Volume 15, Issue 1 (4-2018)                   2018, 15(1): 57-77 | Back to browse issues page

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Fattahi P, Askary A, Askary B. Mixed-model assembly line sequencing under uncertainty with stochastic operation times using improved harmony search algorithm . Journal of Operational Research and Its Applications. 2018; 15 (1) :57-77
URL: http://jamlu.liau.ac.ir/article-1-1510-en.html
Associate Professor, Alzahra University, Department of Industrial Engineering, Tehran
Abstract:   (1450 Views)

In recent years, with the high-paced development of technology, diversity seeking of the customers, and the rise of global rivalry, the traditional systems are not sufficient anymore to respond the demands of the customers. In order to supply the requests of the customers, overtaking the rivals, and a longer survival in the area of production, the companies need new production systems so that while increasing the quality of their products, the costs of production as well as the prices of the products shall be decreased. A new production system is called mixed model assembly line (MMAL). Mixed-model assembly lines are a type of production line where a variety of product models with common base characteristics are assemble on it. In this paper we intend to study these kinds of lines with the goal of minimizing the total utility work cost and total idle cost in conditions in which operation time is considered as a stochastic parameter. For having a stochastic modeling, chance constraint approach has been applied. First, we use GAMS software to check the feasibility of the model, then because of NP-hardness nature of the problem, improved harmony search (IHS) algorithm is proposed to solve it. Finally, having an attention to the results, it can be deduced in comparison with the exact answer from the Gams software, the results which were got from IHS algorithm have trivial calculation mistakes and present acceptable responses that signify the efficiency of the algorithm.

Full-Text [PDF 480 kb]   (355 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/05/31 | Accepted: 2017/11/21 | Published: 2018/03/11

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