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Control Limits (control + limit)
Selected AbstractsProduction to order and off-line inspection when the production process is partially observableNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 8 2007Abraham Grosfeld-Nir Abstract This study combines inspection and lot-sizing decisions. The issue is whether to INSPECT another unit or PRODUCE a new lot. A unit produced is either conforming or defective. Demand need to be satisfied in full, by conforming units only. The production process may switch from a "good" state to a "bad" state, at constant rate. The proportion of conforming units in the good state is higher than in the bad state. The true state is unobservable and can only be inferred from the quality of units inspected. We thus update, after each inspection, the probability that the unit, next candidate for inspection, was produced while the production process was in the good state. That "good-state-probability" is the basis for our decision to INSPECT or PRODUCE. We prove that the optimal policy has a simple form: INSPECT only if the good-state-probability exceeds a control limit. We provide a methodology to calculate the optimal lot size and the expected costs associated with INSPECT and PRODUCE. Surprisingly, we find that the control limit, as a function of the demand (and other problem parameters) is not necessarily monotone. Also, counter to intuition, it is possible that the optimal action is PRODUCE, after revealing a conforming unit. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007 [source] Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure processNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 6 2007Yarlin Kuo Abstract This paper finds the optimal integrated production schedule and preventive maintenance plan for a single machine exposed under a cumulative damage process, and investigates how the optimal preventive maintenance plan interacts with the optimal production schedule. The goal is to minimize the total tardiness. The optimal policy possesses the following properties: Under arbitrary maintenance plan when jobs have common processing time, and different due dates, the optimal production schedule is to order the jobs by earliest due date first rule; and when jobs have common due date and different processing times, the optimal production schedule is shortest processing time first. The optimal maintenance plan is of control limit type under any arbitrary production schedule when machine is exposed under a cumulative damage failure process. Numerical studies on the optimal maintenance control limit of the maintenance plan indicate that as the number of jobs to be scheduled increases, the effect of jobs due dates on the optimal maintenance control limit diminishes. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007 [source] SENSORY AND INSTRUMENTAL EVALUATION OF STRAWBERRY YOGURT COLORJOURNAL OF SENSORY STUDIES, Issue 1 2001ADRIANA GAMBARO ABSTRACT Eleven samples of strawberry yogurt prepared with different red color concentrations using Ponceau 4R (E-124) were evaluated by instrumental and sensory methods. Color intensity evaluation was carried out by a panel of eight assessors specifically trained to measure strawberry color in yogurt. Color acceptability was measured with 120 regular and frequent consumers of yogurt. Color was measured with a Minolta Chroma Meter CR-200b, obtaining parameters L*, a* and b*. Principal component analysis was performed on the instrumental variables. Regression models between the instrumental first principal component, red color concentration, sensory intensity, and acceptability allowed determining quality control limits for red color attribute. These limits may be controlled by selecting either instrumental or sensory methods, being the latter easy to implement and providing dependable results. [source] A Bootstrap Control Chart for Weibull PercentilesQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2006Michele D. Nichols Abstract The problem of detecting a shift of a percentile of a Weibull population in a process monitoring situation is considered. The parametric bootstrap method is used to establish lower and upper control limits for monitoring percentiles when process measurements have a Weibull distribution. Small percentiles are of importance when observing tensile strength and it is desirable to detect their downward shift. The performance of the proposed bootstrap percentile charts is considered based on computer simulations, and some comparisons are made with an existing Weibull percentile chart. The new bootstrap chart indicates a shift in the process percentile substantially quicker than the previously existing chart, while maintaining comparable average run lengths when the process is in control. An illustrative example concerning the tensile strength of carbon fibers is presented. Copyright © 2005 John Wiley & Sons, Ltd. [source] Designing Accurate Control Charts Based on the Geometric and Negative Binomial DistributionsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2005Neil C. Schwertman Abstract Attribute control charts are used effectively to monitor for process change. Their accuracy can be improved by judiciously selecting the sample size. The required sample sizes to achieve accuracy can be quite restrictive, especially when the nominal proportions of non-conforming units are quite small. The usual attribute control chart has a set sample size and the number of non-conforming units in the sample is plotted. If, instead of setting a specific sample size the number of non-conforming units is set, an alternative monitoring process is possible. Specifically, the cumulative count of conforming (CCC- r) control chart is a plot of the number of units that must be tested to find the rth non-conforming unit. These charts, based on the geometric and negative binomial distributions, are often suggested for monitoring very high quality processes. However, they can also be used very efficiently to monitor processes of lesser quality. This procedure has the potential to find process deterioration more quickly and efficiently. Xie et al. (Journal of Quality and Reliability Management 1999; 16(2):148,157) provided tables of control limits for CCC- r charts for but focused mainly on high-quality processes and the tables do not include any assessments of the risk of a false alarm or the reliability of detecting process change. In this paper, these tables are expanded for processes of lesser quality and include such assessments using the number of expected monitoring periods (average run lengths (ARLs)) to detect process change. Also included is an assessment of the risk of a false alarm, that is, a false indication of process deterioration. Such assessments were not included by Xie et al. but are essential for the quality engineer to make sound decisions. Furthermore, a hybrid of the control charts based on the binomial, geometric and negative binomial distributions is proposed to monitor for process change. Copyright © 2005 John Wiley & Sons, Ltd. [source] Model Inadequacy and Residuals Control Charts for Autocorrelated ProcessesQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2005Murat Caner Testik Abstract As a result of time series parameter estimation based on previous data, the probability content of residuals control charts may vary when standard control limits are used. In this paper, we consider the AR(1) process with the autoregressive parameter being estimated from a sample of observations. The performance of the exponentially weighted moving average (EWMA) control chart for residuals is investigated. Modified control limits that account for the uncertainty in the parameter estimate are provided. Comparisons through simulation signify the importance of the modified control limits. Copyright © 2004 John Wiley & Sons, Ltd. [source] Control charts: a cost-optimization approach for processes with random shiftsAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2004András Zempléni Abstract In this paper we describe an approach for establishing control limits and sampling times which derives from economic performance criteria and a model for random shifts. The total cost related to both production and control is calculated, based on cost estimates for false alarms, for not identifying a true out of control situation, and for obtaining a data record through sampling. We describe the complete process for applying the method and compare with conventional procedures to real data from a Portuguese pulp and paper industrial plant. It turns out that substantial cost-reductions may be obtained. Copyright © 2004 John Wiley & Sons, Ltd. [source] Effect of corazonin and crustacean cardioactive peptide on heartbeat in the adult American cockroach (Periplaneta americana)ARCHIVES OF INSECT BIOCHEMISTRY AND PHYSIOLOGY (ELECTRONIC), Issue 2 2006Karel Sláma Abstract Changes in the frequency of cardiac pulsations have been monitored in the decapitated body of adult P. americana before and 5 h after the injections of [Arg7]-corazonin and CCAP, using newly invented touch-free, noninvasive optocardiographic methods. Relatively large dosages of these peptides (10,6 M concentrations in the body) had no effect on the rate of the heartbeat beyond the Ringer control limits. It has been concluded, therefore, that Corazonin and CCAP, which are currently cited in the literature as "the most potent cardiostimulating peptides" in insects, have no effect on the physiological regulation of cardiac functions in the living body. Arch Insect Biochem Physiol 62:91,103, 2006. © 2006 Wiley-Liss, Inc. [source] | |||||||||||||