نمودار کنترل نوع میانگین متحرک
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نمودار کنترل نوع میانگین متحرک

عنوان فارسی مقاله: نمودار کنترل نوع میانگین متحرک وزنی نمایی تطبیقی برای نظارت بر میانگین فرآیند
عنوان انگلیسی مقاله: An adaptive exponentially weighted moving average-type control chart to monitor the process mean
مجله/کنفرانس: مجله اروپایی درباره تحقیقات عملیاتی – European Journal of Operational Research
رشته های تحصیلی مرتبط: آمار
گرایش های تحصیلی مرتبط: آمار ریاضی
کلمات کلیدی فارسی: کنترل کیفیت، نمودار کنترل تطبیقی، میانگین متحرک وزنی نمایی، نمودار کنترل، طول متوسط اجرا
کلمات کلیدی انگلیسی: Quality control، Adaptive control chart، Exponentially weighted moving average، Control chart، Average run length
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ejor.2019.07.002
دانشگاه: Department of Systems & Technology, Harbert College of Business, Auburn University, Auburn, AL, 36849-5266, United States
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.712 در سال 2018
شاخص H_index: 226 در سال 2019
شاخص SJR: 2.205 در سال 2018
شناسه ISSN: 0377-2217
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13528
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. Adaptive EWMA chart


3. Proposed adaptive control chart


4. Performance of the proposed adaptive control chart


5. Conclusions


Acknowledgment


Declarations of interest


Reference

نمونه متن انگلیسی مقاله

Abstract


Exponentially weighted moving average (EWMA) control charts are typically used for faster detection of shifts in the process mean, relative to a Shewhart control chart, when the degree of shift is small. Normal guidelines suggest using a small (large) value of the weighting constant, λ, for detecting smaller (larger) shifts in the process mean. Prior research has suggested that the choice of λ should depend on the observed data and have considered the use of a weighting constant, that varies and adapts as monitoring continues and new data are collected. One such adaptive control chart, called the AEWMA chart, utilizes a rather computationally complex scheme to determine the weighting constant λ and it requires knowledge of the size of the shift, to specify whether it is “small” or “large”. A complex twophase optimization scheme is then solved to yield “good solutions”. As an alternative, we propose an adaptive EWMA-type control chart that does not require knowledge of the degree of the shift. Further, the computational scheme is easier and completed in one stage. The performance of the proposed chart is studied using simulations, where the degree of the shift in the process mean is varied over a wide range of values. Based on the average run length (ARL), as a performance measure, the proposed chart is demonstrated to perform uniformly better than the traditional EWMA chart with a constant weight. The proposed chart also performs better than the AEWMA chart for moderate to large shifts.


Introduction


The traditional Shewhart control chart technique, introduced in 1924 by Walter Shewhart, has been used in a variety of manufacturing and service industries. The commonly used control charts are those based on the sample mean (X¯), the range (R), the standard deviation (s), individual data (X) and the moving range (MR). In the first two situations (X¯ and R, X¯ and s), it is assumed that independent and identically distributed observations (X) are selected from a process, in subgroups, where the subgroup size is denoted by n. In the third situation, the subgroup size is 1 and the process is monitored based on individual observations (Mitra, 2016). The popular Shewhart control charts, while easy to implement, have the disadvantage that for small shifts in the process parameters, for example, the mean, it takes a rather long time to detect the shift. Consequently, control charts have been developed with the purpose of faster detection of small changes in the process parameters. These include the cumulative sum (CUSUM) chart and the exponentially-weighted moving average (EWMA) control chart (Albin, Kang, & Shea, 1997; Chen & Elsayed, 2002; Domangue & Patch, 1991; Klein, 1996; Lucas & Saccucci, 1990; Roberts, 1959). The traditional EWMA control chart is based on the monitoring statistic.

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