An Assorted Design for Joint Monitoring of Process Parameters: An Efficient Approach for Fuel Consumption

Due to high fuel consumption, we face the problem of not only the increased cost, but it also affects greenhouse gas emission. This paper presents an assorted approach for monitoring fuel consumption in trucks with the objective to minimize fuel consumption. We propose a control charting structure for joint monitoring of mean and dispersion parameters based on the well-known max approach. The proposed joint assorted chart is evaluated through various performance measures such as average run length, extra quadratic loss, performance comparison index, and relative average run length. The comparison of the proposed chart is carried out with existing control charts, including a combination of X and S, the maximum exponentially weighted moving average (Max-EWMA), combined mixed exponentially weighted moving average-cumulative sum (CMEC), maximum double exponentially weighted average (MDEWMA), and combined mixed double EWMA-CUSUM (CMDEC) charts. The implementation of the proposed chart is presented using real data regarding the monitoring of fuel consumption in trucks. The outcomes revealed that the joint assorted chart is very efficient to detect different kinds of shifts in process behaviors and has superior performance than its competitor charts.Deanship of Scientific Research, King Saud University, King Fahd University of Petroleum and MineralsScopu

Univariate and multivariate linear profiles using max type extended exponentially weighted moving average schemes

Many studies have shown that industrial as well as non-industrial business organizations present a growing need of robust and more efficient multivariate monitoring schemes in order to be able to monitor several quality characteristics simultaneous. To monitor two or more parameters simultaneously, several monitoring schemes are used concurrently in most of the cases instead of using a single scheme. Thus, in this paper, the exponentially weighted moving average (EWMA), double EWMA (DEWMA) and the recent triple EWMA (TEWMA) procedures are used to develop new single univariate and multivariate Max-type monitoring schemes for linear profiles under the assumptions of fixed and random linear models to monitor the regression parameters and variance error simultaneously. It is observed that the newly proposed schemes are better alternatives of the classical univariate and multivariate EWMA, DEWMA and TEWMA schemes for linear profiles in terms of the average run-length (ARL) and expected ARL profiles. Numerical examples are presented using simulated and real-life data.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Statistic