تجزیه و تحلیل پویا ناشی از تاریخ در یک سیستم تولید انعطاف پذیر
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|16207||2011||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Manufacturing Systems, Volume 30, Issue 1, January 2011, Pages 28–40
This paper investigates the effects of dynamic due-date assignment models (DDDAMs), routing flexibility levels (RFLs), sequencing flexibility levels (SFLs) and part sequencing rules (PSRs) on the performance of a flexible manufacturing system (FMS) for the situation wherein part types to be produced in the system arrive continuously in a random manner. The existing DDDAMs considered are dynamic processing plus waiting time and dynamic total work content. A new model known as dynamically estimated flow allowance (DEFA) has also been developed and investigated. The routing flexibility of the system and the sequencing flexibility of parts are both set at three levels. A discrete-event simulation model of the FMS is used as a test-bed for experimentation. The performance measures evaluated are mean flow time, mean tardiness, percentage of tardy parts and mean flow allowance. The statistical analysis of the simulation results reveals that there are significant interactions among DDDAMs, RFLs, SFLs and PSRs for all the performance measures. The proposed DEFA model provides the minimum percentage of tardy parts in all the experiments. Regression-based metamodels have been developed using the simulation results. The metamodels are found to provide a good prediction of the performance of the FMS within the domain of their definition.
With the globalization of manufacturing, there has been a renewed interest in the competitiveness of the manufacturing sector throughout the world. There is an increasing trend towards higher product variety, smaller lot sizes and shorter lead times in the market place. In this environment, manufacturing companies are forced to implement systems that can provide flexibility and efficiency . Emergence of flexible manufacturing systems is an important development in this direction. MacCarthy and Liu  state that a flexible manufacturing system (FMS) is a production system in which groups of numerically controlled or computer numerically controlled machine tools and an automated Material Handling System (MHS) work together under computer control. Stecke  identifies four hierarchical levels of decision problems in FMSs, i.e. design, planning, scheduling and control. Scheduling decision problems of FMSs continue to attract the interest of both the academic and industrial sectors . This can be attributed to the fact that these problems have fundamental implications on the overall performance of the system. Proper scheduling procedures are essential for the efficient utilization of the expensive resources in FMSs such as machines and MHS and for improving the responsiveness of the system in meeting the changing customer needs. Smith  and Lee et al.  provide a review of simulation-based research on manufacturing system design and operation problems. Hwang and Kim , Merchawi and ElMaraghy , Son et al. , Sinreich and Shnits , and Um et al.  adopt simulation for scheduling FMSs.
نتیجه گیری انگلیسی
In this research study, the analysis of the performance of an FMS under different DDDAMs, RFLs, and SFLs together with PSRs has been carried out using a discrete-event simulation model. Two existing DDDAMS and one proposed DDDAM have been investigated. The part sequencing rules considered for experimentation include processing time-based rules and due-date based rules such as SIO, CRSPT, and EODD in addition to the FIFO rule. The simulation output has been suitably subjected to steady state analysis to ensure that further investigations are free from initial bias. The statistical analyses of the simulation results reveal that there are significant interactions among DDDAMs, RFLs, SFLs and PSRs for all the performance measures. The top three best performing DDDAM–PSR combinations for various performance measures are determined as follows: