

Anahtar Kelimeler

Montaj hattı dengeleme, Öğretme-öğrenme-tabanlı eniyileme algoritması, Melez öğretme-öğrenme-tabanlı eniyileme algoritması, İki yönlü montaj hatları, Karışık modelli montaj hatları

Balancing of mixed-model two-sided assembly lines using teaching-learning based optimization algorithm

Öz

The
Teaching-Learning Based Optimization (TLBO) algorithm is a population-based
optimization technique that has been shown to be competitive against other
population-based algorithms. The main purpose of this paper is to solve the
balancing problem of mixed-model two-sided assembly lines by using TLBO
algorithm first time in the literature. Most recently, hybrid
teaching-learning-based optimization (HTLBO) algorithm is proposed by [1] for
solving the balancing of stochastic simple two-sided assembly line problem. The
HTBLO algorithm is compared with the well-known 10 different meta-heuristic
algorithms in the literature in [1]. The tests performed underlined that HTLBO
algorithm presented more outstanding performance when compared to other
algorithms. In this paper, HTLBO algorithm is also adapted for solving the
problem of balancing mixed-model two-sided assembly line and its performance is
analysed. The objective function of this study is to minimize the number of
mated-stations and total number of stations for a predefined cycle time. A
comprehensive computational study is conducted on a set of test problems that
are taken from the literature and the performance of the algorithms are
compared with existing approaches. Experimental results show that TLBO
algorithm has a noticeable potential against to the best-known heuristic
algorithms and HTLBO algorithm results show that it performs well as far as the
best-known heuristic algorithms for the problem in the literature.

Anahtar Kelimeler

Assembly line balancing, Teaching-learning based optimization algorithm, Hybrid teaching-learning based optimization algorithm, Two-sided assembly lines, Mixed-model assembly lines

Kaynakça

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