Pamukkale Univ Muh Bilim Derg. 2018; 24(4): 682-691 | DOI: 10.5505/pajes.2017.14227

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

Alper Hamzadayi
Van Yuzuncu Yil University, Enginering Faculty, Department Of Industrial Engineering, 65080, Van / Turkey

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

---

Öðretme-Öðrenme algoritmasýný kullanarak iki yönlü karýþýk modelli montaj hattý dengeleme

Alper Hamzadayi
Van Yüzüncü Yýl Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliði Bölümü, 65080, Van / Türkiye

Öðretme-Öðrenme-Tabanlý Eniyileme (ÖÖTE) algoritmasý, diðer popülasyon-tabanlý algoritmalar kadar etkin olduðu ortaya konmuþ, popülasyon-tabanlý bir eniyileme algoritmasýdýr. Bu makalenin temel amacý, ÖÖTE algoritmasýný kullanarak iki yönlü karýþýk modelli montaj hattý dengeleme problemini ilk defa çözmektir. Yakýn zamanda, stokastik iki yönlü tek modelli montaj hattý dengeleme problemini çözmek için [1]’de melez öðretme-öðrenme-tabanlý eniyileme (MÖÖTE) algoritmasý önerilmiþtir. [1]’de MÖÖTE algoritmasý en iyi bilinen 10 farklý meta-sezgisel algoritma ile karþýlaþtýrýlmýþtýr. Yapýlan testler MÖÖTE algoritmasýnýn diðer algoritmalara göre daha üstün bir performans sergilediðini ortaya koymuþtur. Bu makalede ayrýca, MÖÖTE algoritmasý iki yönlü karýþýk modelli montaj hattý dengeleme problemini çözmek için adapte edilmiþ ve algoritmanýn performansý test edilmiþtir. Bu çalýþmanýn amacý önceden tanýmlanmýþ çevrim süresinde karþýlýklý eþleþen istasyon sayýsýný ve toplam istasyon sayýsýný en aza indirmektir. Literatürden alýnan test problem gruplarý üzerinden kapsamlý bir deneysel çalýþma gerçekleþtirilmiþtir ve algoritmalarýn performanslarý var olan yaklaþýmlarla karþýlaþtýrýlmýþtýr. Deneysel çalýþmalar ÖÖTE algoritmasýnýn karþýlaþtýrýlan diðer en iyi bilinen sezgisel algoritmalara karþý göze çarpan bir potansiyele sahip olduðunu ve problemin çözümünde MÖÖTE algoritmasýnýn bilinen en iyi sezgisel algoritmalar kadar iyi performans sergilediðini ortaya koymuþtur.

Anahtar Kelimeler: Montaj hattý dengeleme, Öðretme-öðrenme-tabanlý eniyileme algoritmasý, Melez öðretme-öðrenme-tabanlý eniyileme algoritmasý, Ýki yönlü montaj hatlarý, Karýþýk modelli montaj hatlarý

---

CITE