طراحی طرح سیستم تولید تک ردیفه و چند ردیفه ی انعطاف پذیر توسط الگوریتم ژنتیک
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|15995||2004||9 صفحه PDF||سفارش دهید||5120 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Materials Processing Technology, Volumes 157–158, 20 December 2004, Pages 150–158
The paper presents a model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application made and the analysis are discussed.
Layout of flexible manufacturing system (FMS) involves distributing different resources in a given FMS and achieving maximum efficiency of the services offered. With this in mind FMS are designed to optimize production flow from the first stages as raw material to finished product. The layout has an important impact on the production time and cost, especially in the case of large FMS . It was estimated that 20–50% of the manufacturing costs are due to handling of work pieces; by a good arrangement of devices it is possible to reduce the manufacturing costs for 10–30% . Some other authors report even higher percentage of material handling based costs, for example Chiang and Kouvelis report that 30–70% of total manufacturing costs may be attributed to materials handling and layout . Therefore, already in an early stage of designing of the FMS it is necessary to have an idea of the layout of the devices. Usually the selected fitness function is the minimum total costs of handling of work pieces. In general, those costs are the sum of the transport costs (these are proportional to the intensity of the flow and distances) and other costs. Section 1 of the paper presents the problem and the aim of designing the FMS. Section 2 introduces the FMS and its specific properties with respect to transport and design. Section 3 makes survey of researches in the area of designing the FMS. Section 4 briefs the reader on the genetic algorithms (GA) method used in our work. Section 6 gives detailed information about the method itself of searching for solution by GA and the evolutionary and genetic operators used. Section 7 summarizes the results obtained by the model. The discussion and the concluding findings follow.
نتیجه گیری انگلیسی
By means of the presented model we can find the optimum layout of the devices in the FMS. The model searches for the optimum layout in rows and finds itself the optimum numbers of rows. The layout can be either the layout in single row or multiple rows. The model does not limit itself to one solution only, but it can propose several equally good solutions which can differ very much. From the solutions reached, having similar values of the fitness functions, the rules for designing our FMS by taking into account the limitations not considered in our fitness function can be set forth. It was also found out that the used coding of the organism was suitable for solving the problem of the FMS designing and that it was simple to implement. In future, the cost function of our model will be extended by other criteria (e.g., relations between the individual devices, shape of available area, I/O points, etc.). Also, the parameters of the length of the row of devices could be included in the GA. Thus, only the greatest and the smallest length of the row would be determined, whereas the GA would automatically select also the row length and would in that way determine also the optimal length of the row of devices through evolution.