Optimization of multiple responses in the Taguchi method using fuzzy regression

Al–Refaie, Abbas

doi:10.1017/s0890060413000528

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…The output of the system can be indicated by one or more quality specifications. These quality characteristics are usually one of the following three cases (Zou et al, 2013;Al-Refaie et al, 2013;Cheng et al, 2013):…”

Section: Quality Loss Functionmentioning

confidence: 99%

Proposing a two-criterion quality loss function using critical process capability indices

Geramian

Shahin

Bandarrigian

et al. 2017

BIJ

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Purpose Average quadratic quality loss function (QQLF) measures quality of a given process using mean shift from its target value and variance. While it has a target parameter for the mean, it lacks a target for the variance revisable for counting any progress of the process across different quality levels, above/below the standard level; thus, it appears too general. Hence, in this research, it was initially supposed that all processes are located at two possible quality spaces, above/below the standard level. The purpose of this paper is to propose a two-criterion QQLF, in which each criterion is specifically proper to one of the quality spaces. Design/methodology/approach Since 1.33 is a literarily standard or satisfactory value for two most important process capability indices Cp and Cpk, its upper/lower spaces are assumed as high-/low-quality spaces. Then the indices are integrated into traditional QQLF, of type nominal the best (NTB), to develop a two-criterion QQLF, in which each criterion is more suitable for each quality space. These two criteria have also been innovatively embedded in the plan-do-check-act (PDCA) cycle to help continuous improvement. Finally, the proposed function has been examined in comparison with the traditional one in Feiz Hospital in the province of Isfahan, Iran. Findings Results indicate that the internal process of the studied case is placed on the lower quality space. So the first criterion of revised QQLF gives a more relevant evaluation for that process, compared with the traditional function. Moreover, this study has embedded both proposed criteria in the PDCA cycle as well. Research limitations/implications Formulating the two-criterion QQLF only for observations of normal and symmetric distributions, and offering it solely for NTB characteristics are limitations of this study. Practical implications Two more relevant quality loss criteria have been formulated for each process (service or manufacturing). However, in order to show the comprehensiveness of the proposed method even in service institutes, emergency function of Feiz Hospital has been examined. Originality/value The traditional loss function of type NTB merely and implicitly targets zero defect for variance. In fact, it calculates quality loss of all processes placed on different quality spaces using a same measure. This study, however, provides a practitioner with opportunity of targeting excellent or satisfactory targets.

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Section: Quality Loss Functionmentioning

confidence: 99%

Proposing a two-criterion quality loss function using critical process capability indices

Geramian

Shahin

Bandarrigian

et al. 2017

BIJ

View full text Add to dashboard Cite

show abstract

“…In contrast, the fuzzy goal programming (FGP) is used when such values are not known precisely. [17][18][19][20][21][22][23][24][25][26][27][28][29] Therefore, the FGP approach will be used to define the combination of optimal factor settings of vegetable oil filling process that reduces the number of defective cans and enhances the production rate. The remaining of this research is outlined in the following sequence.…”

Section: Introductionmentioning

confidence: 99%

Optimizing performance of oil filling processes using fuzzy goal programming

Al–Refaie

Ghazaleh

2017

Proceedings of the Institution of Mechanical Engineers, Part E:

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This research aims at improving the performance of the filling line machine using fuzzy goal programming. Two main quality responses including the number of defective products and the production rate of the filling machine are of main interest. Initially, the control charts for number of nonconforming and production rate, np and I-MR, respectively, are established and indicate that the process is in statistical control. However, the process is found incapable. The fuzzy goal programming model is adopted to identify the combination of optimal factor settings utilizing the Taguchi's L 16 array. The optimal factor settings are 5.

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“…In practice, optimizing performance under fuzzy conditions becomes a real challenge to decision makers (Al-Refaie, Chen, & Al-Athamenh, 2016; Al-Refaie, Aldwairi, et al, 2017). Fuzzy goal programing has been found effective in dealing with uncertainty (Al-Refaie, 2013, 2015 a , 2015 b ). It has been applied to optimize performance in several business applications (Al-Refaie, 2014 a , 2014 b , 2014 c ; Al-Refaie et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimal fuzzy scheduling and sequencing of multiple-period operating room

Al–Refaie

Judeh

2017

AIEDAM

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Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

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Optimization of multiple responses in the Taguchi method using fuzzy regression

Cited by 10 publications

References 18 publications

Proposing a two-criterion quality loss function using critical process capability indices

Proposing a two-criterion quality loss function using critical process capability indices

Optimizing performance of oil filling processes using fuzzy goal programming

Optimal fuzzy scheduling and sequencing of multiple-period operating room

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