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

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

Abstract

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.

Keywords

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

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