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  • 1.
    Dehlendorff, Christian
    et al.
    Informatics and Mathematical Modelling, Section for Statistics, Lyngby, Technical University of Denmark, Department of DTU Informatics, DTU Data Analysis, Technical University of Denmark, Technical University of Denmark.
    Kulahci, Murat
    Informatics and Mathematical Modelling, Section for Statistics, Lyngby, Technical University of Denmark.
    Merser, Sören
    Frederiksberg University Hospital, Clinic of Orthopaedic Surgery.
    Andersen, Klaus Kaae Aae
    Technical University of Denmark, Lyngby.
    Conditional Value at Risk as a Measure for Waiting Time in Simulations of Hospital Units2010In: Quality Technology & Quantitative Management, ISSN 1684-3703, E-ISSN 1811-4857, Vol. 7, no 3, p. 321-336Article in journal (Refereed)
    Abstract [en]

    The utility of conditional value at risk (CVaR) of a sample of waiting times as a measure for reducing long waiting times is evaluated with special focus on patient waiting times in a hospital. CVaR is the average of the longest waiting times, i.e., a measure at the tail of the waiting time distribution. The presented results are based on a discrete event simulation (DES) model of an orthopedic surgical unit at a university hospital in Denmark. Our analysis shows that CVaR offers a highly reliable performance measure. The measure targets the longest waiting times and these are generally accepted to be the most problematic from the points of view of both the patients and the management. Moreover, CVaR can be seen as a compromise between the well known measures: average waiting time and the maximum waiting time

  • 2.
    Soltanali, Hamzeh
    et al.
    Ferdowsi University of Mashhad, Mashhad, Iran.
    Rohani, Abbas
    Ferdowsi University of Mashhad, Mashhad, Iran.
    Tabasizadeh, Mohammad
    Ferdowsi University of Mashhad, Mashhad, Iran.
    Hossein Abbaspour-Fard, Mohammad
    Ferdowsi University of Mashhad, Mashhad, Iran.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Operational reliability evaluation-based maintenance planning for automotive production line2019In: Quality Technology & Quantitative Management, ISSN 1684-3703, E-ISSN 1811-4857Article in journal (Refereed)
    Abstract [en]

    Reliability evaluation plays a critical role in upgrading the availability and productivity of automotive manufacturing industries by adopting the well-planned maintenance. Due to the lack of operation management studies in automotive industry, this paper addresses an operational reliability evaluation through failure behavior trend in an automotive production line. The main approaches for reliability analysis in this study include statistical structure and Monte Carlo simulation model. The statistical structure consists of three steps: data acquisition and homogenization process, validity of the trend hypothesis and parameters estimation. The reliability evaluation under statistical approach identified the main bottlenecks through the recognized behavior trend of system so that needs to be considered as a priority. Besides, K–R algorithm as Monte Carlo simulation was carried out to simulate reliability regarding failure distribution function. The result of Monte Carlo simulation with different iterations provides a high prediction accuracy of reliability with the lowest error. In addition, regarding the computed reliability through the proposed approaches and total expected cost, a reliability-based maintenance optimization model was conducted. The proposed maintenance intervals could be useful for improving the operational performance of critical components in automotive system.

  • 3.
    Tyssedal, John Sølve
    et al.
    Department of Mathematical Sciences, The Norwegian University of Science and Technology, Trondheim.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Experiments for multi-stage processes2015In: Quality Technology & Quantitative Management, ISSN 1684-3703, E-ISSN 1811-4857, Vol. 12, no 1, p. 13-28Article in journal (Refereed)
    Abstract [en]

    Multi-stage processes are very common in both process and manufacturing industries. In this article we present a methodology for designing experiments for multi-stage processes. Typically in these situations the design is expected to involve many factors from different stages. To minimize the required number of experimental runs, we suggest using mirror image pairs of experiments at each stage following the first. As the design criterion, we consider their projectivity and mainly focus on projectivity P ≥ 3 designs. We provide the methodology for generating these designs for processes with any number of stages and also show how to identify and estimate the effects. Both regular and non-regular designs are considered as base designs in generating the overall design

  • 4.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Safety regions in process capability plots2006In: Quality Technology & Quantitative Management, ISSN 1684-3703, E-ISSN 1811-4857, Vol. 3, no 2, p. 227-246Article in journal (Refereed)
    Abstract [en]

    A process is usually defined to be capable if the process capability index exceeds a stated threshold value, e.g., Cpm > 4/3. This inequality can be expressed graphically as a region in the plane defined by the process parameters (,μσ). In the obtained plot special regions can be plotted to test for process capability. These regions are similar to confidence regions for (,μσ). This idea of using regions in process capability plots to assess the capability is developed further for the capability index Cpm. A new circular region is constructed that can be used, in a simple graphical way, to draw conclusions about the capability of the process at a given significance level. Using circular regions several characteristics with different specification limits and different sample sizes can be monitored in the same plot. Under the assumption of normality the suggested method is investigated with respect to power as well as compared to other existing graphical methods for drawing inference about process capability.

  • 5.
    Vännman, Kerstin
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Albing, Malin
    Process capability plots for one-sided specification limits2007In: Quality Technology & Quantitative Management, ISSN 1684-3703, E-ISSN 1811-4857, Vol. 4, no 4, p. 569-590Article in journal (Refereed)
    Abstract [en]

    We extend the idea of process capability plots from the case of two-sided specification intervals to derive a graphical method useful when doing capability analysis having one-sided specification limits. The derived process capability plots are based on existing capability indices for one-sided specification limits. Both the cases with and without a target value are investigated. Under the assumption of normality we suggest estimated process capability plots to be used to assess process capability at a given significance level. Theoretical results are given for determining the significance level as well as power for the method. The presented graphical approach is helpful to determine if it is the variability, the deviation from target, or both that need to be reduced to improve the capability. Examples are presented.

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