Gülşen Aydın Keskin

The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection

For most of managers purchasing is a strategic issue. Thus, to select the suitable suppliers has strategic importance for every company. The objective of supplier selection is to reduce purchasing risk, maximize overall value to the purchaser and build a long term, reliable relationship between buyers and suppliers. Many methods have been proposed and used for supplier evaluation and selection; most of them try to rank the suppliers from the best to the worst and to choose the appropriate supplier(s). Supplier evaluation and selection is a complex and typical multi criteria decision-making problem. Because of human judgment needs in many area of supplier selection such as preferences on alternatives or on the attributes of suppliers or the class number and borders supplier selection becomes more difficult and risky. In this study, a new tool for supplier selection is proposed. In this paper, we applied Fuzzy Adaptive Resonance Theory (ART)s classification ability to the supplier evaluation and selection area. The proposed selection method, using Fuzzy ART not only selects the most appropriate supplier(s) and also clusters all of the vendors according to chosen criteria. To explain the Fuzzy ART method a real-life supplier selection problem is solved and suppliers are categorized according to their similarities. The obtained results show that the proposed method is well suited as a decision-making tool for supplier evaluation and selection problem.

Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection

Decision-making techniques are used to help evaluate the current suppliers’ aim at classifying performance of individual suppliers against desired levels of performance, so as to design suitable plans to increase the performance and capabilities of suppliers. In this study, an integrated model is introduced and proposed for increasing the supplier selection and evaluation quality. The methodology is composed of two steps. The first stage is fuzzy decision-making trial and evaluation laboratory method in which the interactions between the evaluation criteria and the criteria weight have been computed. At the second stage, performances of suppliers are assessed using both the criteria weights obtained at the first stage and fuzzy c-means clustering algorithm by classifying the vendors according to their performances. Obtained results show that the proposed model is very well suited as a decision-making tool for supplier selection decisions.

Predicting tropospheric ozone concentrations in different temporal scales by using multilayer perceptron models

article i nfo This study encompasses ozone modeling in the lower atmosphere. It was aimed to develop an appropriate neural network model in order to predict ozone concentrations in various temporal scales as a function of meteorological variables and air quality parameters. All data were collected from Dilovasi, Turkey as this site represents typical industrial regions with major air pollution problems. In the study performance of the multilayer perceptron models were tested for both annual and seasonal periods as meteorological conditions highly influence the ozone levels. Among the various architectures, a network of two hidden layers with fifteen neurons was found to give successful predictions. Modeling efficiency of the developed network was also evaluated for day light and night time data of warming season exhibiting highest ozone levels. Furthermore, principle component analysis was performed by using annual data in order to reduce the number of input variables describing ozone formation. Model run with principle components has also provided satisfying performance.

Multiple Criteria ABC Analysis with FCM Clustering

The number of stock keeping units (SKUs) possessed by organizations can easily reach quite a few. An inventory management policy for each individual SKU is not economical to design. ABC analysis is one of the conventionally used approaches to classify SKUs. In the classical method, the SKUs are ranked with respect to the descending order of the annual dollar usage, which is the product of unit price and annual demand. The few of the SKUs that have the highest annual dollar usage are in group A and should be taken into account mostly; the SKUs with the least annual dollar usage are in group C and should be taken into account least; the remaining SKUs are in group B. In this study, we proposed fuzzy c-means (FCM) clustering to a multicriteria ABC analysis problem to help managers to make better decision under fuzzy circumstancse. The obtained results show that the FCM is a quite simple and an easily adaptable method to inventory management.

Using principal component analysis and fuzzy c–means clustering for the assessment of air quality monitoring

Determining whether a reduction can be made in the total number of monitoring stations within the Air Quality Monitoring Network is very important since in case of necessity, the devices at one group of stations having similar air pollution characteristics can be transferred to another zone. This would significantly decrease the capital investment and operational cost. Therefore, the objective of this study was grouping the monitoring stations that share similar air pollution characteristics by using the methods of principal component analysis (PCA) and fuzzy c– means (FCM). In addition, this study also enables determining the emission sources, evaluating the performances of the methods and examining the zone in terms of pollution. In the classification of monitoring stations, different groups were formed depending on both the method of analysis and the type of pollutants. As a result of PCA, 5 and 3 classes have been determined for SO2 and PM10, respectively. This shows that the number of monitoring stations can be decreased. When reduced classes were analyzed, it was observed that a clear distinction cannot be made considering the affected source type. During the implementation of the FCM method, in order to facilitate comparison with the PCA, the monitoring stations were classified into 5 and 3 groups for SO2 and PM10, respectively. When the results were analyzed, it was seen that the uncertainty in PCA was reduced. When the two methods are compared, FCM was found to provide more significant results than PCA. The evaluation in terms of pollution, the results of the study showed that PM10 exceeded the limit values at all the monitoring stations, and SO2 exceeded the limit values at only 3 of the 22 stations.

Fuzzy adaptive resonance theory approach to supplier involvement in product development: a case study in Turkish automobile industry

This study proposes a mechanism to evaluate suppliers for involvement during product development process. The proposed methodology is discussed within a multinational automobile firm and preliminary analysis indicates that the approach provides an effective mechanism for selecting suppliers involved in the product development process. To remedy to supplier integration problems, a clustering approach – fuzzy adaptive resonance theory (Fuzzy ART) – is used. The proposed methodology is explained using a case study that is realised in an automobile firm. Eventually, the results are presented comparatively, which obtained by current supplier evaluation system of the firm and provided from Fuzzy ART methodology. [Submitted 14 July 2009; Revised 16 November 2009; Accepted 17 November 2009]

Bir Konteyner Limanında Etkin Saha Planlaması için Simülasyon Tabanlı Bir Yaklaşım

Deniz tasimaciligi, cesitli ulasim cesitleri arasinda en onemli olanidir . Konteynerler, deniz tasimaciliginin bir parcasi olarak uluslararasi duzeyde nakledilmektedir. Limanlar, konteyner istifleme surecinin verimliligi acisindan kritik bir role sahiptir. Musteri memnuniyetini en ust duzeyde saglamak icin en kisa surede gelen gemilerin operasyonlarini tamamlamak gerekmektedir. Bu noktada, liman sureclerini iyilestirmek icin simulasyon bir karar destek sistemi olarak etkin bir sekilde kullanilmaktadir. Bu calismada, bir konteyner limanindaki darbogazlari gidererek en uygun istif planlamasini gerceklestirebilmek icin mevcut durum ve iki alternatif senaryo benzetim yoluyla birbirleriyle karsilastirilmistir. Gelen gemiler icin bosaltma zamaninin en aza indirgenmesi, tasiyicilarin vinclerin altindaki bekleme surelerinin kisaltilmasi ve tum ekipmanlarin adil kullanimi icin en uygun saha duzeninin belirlenmesi amaclanmistir. Boylece, enerji ve isgucu maliyetlerinin azaltilmasi planlanmaktadir.

An integrated customer relationship management and Data Mining framework for customer classification and risk analysis in health sector

In todays competitive environment, having a customer oriented approach is inevitable for organizations. At this point, in order to achieve customer satisfaction, customer relationship management (CRM) targets to provide products and services which meet customer expectation. Data which is collected about customers is an important source to determine their needs. Therefore, analysis is made to determine characteristic features of customers by using the data in data bases of the companies. Thanks to this data, it is possible to determine the product choice of customers and to put forth the opportunities that can be provided to customers. Data Mining (DM) is a process of revealing a meaningful information and image by the analysis of data. In this study, the data of a company which is operating in health sector in Turkey was analysed using some data mining algorithms like Naive Bayes which is an algorithm of classification rule, J48 which is one of the algorithms to generate decision tree and k-nearest neighbour IBk algorithm. Thus, customers were classified by product groups and risk factors. As a result of the study, it is aimed to develop a marketing strategy and a more effective sales campaign for the company by using the classification results. As well, to the best of our knowledge, our paper is the first study which realized in health sector.

Metot Zaman Ölçümü (MZÖ) yöntemi ile üretim süresinin iyileştirilmesi: beyaz eşya yan sanayisinde bir uygulama

Gunumuzun dinamik rekabet kosullarinda isletmeler icin maliyetlerin azaltilmasi ve uretim sureclerinde verimliligin arttirilmasi bir zorunluluk haline gelmistir. Bu baglamda uretim surelerinin onceden belirlenmesi ve mumkunse iyilestirilmesi buyuk onem tasimaktadir. Uretim surelerinin belirlenmesi icin kullanilan yontemlerden birisi de Metot Zaman Olcumu (MZO) yontemidir. MZO, el ile yapilan bir isi bu isi yapmak icin gereken temel vucut hareketleri turunden inceleyerek, hareketler icin onceden bir standart sure ortaya koyan is olcum yontemidir. Bu calismada, MZO yontemi ile beyaz esya yan sanayisinde faaliyet gosteren bir isletme icin uretim surelerinin iyilestirilmesi amaclanmistir. Bu yontem yardimiyla en kucuk hareketler bile hassas bir sekilde incelendiginden kucuk iyilestirme firsatlari yakalanarak daha verimli calisan bir uretim hatti elde edilmesi hedeflenmistir. Calisma sonucunda ele alinan urun icin iyilestirme oncesindeki uretim suresi ile iyilestirmeden sonraki uretim suresi karsilastirilarak gunluk uretim miktarinda meydana gelen artis ortaya konulmustur.