Development of a multi-attribute selection procedure for non-traditional machining processes

2003 ◽  
Vol 217 (7) ◽  
pp. 993-1009 ◽  
Author(s):  
M Yurdakul ◽  
C Cçogun
Keyword(s):  
Selection Procedure ◽  
Attribute Selection ◽  
Machining Processes

Comparison of Fuzzy and Crisp Versions of an AHP and TOPSIS Model for Nontraditional Manufacturing Process Ranking Decision

2019 ◽  
Vol 18 (02) ◽  
pp. 167-192 ◽  
Author(s):  
Mustafa Yurdakul ◽  
Yusuf Tansel İç
Keyword(s):  
Fuzzy Ahp ◽  
Level Structure ◽  
Selection Procedure ◽  
High Strength ◽  
Fine Tuning ◽  
Attribute Selection ◽  
Machining Processes ◽  
Ranking Model ◽  
Order Preference ◽  
Fuzzy Weights

Nontraditional manufacturing processes (NTMPs) are especially preferred when it is necessary to machine very small and delicate parts, obtain complex shapes or process very hard and high strength materials. New NTMPs are developed continually and the total number of NTMPs being used in the machining industry is increasing so that ranking and selection of the most proper NTMP requires multi-level and systematic models. [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] developed such an NTMP ranking model. The developed NTMP ranking model in [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] had a two-level structure and used crisp Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) together to rank feasible NTMPs. This study aims to replace crisp (nonfuzzy) versions of the AHP and TOPSIS with the fuzzy ones. Application of the fuzzy NTMP ranking model is illustrated and its results are compared with the ones obtained in [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] to evaluate the significance of the differences in ranking results. The comparisons show that using fuzzy AHP and TOPSIS approaches instead of the crisp ones in the ranking model provided considerable ranking differences. The fuzzy NTMP ranking model is studied furthermore in the paper by updating the NTMP list and fine-tuning fuzzy weights of the pertinent attributes.

scholarly journals Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

2018 ◽  
Vol 13 (3) ◽  
pp. 391-407
Author(s):  
Xiaofei Ma ◽  
Yi Feng ◽  
Yi Qu ◽  
Yang Yu
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Selection Procedure ◽  
Attribute Selection ◽  
Attribute Data ◽  
Correlation Algorithm ◽  
Decision Attributes ◽  
Optimal Set ◽  
Actual Decision

Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better.

Construction and Validation of a Short Adjectives Checklist to Measure Big Five (SACBIF)

1996 ◽  
Vol 12 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Marco Perugini ◽  
Luigi Leone
Keyword(s):  
Construct Validity ◽  
Psychometric Properties ◽  
Big Five ◽  
Factor Model ◽  
Five Factor Model ◽  
Selection Procedure ◽  
Factorial Structure ◽  
Personality Dimensions

The aim of this contribution is to present a new short adjective-based measure of the Five Factor Model (FFM) of personality, the Short Adjectives Checklist of BIg Five (SACBIF). We present the various steps of the construction and the validation of this instrument. First, 50 adjectives were selected with a selection procedure, the “Lining Up Technique” (LUT), specifically used to identify the best factorial markers of the FFM. Then, the factorial structure and the psychometric properties of the SACBIF were investigated. Finally, the SACBIF factorial structure was correlated with some main measures of the FFM to establish its construct validity and with some other personality dimensions to investigate how well these dimensions could be represented in the SACBIF factorial space.

Using the Theory of Planned Behavior to Predict Faking in Selection Exercises Varying in Fidelity

2018 ◽  
Vol 17 (3) ◽  
pp. 155-160 ◽  
Author(s):  
Daniel Dürr ◽  
Ute-Christine Klehe
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Predictive Validity ◽  
Group Discussion ◽  
Role Play ◽  
Selection Procedure ◽  
High Fidelity ◽  
Selection Research ◽  
Play Group

Abstract. Faking has been a concern in selection research for many years. Many studies have examined faking in questionnaires while far less is known about faking in selection exercises with higher fidelity. This study applies the theory of planned behavior (TPB; Ajzen, 1991 ) to low- (interviews) and high-fidelity (role play, group discussion) exercises, testing whether the TPB predicts reported faking behavior. Data from a mock selection procedure suggests that candidates do report to fake in low- and high-fidelity exercises. Additionally, the TPB showed good predictive validity for faking in a low-fidelity exercise, yet not for faking in high-fidelity exercises.

Predictive Validity of a Selection Procedure for Air Traffic Controller Trainees

2013 ◽  
Vol 3 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Yvonne Pecena ◽  
Doris Keye ◽  
Kristin Conzelmann ◽  
Dietrich Grasshoff ◽  
Peter Maschke ◽  
...  
Keyword(s):  
Predictive Validity ◽  
Selection Procedure ◽  
Air Traffic ◽  
Performance Criteria ◽  
Training Phase ◽  
Air Traffic Controller ◽  
Training Performance ◽  
Navigation Service ◽  
German Aerospace ◽  
Training Success

The job of an air traffic controller (ATCO) is very specific and demanding. The assessment of potential suitable candidates requires a customized and efficient selection procedure. The German Aerospace Center DLR conducts a highly selective, multiple-stage selection procedure for ab initio ATCO applicants for the German Air Navigation Service Provider DFS. Successful applicants start their training with a training phase at the DFS Academy and then continue with a unit training phase in live traffic. ATCO validity studies are scarcely reported in the international scientific literature and have mainly been conducted in a military context with only small and male samples. This validation study encompasses the data from 430 DFS ATCO trainees, starting with candidate selection and extending to the completion of their training. Validity analyses involved the prediction of training success and several training performance criteria derived from initial training. The final training success rate of about 79% was highly satisfactory and higher than that of other countries. The findings demonstrated that all stages of the selection procedure showed predictive validity toward training performance. Among the best predictors were scores measuring attention and multitasking ability, and ratings on general motivation from the interview.

Identifying Measurement Invariant Item Sets in Cross-Cultural Settings Using an Automated Item Selection Procedure

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 177-188 ◽  
Author(s):  
Martin Schultze ◽  
Michael Eid
Keyword(s):  
Measurement Invariance ◽  
Optimal Solution ◽  
Selection Procedure ◽  
Cross Cultural ◽  
Item Selection ◽  
Ant System ◽  
Item Quality ◽  
Ant System Algorithm ◽  
Selection For ◽  
Selection Of

Abstract. In the construction of scales intended for the use in cross-cultural studies, the selection of items needs to be guided not only by traditional criteria of item quality, but has to take information about the measurement invariance of the scale into account. We present an approach to automated item selection which depicts the process as a combinatorial optimization problem and aims at finding a scale which fulfils predefined target criteria – such as measurement invariance across cultures. The search for an optimal solution is performed using an adaptation of the [Formula: see text] Ant System algorithm. The approach is illustrated using an application to item selection for a personality scale assuming measurement invariance across multiple countries.

Knowledge-Based Decision Support for Diagnosis and Therapy: On the Multiple Usability of Patient Data

1989 ◽  
Vol 28 (02) ◽  
pp. 69-77 ◽  
Author(s):  
R. Haux
Keyword(s):  
Expert Systems ◽  
Statistical Data ◽  
Patient Data ◽  
Attribute Selection ◽  
Patient Specific ◽  
Statistical Data Analysis ◽  
Inference Mechanism ◽  
Diagnosis And Therapy ◽  
Knowledge Based ◽  
Clinical Registries

Abstract:Expert systems in medicine are frequently restricted to assisting the physician to derive a patient-specific diagnosis and therapy proposal. In many cases, however, there is a clinical need to use these patient data for other purposes as well. The intention of this paper is to show how and to what extent patient data in expert systems can additionally be used to create clinical registries and for statistical data analysis. At first, the pitfalls of goal-oriented mechanisms for the multiple usability of data are shown by means of an example. Then a data acquisition and inference mechanism is proposed, which includes a procedure for controlling selection bias, the so-called knowledge-based attribute selection. The functional view and the architectural view of expert systems suitable for the multiple usability of patient data is outlined in general and then by means of an application example. Finally, the ideas presented are discussed and compared with related approaches.

OPTIMIZATION OF ATTRIBUTE SELECTION MODEL USING BIO-INSPIRED ALGORITHMS

2019 ◽  
Vol 18 (1) ◽  
pp. 35-55
Author(s):  
Mohammad Aizat Basir ◽  
Yuhanis Yusof ◽  
Mohamed Saifullah Hussin
Keyword(s):  
Selection Model ◽  
Attribute Selection

Experience of using time-lapse microscopy in IVF and ICSI programs

2020 ◽  
pp. 47-50
Author(s):  
N. V. Saraeva ◽  
N. V. Spiridonova ◽  
M. T. Tugushev ◽  
O. V. Shurygina ◽  
A. I. Sinitsyna
Keyword(s):  
Pregnancy Rate ◽  
Embryo Transfer ◽  
Selection Procedure ◽  
Time Lapse ◽  
Single Embryo Transfer ◽  
Main Group ◽  
Clinical Pregnancy ◽  
Control Group ◽  
Time Lapse Microscopy

In order to increase the pregnancy rate in the assisted reproductive technology, the selection of one embryo with the highest implantation potential it is very important. Time-lapse microscopy (TLM) is a tool for selecting quality embryos for transfer. This study aimed to assess the benefits of single-embryo transfer of autologous oocytes performed on day 5 of embryo incubation in a TLM-equipped system in IVF and ICSI programs. Single-embryo transfer following incubation in a TLM-equipped incubator was performed in 282 patients, who formed the main group; the control group consisted of 461 patients undergoing single-embryo transfer following a traditional culture and embryo selection procedure. We assessed the quality of transferred embryos, the rates of clinical pregnancy and delivery. The groups did not differ in the ratio of IVF and ICSI cycles, average age, and infertility factor. The proportion of excellent quality embryos for transfer was 77.0% in the main group and 65.1% in the control group (p = 0.001). In the subgroup with receiving eight and less oocytes we noted the tendency of receiving more quality embryos in the main group (р = 0.052). In the subgroup of nine and more oocytes the quality of the transferred embryos did not differ between two groups. The clinical pregnancy rate was 60.2% in the main group and 52.9% in the control group (p = 0.057). The delivery rate was 45.0% in the main group and 39.9% in the control group (p > 0.050).

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