Browse ORBi by ORBi project

- Background
- Content
- Benefits and challenges
- Legal aspects
- Functions and services
- Team
- Help and tutorials

Evaluation of the Economic Statistical Design of the Multivariate T2 Control Chart with Multiple Variable Sampling Intervals Scheme: NSGA-II Approach Faraz, Alireza ; ; in Journal of Statistical Computation & Simulation (2015), 85(12), 2442-2455 The economic and statistical merits of a multiple variable sampling intervals (MVSI) scheme are studied. The problem is formulated as a double-objective optimization problem with the adjusted average time ... [more ▼] The economic and statistical merits of a multiple variable sampling intervals (MVSI) scheme are studied. The problem is formulated as a double-objective optimization problem with the adjusted average time to signal as the statistical objective and the expected cost per hour as the economic objective. Bai and Lee’s [2] economic model is considered. Then we find the Pareto-optimal designs in which the two objectives are minimized simultaneously by using the non-dominated sorting genetic algorithm. Through an illustrative example, the advantages of the proposed approach is shown by providing a list of viable optimal solutions and graphical representations, which indicate the advantage of flexibility and adaptability of our approach. [less ▲] Detailed reference viewed: 42 (4 ULg)Economic Statistical Design of the VP( X) ̅ Control Charts for Monitoring a Process under Non-normality ; Faraz, Alireza ; in International Journal of Production Research (2014), 53(14), 4218-4230 Recent studies proved that variable parameters (VP) X ̅ control charts not only detects process mean shifts quicker than the classical X ̅ control chart but also has better economic properties ... [more ▼] Recent studies proved that variable parameters (VP) X ̅ control charts not only detects process mean shifts quicker than the classical X ̅ control chart but also has better economic properties. Furthermore, like most papers in control chart design, the fundamental assumption is that process data are normally distributed. Nevertheless, process quality variables may not be normal in application. In this paper, we investigate the economic statistical design of the VP X ̅ control chart when the underlying process distribution is non-normal. We illustrate the design procedure and perform a sensitivity analysis on the process and cost parameters based upon the degrees of skewness and kurtosis of the population using an industrial application. [less ▲] Detailed reference viewed: 61 (12 ULg)The application of the NSGA-II optimization method in designing control charts Faraz, Alireza ; Heuchenne, Cédric ; Conference (2014, June 04) The problem of designing control chart is formulated as a multi-objective optimization problem with the adjusted average time to signal as the statistical objective and the expected cost per hour as the ... [more ▼] The problem of designing control chart is formulated as a multi-objective optimization problem with the adjusted average time to signal as the statistical objective and the expected cost per hour as the economic objective. Then we try to find the Pareto-optimal designs in which the two objectives are minimized simultaneously by using the elitist non-dominated sorting genetic algorithm method. Through an illustrative example, the advantages of the proposed approach is shown by providing a list of viable optimal solutions and graphical representations, thereby bolding the advantage of flexibility and adaptability. [less ▲] Detailed reference viewed: 33 (3 ULg)A modified economic-statistical design of the VP multivariate control charts ; Faraz, Alireza Conference (2012, January 15) Hotelling’s T2 control chart is one of the most popular multivariate control charts for monitoring multiple variables simultaneously. Recent studies have shown that applying the T2 control chart by using ... [more ▼] Hotelling’s T2 control chart is one of the most popular multivariate control charts for monitoring multiple variables simultaneously. Recent studies have shown that applying the T2 control chart by using a variable parameters (VP) scheme yields more rapid detection of assignable causes than the classical method of taking fixed sample sizes at fixed intervals of time. This paper presents an economic-statistical design (ESD) of the VP T2 control chart using the general model of Lorenzen and Vance. The genetic algorithm (GA) is then employed to search for the optimal values of the eight test parameters of the chart. Furthermore, VP and FRS schemes are compared with respect to the expect cost per unit time. [less ▲] Detailed reference viewed: 39 (3 ULg)Statistical Merits and Economic Evaluation of T2 Control Charts with the VSSC Scheme ; ; Faraz, Alireza et al in Arabian Journal for Science and Engineering (2011), 36(7), 1461-1470 T2 control charts are used to monitor a process when more than one quality variable associated with the process is being observed. Recent studies have shown that using variable sampling size (VSS) schemes ... [more ▼] T2 control charts are used to monitor a process when more than one quality variable associated with the process is being observed. Recent studies have shown that using variable sampling size (VSS) schemes results in charts with more statistical power for detecting small to moderate shifts in the process mean vector. This paper presents an economic-statistical design of T2 control charts with variable sample size and control limits (VSSC). We build a cost model of a VSSC T2 control chart for the purpose of economic-statistical design using the model of Costa and Rahim (J. Appl. Stat. 28:875–885, 2001). This cost model constructed involves the cost of false alarms, the cost of finding and eliminating an assignable cause, the cost associated with production in an out-of-control state, and the cost of sampling and testing. We optimize this model using a genetic algorithm approach. Furthermore, VSSC and VSS T2 charts are compared with respect to the expected cost per unit time. [less ▲] Detailed reference viewed: 75 (24 ULg) |
||