Kumru Didem Atalay

Quality improvement strategies of highway bus service based on a fuzzy quality function deployment approach

In this study, fuzzy quality function deployment (FQFD) is used as a tool to improve service quality in the passenger transportation industry. QFD enables firms to hear the voice of the customer appropriately and integrate it with the technical language in design processes. However, the nature of uncertainty and subjectivity in service delivery processes makes it difficult to employ QFD consistently. This study provides an empirical example of how FQFD could be appropriately used. ‘Employee’ and ‘tangibility of buses’ are the items from the hybrid views of both customers and service provider managers who require the most attention when differentiating a passenger transportation service from a rivals. The vital technical requirements relate to ‘employees’, ‘the features of buses’, and ‘error-free services’.

A Comparative Holistic Fuzzy Approach for Evaluation of the Chain Performance of Suppliers

The competition between the companies in the dynamic market conditions has made the Supply Chain Management (SCM) a more important issue. The companies which have organized their supply chain effectively have obtained more flexibility in their manufacturing processes in addition to delivery of the customer demands. In this study, two different multicriteria decision making algorithms composed of the FAHP and a holistic hybrid method using FTOPSIS were utilized for an electronic company in wholly fuzzy processes. The FAHP is used for determination of the global weights of the factors and the performances of alternative suppliers are evaluated by using both FAHP-based and FAHP-FTOPSIS hybrid methods for synthetic extent values of pairwise comparisons. The sequences of the suppliers differed for the algorithms. The performances of the proposed approaches are quite successful and flexible in a narrow interval. The managerial advantages obtained from the proposed fuzzy algorithms are also analyzed and interpreted.

A new hybrid intuitionistic approach for new product selection

This paper proposes a new hybrid approach for multi-criteria decision-making problems combining intuitionistic fuzzy analytic hierarchy process and intuitionistic fuzzy multi-objective optimization by ratio analysis. Analytic hierarchy process has an inherent ability for handling intangible problems and implements a simple scale to represent evaluations in the structure of pairwise comparisons. Multi-objective optimization by ratio analysis optimizes the solution of a problem having two or more conflicting objectives, taking into account certain constraints. In real-life decision problems, evaluations of decision makers related to performance of alternatives and criteria weights can be expressed by linguistic terms comprising vagueness and uncertainty. These uncertain, vague and hesitant judgments of decision makers can be described more comprehensively by using intuitionistic fuzzy set theory. The proposed approach is a powerful tool for dealing with information which consists of hesitancy and vagueness. An illustrative example related to new product selection for a company is also presented to demonstrate the implementation of the approach.

Randomness tests for the method of uniform sampling quasi-random number generator (MUS-QRNG)

Random number generation is still an important research field in many scientific applications today. Cryptography, Monte Carlo simulations and commertial applications all rely on reference random data. Randomness tests and basic statistics share the same history. Randomness can be summarized as the unpredictability of future samples of a random number generator even in the presence of known all past values. Various randomness tests are developed and due to their individual contributions, usually a battery of tests are applied to verify a random number generator. In signal processing however, the error of a specific observed sample set to a given distribution could be much more important when it is used as the input for a system model. Recently, this distance of finite samples set to a given distribution is studied and a quantitative measure for quality is proposed. Multi run computations like Monte Carlo simulations, often rely on accurate statistical data for high repetibility. Otherwise when the data is not accurate, the results could often rely on the source of random data generator. Many runs are often required to gain a confidence in the presence of those variances. In this work, recently proposed quasi-random number generator utilizing method of uniform sampling (MUS) is tested using standard goodness-of-fitness tests. MUS-QRNG numbers are shown to have exact statistics and also their randomness test results are observed to be similar to well known reference generator of Matlab. MUS-QRNG is proposed for high quality random data generation.

A case study on shopping malls attributes for young consumers

Purpose This study aims to determine the mall criteria that are the most crucial for the youth market by determining the winning brand in comparison to other offerings to understand what is required to gain a competitive advantage and to differentiate a mall from its rivals. Design/methodology/approach This study chose the Stochastic Multicriteria Acceptability Analysis-2 method to evaluate the mall preferences of young people. By using this method, the various criteria were evaluated for more than one alternative to find the best solution. JSMA program was used to analyze the data. The survey was administered using the mall intercept method to reduce sample bias. Findings The study identifies that the criteria that have the highest impact on the mall preferences of young people are the mall campaigns for loyal customers; the traffic in the mall locality and the mall’s parking facilities; the mall’s facilities for disabled people; the quality of the mall locality; and the quality of the people visiting the mall. The study reveals that a mall’s physical features, its facilities and the criteria related to employees have a very low impact on the mall choices of young people. The study further finds that the youth market has very low satisfaction levels for all of the identified criteria. This study reveals that this macro accessibility criterion is less relevant for the youth market than for the general population. Originality/value Despite the importance of this market, there is insufficient research on the shopping behavior of young people. They have a considerable impact on the purchasing decisions of their families, significant disposable income and constitute the future market for the sector. This study uniquely enables the sequential ordering of customers’ decision-making criteria and determines the effectiveness or impact of these criteria in the mall sector.

A New Approach for Wage Management System Using Fuzzy Brackets in Industry

Job evaluation is used to determine the relative importance of each job in a company in order to structure an accurate wage management system. Job evaluation can be also defined as a multicriteria decision-making problem. However, according to the diversity of managers’ assessment, the evaluation processes are often resulting in pay inequity. This outcome can be circumvented by utilizing a fuzzy job evaluation system. In this study, one of the more robust multicriteria decision-making methods, Fuzzy Analytic Hierarchy Process (FAHP), is performed in job evaluation system in order to rank predetermined 13 criteria. The fuzzy wage brackets are developed and inserted into the process which is obtained from the results of mathematical model to designate the bounds for predefined 86 jobs. Eventually an accurate payment system is proposed for a company in steel industry by using Fuzzy Regression Analysis (FRA).

Investigation of System Productivity with Fuzzy Availability Analysis Considering Failure and Repair Times

Today’s more complicated engineering systems make it difficult to analyze system performance accurately due to the increase in the number of components and interconnections within the system. System performance and measures such as failure rate, repair rate and availability are important measures to analyze a system and obtain system productivity. While application of system availability is most widely used for electrical and electronic systems, in recent years it has been started to be used as a performance measure of manufacturing systems. In this study, a novel approach is proposed using both simulation modeling technique and fuzzy availability analysis considering failure and repair times of components to investigate system productivity in a more consistent and logical manner. Simulation model is also used to analyze system behavior and estimate system throughput. Because of insufficient and inaccurate historical data related with component failure and repair times of the considered system, failure and repair data are defined with the fuzzy membership function. This approach provides more detailed information about system characteristics. Thus, more realistic system productivity considering failure and repair times which represents the real behavior of system can be obtained using this approach.

Grey Forecasting Model for CO 2 Emissions of Developed Countries

Global warming endangers our health, jeopardizes our national security, and threatens other basic human needs. Greenhouse gas (GHG) emissions mitigation is a high priority issue for most of the countries in the world. Carbon-dioxide (CO2) is one of the most important GHG emissions, so prediction of CO2 emissions is very important issue for the countries. On the purpose of predicting following years’ CO2 emissions of seven developed countries; Australia, China, Italy, Spain, Turkey, United Kingdom and United States, grey forecasting method GM(1,1) which is suitable for solving uncertainty problems with less or lack of information is used in this study. Grey forecasting method is also widely used to forecast carbon emissions in the literature and generally, a few data are considered. The historical data of CO2 emissions period between 2010 and 2014 is used to forecast the following four-year emissions, up to 2018. For the accuracy of the forecasting model, the post-ratio error (C) indicator is used which is one of the most widely used indicators for similar research. Using this model, some countries that already start to take precautions to decrease emissions could be check if they reach their target and mitigate the effects on climate change in their countries in a long term. This study can also be a counsellor or indicator for the countries that has not improved any environmental policy and chance to take into effect the precautions for their climate change plans. In other words, the methods proposed in this study can be used by countries to review their environmental policies and estimate their outcomes.

Evaluation of effect of different membership functions on risk assessment

This study aims to define the relationship between risk degrees and risk indexes on different functional structures with the assumption that risk degrees may not always present a linear relationship with the risk indexes. In this way, risk indexes suitable for expert evaluation of working conditions and computed using three different membership functions are determined. Among the membership functions used, one is preferred as linear and the others are preferred as non-linear. Additionally, a new fuzzy risk assessment (RA) algorithm is developed using these three membership functions. With this new fuzzy RA algorithm, a more flexible and precise process becomes available, while information loss during the determination of the risk index of danger sources is prevented. As a result, non-linear increasing membership function is selected as most suitable for the expression of the relationship between risk degrees and risk indexes.

A New Stochastic MCDM Approach Based on COPRAS

This study proposes a novel integrated Complex Proportional Assessment (COPRAS) approach by using stochastic decision process named as Stochastic COPRAS (COPRAS-S) to increase the evaluation performance of COPRAS. In COPRAS-S, criteria importance weights and the performance values of alternatives are determined by generating random numbers from uniform distribution in a range of minimum and maximum values of a limited number of decision-maker evaluations. Thus, the numbers of experts are increased and decision-making process is performed in an effective way because different opinions are incorporated. In addition, randomness feature brought with vagueness in decision is modeled in this process. A special normalization approach based on standard deviation is also implemented in COPRAS-S. In this way, cost and benefit type criteria are evaluated in a different way. This proposed stochastic structure for COPRAS is a practical and powerful tool that strengthens the decision.