حل مشکلات برنامه ریزی توزیع شده FMS و موضوع تعمیر و نگهداری:روش الگوریتم ژنتیکی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|16050||2006||12 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Robotics and Computer-Integrated Manufacturing, Volume 22, Issues 5–6, October–December 2006, Pages 493–504
In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.
The significance of distributed scheduling (DS) has been recognized by many researchers and industrialists in recent years because of the changes in the mode of today's production environment. Single factory production in traditional manufacturing has been gradually replaced by multi-factory production due to the trend of globalization. These factories may be geographically distributed in different locations, which allow them to be closer to their customers, to comply with the local laws, to focus on a few product types, to produce and market their products more effectively, and to be responsive to market changes more quickly  and . Each factory is usually capable of manufacturing a variety of product types. Some may be unique in a particular factory, while some may not. In addition, they may have different production efficiency and various constraints depending on the machines, labor skills and education levels, labor cost, government policy, tax, nearby suppliers, transportation facilities, etc. This induces different operating costs, production lead time, customer service levels, etc. in different factories , ,  and .
نتیجه گیری انگلیسی
In conclusion, this paper proposed a Genetic Algorithm with Dominant Genes (GADG) approach to solve distributed FMS scheduling problem subject to machine maintenance. The idea of Dominant Genes (DGs) and various genetic operators including selection, crossover, and mutation have been presented. A Simple Genetic Algorithm (SGA) approach has been compared with Petri Nets  and Ant Colony  in Lee and DiCesare's Model to testify its performance. The optimization results indicate that SGA performs better. However, when the problem size increases, the deviation of the solutions obtained becomes larger. To overcome the problem, DGs is applied. In traditional crossover mechanism, a number of genes will be randomly selected, governed by a predefined crossover rate(s). However, it is usually difficult to ensure that the important part of the chromosome structure can be selected and inherited to its offspring. In the new approach, the proposed DGs identify and record the best genes in each chromosome, and the corresponding structure. The results obtained by GADG have been compared with the ones obtained by SGA. The comparison indicates that GADG improves the quality of the solution, and reduces the deviation of the results obtained. Lastly, GADG has been modified to consider machine maintenance. Two sets of simulation runs have been carried out. Set 1 does not consider maintenance, while Set 2 does. The comparison of the results demonstrates that considering maintenance during DS can shorten the makespan by improving the machine utilization. The average makespan obtained by Set 2 is shorter. In addition, the machine utilization is improved as well.