Modern Manufacturing Engineering ›› 2017, Vol. 440 ›› Issue (5): 17-21.doi: 10.16731/j.cnki.1671-3133.2017.05.004

Previous Articles     Next Articles

A teaching-learning-based optimization algorithm for mixed-model two-sided assembly line balancing problem

Rao Di, Tang Qiuhua, Zhang Liping, Zheng Caifu   

  1. College of Machinery and Automation,Wuhan University of Science and Technology,Wuhan 430081,China
  • Received:2016-02-19 Online:2017-05-20 Published:2018-01-08

Abstract: To solve mixed-model two-sided assembly line balancing problems type Ι effectively,an Improved Teaching-Learning-Based Optimization(ITLBO) algorithm is proposed.In this algorithm,the random key method is used to generate initial solutions,and then a new self-learning phase is introduced to strengthen the local search ability of the proposed algorithm.In addition,according to the characteristics of the mixed-model two-sided assembly line,a novel heuristic decoding method is put forward.The decoding method tires to reduce the sequence-dependent idle times in the decoding process,balances the workloads on two stations within a same mated-station to ensure the workload balance,and also puts forward a new strategy to deal with the last mated-station for the purpose of further reducing the number of stations.The proposed algorithm solves all the benchmark problems of the mixed-model two-sided assembly line,and it is compared with six different algorithms.Computational results show that the proposed decoding reduces idle times effectively,and the proposed algorithm is superior to other algorithms.

Key words: mixed-model two-sided assembly line, assembly balancing, teaching-learning-based optimization algorithm, heuristic decoding scheme

CLC Number: 

Copyright © Modern Manufacturing Engineering, All Rights Reserved.
Tel: 010-67126028 E-mail: 2645173083@qq.com
Powered by Beijing Magtech Co. Ltd