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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|16093||2008||14 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Advances in Engineering Software, Volume 39, Issue 3, March 2008, Pages 219–232
The advent of automated manufacturing systems and the variability in demand pattern have forced the manufacturers to increase the flexibility and efficiency of their automated systems to stay competitive in the dynamic market. Loading decisions play an important role in determining the efficiency of manufacturing systems. Machine loading problems in flexible manufacturing systems (FMSs) are known to be NP-hard problems. Although some NP-hard problems could still be optimized for very small instances, machine loading complexity is so extensive that even small problems take excessive computational time to reach the optimal solution. To ease the tedious computations, and to get a good solution for large problems, this paper develops a special Immune Algorithm (IA) named ‘Modified immune algorithm (MIA)’. IA is a suitable method due to its self learning capability and memory acquisition. This paper improves some issues inherent in existing IAs and proposes a more effective immune algorithm with reduced memory requirements and reduced computational complexity. In order to verify the efficacy and robustness of the proposed algorithm, the paper presents comparisons to existing immune algorithms with benchmark functions and standard data sets related to the machine loading problem. In addition proposed algorithm has been tested at different noise level to examine the efficiency of algorithm on different platforms. The comparisons show consistently that the proposed algorithm outperforms the existing techniques. For all machine loading dataset proposed algorithm has shown good results as compared to the best results reported in the literature.
Time effective production has become a key issue in manufacturing environments for striving in the hard global competitive markets. Loading decisions play an important role in time effective production by processing the job in a feasible sequencing schedule with the proper utilization of resources. Effective loading decisions are particularly important in the large, complex manufacturing systems encountered in high technology industries dealing with processing of customized products and many others, where, simple manual techniques are unlikely to yield good results . According to Stecke , machine loading problem is one of six post-release decisions of a flexible manufacturing system that is known for its computational complexity and high variability. Due to inherent generality of FMS, it becomes necessary to define the configuration of an FMS. A typical FMS unit includes 5–25 Numerical Control (NC) machines, a central storage system and an automated material handling system. A computer system is used to control the above components of FMS. In general, two types of operational decisions (pre-release decision and post-release decision) are associated with operations of FMS. Pre-arrangement of jobs and tools comes under the category of pre-release decision of FMS whereas routing and sequencing of jobs comes under post-release decision of FMS. Various post-release decisions of FMS include:
نتیجه گیری انگلیسی
Loading problem in a Flexible manufacturing system is well known for possessing a large variety of objectives and constraints. This paper presents a Modified immune algorithm (MIA) with advanced mutation strategy and elitist based immune memory that has been sufficiently able to resolve the loading problem by solving all the sub-problems of machine loading in an integrated manner. The proposed MIA enhances the applicability of traditional clonal algorithm by making some modifications in the operators. The paper has demonstrated the capabilities of the proposed algorithm to converge faster and to a better solution, than other comparable techniques. For the same cause, the proposed algorithm has been tested over a set of benchmark functions taken from the literature. The comparative results thus obtained are showing the supremacy and robustness of the proposed algorithm.