گروه بندی اقدامات بهره وری و تاثیر آنها بر اقدامات کارخانه برای تشکیل مسئله اجزا ماشین: یک مطالعه شبیه سازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|9820||2007||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence,, Volume 20, Issue 1, February 2007, Pages 63-78
Over the past 25 years, the machine-part cell formation problem has been the subject of numerous studies. Researchers have applied various methodologies to the problem in an effort to determine optimal clusterings of machines and optimal groupings of parts into families. The quality of these machine and part groupings have been evaluated using various objective functions, including grouping efficacy, grouping index, grouping capability index, and doubly weighted grouping efficiency, among others. In this study, we investigate how appropriate these grouping quality measures are in determining cell formations that optimize factory performance. Through the application of a grouping genetic algorithm, we determine machine/part cell formations for several problems from the literature. These cell formations are then simulated to determine their impact on various factory measures, such as flow time, wait time, throughput, and machine utilization, among others. Results indicate that it is not always the case that a “more efficient” machine/part cell formation leads to significant changes or improvements in factory measures over a “less efficient” cell formation. In other words, although researchers are working to optimize cell formations using efficiency measures, cells formed this way do not always demonstrate optimized factory measures.
One of the decisions that must be made at a cellular manufacturing facility is how to separate machines into groups and parts into families in order to form efficient cells. The area of research that deals with this problem is known as group technology. Specifically, the machine-part cell formation (MPCF) problem addresses issues concerning the creation of part families based on each part's processing requirements, and the construction of machine groups based on each machine's ability to process particular part families. The efficiency of a cell formation is based on how many parts must leave their assigned cell in order to be processed, and on the machine utilization within each cell. Research has shown that manipulation of a problem's given machine-part (MP) matrix into a block diagonal form is a key component in creating efficient cells. In order to evaluate and compare the quality of multiple solutions to an MPCF problem, a measure of solution quality must be selected. Numerous such measures have appeared in the literature. Among these are grouping efficiency (Chandrasekharan and Rajagopalan, 1989), grouping efficacy (Kumar and Chandrasekharan, 1990), grouping index (Nair and Narendran, 1996), grouping capability index (Hsu, 1990), and doubly weighted grouping efficiency (Sarker, 2001). Each of these measures uses the block diagonal form of the given MP matrix that results from arranging the rows and columns based on a particular solution for an MPCF problem. In general, researchers agree that high-quality solutions arise when the constructed cells of the solution result in a block diagonal matrix containing minimal voids (zeros in the diagonal blocks) and minimal exceptions (ones outside of the diagonal blocks). From a practical standpoint, floor managers of a cellular manufacturing facility are probably not interested in the efficiency score of the cells based on the measures given above. They are more interested in measures that evaluate how well the plant is performing, such as flow time, wait time, throughput, machine utilization, etc. The focus of this research is to examine how well the cells formed using the “efficiency measures”, such as grouping efficacy, grouping index, grouping capability index, or doubly weighted grouping efficiency actually perform when evaluated using “factory measures” such as flow time, wait time, throughput, and machine utilization. Our simulation results indicate that it is not always the case that improving a particular problem's efficiency score leads to significantly different values for factory measures. In fact, there are even instances when cell formations with higher efficiency scores, when simulated, result in factory measures inferior to the factory measures for cell formations with lower efficiency scores. This paper is organized as follows. Section 2 provides background on the MPCF problem and a review of the quality measures used to evaluate MPCF solutions. Section 3 describes the solution technique applied in our research. In Section 4, we describe our simulation study and the factory measures it incorporates. In Section 5 we provide an analysis of our results. Conclusions are presented in Section 6.