Volume 27, Number 2 (IJIEPR 2016)                   IJIEPR 2016, 27(2): 167-178 | Back to browse issues page

DOI: 10.22068/ijiepr.27.2.167

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Noroozi A, Molla-Alizadeh-Zavardehi S, Mokhtari H. A fixed and flexible maintenance operations planning optimization in a parallel batch machines manufacturing system. IJIEPR. 2016; 27 (2) :167-178
URL: http://ijiepr.iust.ac.ir/article-1-648-en.html

Head of Department of Industrial Engineering , mokhtari_ie@yahoo.com
Abstract:   (1053 Views)

Scheduling has become an attractive area for artificial intelligence researchers. On other hand, in today's real-world manufacturing systems, the importance of an efficient maintenance schedule program cannot be ignored because it plays an important role in the success of manufacturing facilities. A maintenance program may be considered as the heath care of manufacturing machines and equipments. It is required to effectively reduce wastes and have an efficient, continuous manufacturing operation. The cost of preventive maintenance is very small when it is compared to the cost of a major breakdown. However, most of manufacturers suffer from lack of a total maintenance plan for their crucial manufacturing systems. Hence, in this paper, we study a maintenance operations planning optimization on a realistic variant of parallel batch machines manufacturing system which considers non-identical parallel processing machines with non-identical job sizes and fixed/flexible maintenance operations. To reach an appropriate maintenance schedule, we propose solution frameworks based on an Artificial Immune Algorithm (AIA), as an intelligent decision making technique. We then introduce a new method to calculate the affinity value by using an adjustment rate. Finally, the performance of proposed methods are investigated. Computational experiments, for a wide range of test problems, are carried out in order to evaluate the performance of methods.

Full-Text [PDF 354 kb]   (243 Downloads)    
Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2015/06/9 | Accepted: 2016/06/22 | Published: 2016/12/6

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