| 1. |
- Suder, Asli, et al.
(author)
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Fuzzy Multiattribute Consumer Choice among Health Insurance Options
- 2016
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In: Technological and Economic Development of Economy. - : Vilnius Gediminas Technical University. - 1392-8619 .- 2029-4913 .- 2029-4921. ; 22:1, s. 1-20
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Journal article (peer-reviewed)abstract
- People buy insurance to protect themselves against possible financial loss in the future. Health insurance provides protection against the possibility of financial loss due to health care use. A selection among health insurance options is a multiattribute decision making problem including many conflicting criteria. This problem can be better solved using the fuzzy set theory since human decision making is generally based on vague and linguistic data. We propose an integrated methodology composed of fuzzy AHP and fuzzy TOPSIS to select the best health insurance option. The considered option types, Health Savings Account (HSA), Flexible Spending Accounts (FSA), and Health Reimbursement Arrangement (HRA) are evaluated using eight different criteria under fuzziness. A sensitivity analysis is also realized.
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| 2. |
- Turanoglu Bekar, Ebru, 1987, et al.
(author)
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An ANFIS Algorithm for Forecasting Overall Equipment Effectiveness Parameter in Total Productive Maintenance
- 2015
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In: Journal of Multiple-Valued Logic and Soft Computing. - 1542-3980. ; 25:6, s. 535-554
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Journal article (peer-reviewed)abstract
- otal Productive Maintenance (TPM) is a successful technique used for corrective, preventive and predictive maintenance policies. It is important in identifying the success and overall effectiveness of the manufacturing process for long term economic viability of business. Overall equipment effectiveness (OEE) is commonly used and well-accepted metric for TPM implementation in many manufacturing industries. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to obtain forecasted results for OEE parameter in TPM through some predetermined inputs such as availability, performance efficiency and rate of quality. Triangular type of membership functions was determined as low, medium, and high for each input parameter in the ANFIS model. Fuzzy c-means clustering algorithm was used for determining of the membership degrees of membership functions for each input parameter. This study is important to forecast the risk by OEE in the TPM. With the predicted results of OEE performance an appropriate maintenance strategy can be developed and the production can be improved. This can also help reducing the risk level of breakdowns or failures at any critical equipment.
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| 3. |
- Turanoglu Bekar, Ebru, 1987, et al.
(author)
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Fuzzy COPRAS Method for Performance Measurement in Total Productive Maintenance: A Comparative Analysis
- 2016
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In: Journal of Business Economics and Management. - : Vilnius Gediminas Technical University. - 1611-1699 .- 2029-4433. ; 17:5, s. 663-684
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Journal article (peer-reviewed)abstract
- Modern manufacturing firms should be supported by effective maintenance to become successful in their operations. One of the approaches for improving the performance of maintenance activities is to implement a total productive maintenance (TPM) strategy. Overall equipment effectiveness (OEE) is the key measure of TPM. According to the results of the literature review, the performance elements measured by the OEE tool are not sufficient to describe the effectiveness of TPM implementation. Hence, we aim at developing and evaluating new performance measures oriented towards the quantification of TPM implementation effectiveness under fuzzy environment. For the evaluation of each performance measure, at first, the nominal group technique has been used. Then to determine whether these performance measures are statistically significant, conjoint analysis based experimental design has been applied. In the second step, COmplex PRo-portional ASsessment of alternatives with Grey relations (COPRAS-G) and the fuzzy COPRAS method has been developed to evaluate these performance measures in TPM. Proposed fuzzy COPRAS method gives the reassuring results of ranking newly developed performance measures in TPM.
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| 4. |
- Turanoglu Bekar, Ebru, 1987, et al.
(author)
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Using Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network and Response Surface Method to Optimize Overall Equipment Effectiveness for An Automotive Supplier Company
- 2017
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In: Journal of Multiple-Valued Logic and Soft Computing. - 1542-3980. ; 28:4-5, s. 375-407
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Journal article (peer-reviewed)abstract
- Total Productive Maintenance (TPM) is a successful technique that is important in identifying the success and overall effectiveness of the manufacturing process for long term economic viability of business. Overall equipment effectiveness (OEE) is commonly used and well-accepted metric for TPM implementation in many manufacturing industries. As OEE is an important performance measure for effectiveness of any equipment, careful analysis is required to know the effect of various components. An attempt has been done in this research to predict the OEE by using simulation software. The objective is to identify an optimal OEE level to maximize the time between failures and simultaneously minimize the mean repair time. The process of OEE is optimized by using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference system (ANFIS) to identify optimized zone for maximizing output. Finally it is determined the feasible values of inputs using Sequential Quadratic Programming (SQP) algorithm based on trained ANFIS predictive model. The result from this study can be used the inconvenient impact of the failures on the production process, it is strongly recommended to upgrade the operation management, i.e. TPM program, capacity analysis, parts replacement decisions, training programs for technicians/operators, spare parts requirement etc.
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| 5. |
- Öztayşi, Başar, et al.
(author)
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Fuzzy Analytic Hierarchy Process with Interval Type-2 Fuzzy Sets
- 2014
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In: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 59, s. 48-57
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Journal article (peer-reviewed)abstract
- The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1’s, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.
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