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

• Main Author: Alper Hamzadayı
• Format: Article
• Language: English
• Published: Pamukkale University 2018-08-01
• Series: Pamukkale University Journal of Engineering Sciences
• Subjects: assembly line balancing; teaching-learning based optimization algorithm; hybrid teaching-learning based optimization algorithm; two-sided assembly lines; mixed-model assembly lines; montaj hattı dengeleme; öğretme-öğrenme-tabanlı eniyileme algoritması; melez öğretme-öğrenme-tabanlı eniyileme algoritması; i̇ki yönlü montaj hatları; karışık modelli montaj hatları
• Online Access: https://dergipark.org.tr/tr/pub/pajes/issue/38891/454095
• ISSN: 1300-7009; 2147-5881

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.