Şakir Esnaf | Istanbul University (original) (raw)

Papers by Şakir Esnaf

Research paper thumbnail of Comparison Of Fuzzy Time Series Based On Difference Parameters And Two-Factor Time-Variant Fuzzy Time Series Models For Aviation Fuel Production Forecasting

Time series models have been utilized to make accurate predictions in production. This paper empl... more Time series models have been utilized to make accurate predictions in production. This paper employs a 3 year period of aviation fuel production data of Turkey as experimental data set. To forecast the aviation fuel production amounts, fuzzy time series forecasting based on difference parameters and two-factor time-variant fuzzy time series models are used and the results have been compared in this study. Based on the comparison results in the case of aviation fuel production, we conclude that both of the fuzzy time series models have advantages and disadvantages in use.

Research paper thumbnail of Solving Uncapacitated Planar Multi-facility Location Problems by a Revised Weighted Fuzzy c-means Clustering Algorithm

Journal of Multiple-Valued Logic and Soft Computing, 2013

In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar mu... more In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar multi-facility location problems. It eliminates the obligation to sequentially use different methods such as classical fuzzy c-means algorithm, combination of fuzzy c-means and center of gravity, and particle swarm optimization algorithm. Performance of the proposed algorithm for uncapacitated planar multi-facility location problem is tested on well-known research data sets. This new algorithm is compared with the methods including fuzzy c-means, fuzzy c-means based center of gravity and particle swarm optimization. Results indicate that the proposed revised weighted fuzzy c-means algorithm based method is superior in terms of cost minimization and CPU time.

Research paper thumbnail of Fuzzy C-Means Algorithm with Fixed Cluster Centers for Uncapacitated Facility Location Problems: Turkish Case Study

Supply Chain Management Under Fuzziness Studies in Fuzziness and Soft Computing, Jan 14, 2014

In this study, a new algorithm to solve uncapacitated facility location problems is proposed. The... more In this study, a new algorithm to solve uncapacitated facility location problems is proposed. The algorithm is a special version of original fuzzy c-means (FCM) algorithm. In FCM algorithm, unlabeled data are clustered and the cluster centers are determined according to priori known stopping criterion iteratively. Unlike the original FCM, the proposed algorithm allows the unlabeled data are to be assigned with single iteration to related clusters centers, which are assumed to be fixed and known a priori like location of facilities according to their degrees of membership. First, the proposed algorithm is applied to various benchmark problems from literature and compared with integer programming. Second, the proposed algorithm is tested and compared with particle swarm optimization (PSO) and artificial bee colony optimization (ABC) algorithms based uncapaci- tated facility location method on alternative versions such as discrete, continuous, discrete with local search and continuous with local search in literature for a Turkish fertilizer producer’s real data. Numerical results obtained from real life application show that the proposed algorithm outperforms the PSO-based and ABC-based algorithms.

Research paper thumbnail of A fuzzy clustering-based hybrid method for a multi-facility location problem

A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this... more A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, cus- tomers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facil- ities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The cen- ter of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to com- pare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported.

Research paper thumbnail of Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem

Research paper thumbnail of Estimation of the manufacturing industry sub-sectors’ capacity utilization rates using support vector machines

Artificial Intelligence Research, 2013

Research paper thumbnail of A fuzzy clustering-based hybrid method for a multi-facility location problem

Journal of Intelligent Manufacturing, 2009

Research paper thumbnail of Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

Expert Systems With Applications

In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacit... more In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply cen- tres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which opti- mizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer’s real-world data in Turkey are reported. Numerical results show that the proposed approach performs bet- ter than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.

Research paper thumbnail of Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem

Expert Systems with Applications, 2011

Research paper thumbnail of Comparison of Fuzzy Time Series Based on Difference Parameters and Two-Factor Time-Variant Fuzzy Time Series Models for Aviation Fuel Production Forecasting

Time series models have been utilized to make accurate predictions in production. This paper empl... more Time series models have been utilized to make accurate predictions in production. This paper employs a 3 year period of aviation fuel production data of Turkey as experimental data set. To forecast the aviation fuel production amounts, fuzzy time series forecasting based on difference parameters and two-factor time-variant fuzzy time series models are used and the results have been compared in this study. Based on the comparison results in the case of aviation fuel production, we conclude that both of the ...

Research paper thumbnail of Geri Dönüşüm Tesislerinin Yerinin Gustafson-Kessel Algoritması- Konveks Programlama Melez Modeli Tabanlı Simülasyon ile Belirlenmesi

iticu.edu.tr

İstanbul giderek artan nüfusu ve genişleyen coğrafi yapısıyla her geçen gün daha fazla kaynağa ge... more İstanbul giderek artan nüfusu ve genişleyen coğrafi yapısıyla her geçen gün daha fazla kaynağa gereksinim duyan bir metropoldur. Artan tüketimin doğal dengeyi bozmasını engellemek ve doğaya verilen zararı azaltmak, ayrıca yeniden dönüştürülebilen maddelerin tekrar hammadde olarak kullanılmasıyla büyük miktarda enerji tasarrufu sağlamak amacıyla firmalar geri dönüşüm süreçlerine başvurmaktadır. Geri dönüşüm terim olarak, kullanım dışı kalan geri dönüştürülebilir atık malzemelerin çeşitli geri dönüşüm yöntemleri ile hammadde olarak tekrar imalat süreçlerine kazandırılmasıdır. Bu makalede bir asfalt firmasının kurulacak geri dönüşüm tesisleri için kapasite, maliyet, talep ve coğrafi konum kısıtlarına bağlı olarak öncelikle optimum yerle, Gustafson-Kessel bulanık öbekleme algoritması-Konveks programlama melez modeli ile belirlenmiş , daha sonra da belirlenen yer veya yerlere bağlı olarak çeşitli koşullar altında gerçek sisteme ait lojistik performansı, maliyet, darboğaz noktaları ve makine/araç gereksinimi gibi parametreler bir simülasyon uygulaması ile incelenmiştir.

Research paper thumbnail of Data Clustering by Particle Swarm Optimization with the Focal Particles

Clustering is an important technique in data mining. In unsupervised clustering, data is divided ... more Clustering is an important technique in data mining. In unsupervised clustering, data is divided into several subsets (clusters) without any prior knowledge. Heuristic optimization based clustering algorithms tries to minimize an objective function, generally a clustering validity index, in the search space defined by the dimensions of the data vectors. If the number of the attributes of the data is large, then this will decrease the clustering performance. This study presents a new clustering algorithm, particle swarm optimization with the focal particles (PSOFP). Contrary to the standard particle swarm optimization (PSO) approach, this new clustering technique ensures high quality clustering results without increasing the dimensions of the search space. This new clustering technique handles communication among the particles in a swarm by using multiple focal particles. The number of focal particles equals to the number of clusters. This approach simplifies the candidate solution representation by a particle and therefore reduces the effect of ‘curse of dimensionality’. Performance of the proposed method on the clustering analysis is benchmarked against K-means, K-means++, hybrid PSO and the CLARANS algorithms on five datasets. Experimental results show that the proposed algorithm has an acceptable efficiency and robustness and superior to the benchmark algorithms.

Research paper thumbnail of Comparison Of Fuzzy Time Series Based On Difference Parameters And Two-Factor Time-Variant Fuzzy Time Series Models For Aviation Fuel Production Forecasting

Time series models have been utilized to make accurate predictions in production. This paper empl... more Time series models have been utilized to make accurate predictions in production. This paper employs a 3 year period of aviation fuel production data of Turkey as experimental data set. To forecast the aviation fuel production amounts, fuzzy time series forecasting based on difference parameters and two-factor time-variant fuzzy time series models are used and the results have been compared in this study. Based on the comparison results in the case of aviation fuel production, we conclude that both of the fuzzy time series models have advantages and disadvantages in use.

Research paper thumbnail of Solving Uncapacitated Planar Multi-facility Location Problems by a Revised Weighted Fuzzy c-means Clustering Algorithm

Journal of Multiple-Valued Logic and Soft Computing, 2013

In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar mu... more In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar multi-facility location problems. It eliminates the obligation to sequentially use different methods such as classical fuzzy c-means algorithm, combination of fuzzy c-means and center of gravity, and particle swarm optimization algorithm. Performance of the proposed algorithm for uncapacitated planar multi-facility location problem is tested on well-known research data sets. This new algorithm is compared with the methods including fuzzy c-means, fuzzy c-means based center of gravity and particle swarm optimization. Results indicate that the proposed revised weighted fuzzy c-means algorithm based method is superior in terms of cost minimization and CPU time.

Research paper thumbnail of Fuzzy C-Means Algorithm with Fixed Cluster Centers for Uncapacitated Facility Location Problems: Turkish Case Study

Supply Chain Management Under Fuzziness Studies in Fuzziness and Soft Computing, Jan 14, 2014

In this study, a new algorithm to solve uncapacitated facility location problems is proposed. The... more In this study, a new algorithm to solve uncapacitated facility location problems is proposed. The algorithm is a special version of original fuzzy c-means (FCM) algorithm. In FCM algorithm, unlabeled data are clustered and the cluster centers are determined according to priori known stopping criterion iteratively. Unlike the original FCM, the proposed algorithm allows the unlabeled data are to be assigned with single iteration to related clusters centers, which are assumed to be fixed and known a priori like location of facilities according to their degrees of membership. First, the proposed algorithm is applied to various benchmark problems from literature and compared with integer programming. Second, the proposed algorithm is tested and compared with particle swarm optimization (PSO) and artificial bee colony optimization (ABC) algorithms based uncapaci- tated facility location method on alternative versions such as discrete, continuous, discrete with local search and continuous with local search in literature for a Turkish fertilizer producer’s real data. Numerical results obtained from real life application show that the proposed algorithm outperforms the PSO-based and ABC-based algorithms.

Research paper thumbnail of A fuzzy clustering-based hybrid method for a multi-facility location problem

A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this... more A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, cus- tomers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facil- ities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The cen- ter of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to com- pare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported.

Research paper thumbnail of Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem

Research paper thumbnail of Estimation of the manufacturing industry sub-sectors’ capacity utilization rates using support vector machines

Artificial Intelligence Research, 2013

Research paper thumbnail of A fuzzy clustering-based hybrid method for a multi-facility location problem

Journal of Intelligent Manufacturing, 2009

Research paper thumbnail of Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

Expert Systems With Applications

In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacit... more In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply cen- tres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which opti- mizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer’s real-world data in Turkey are reported. Numerical results show that the proposed approach performs bet- ter than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.

Research paper thumbnail of Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem

Expert Systems with Applications, 2011

Research paper thumbnail of Comparison of Fuzzy Time Series Based on Difference Parameters and Two-Factor Time-Variant Fuzzy Time Series Models for Aviation Fuel Production Forecasting

Time series models have been utilized to make accurate predictions in production. This paper empl... more Time series models have been utilized to make accurate predictions in production. This paper employs a 3 year period of aviation fuel production data of Turkey as experimental data set. To forecast the aviation fuel production amounts, fuzzy time series forecasting based on difference parameters and two-factor time-variant fuzzy time series models are used and the results have been compared in this study. Based on the comparison results in the case of aviation fuel production, we conclude that both of the ...

Research paper thumbnail of Geri Dönüşüm Tesislerinin Yerinin Gustafson-Kessel Algoritması- Konveks Programlama Melez Modeli Tabanlı Simülasyon ile Belirlenmesi

iticu.edu.tr

İstanbul giderek artan nüfusu ve genişleyen coğrafi yapısıyla her geçen gün daha fazla kaynağa ge... more İstanbul giderek artan nüfusu ve genişleyen coğrafi yapısıyla her geçen gün daha fazla kaynağa gereksinim duyan bir metropoldur. Artan tüketimin doğal dengeyi bozmasını engellemek ve doğaya verilen zararı azaltmak, ayrıca yeniden dönüştürülebilen maddelerin tekrar hammadde olarak kullanılmasıyla büyük miktarda enerji tasarrufu sağlamak amacıyla firmalar geri dönüşüm süreçlerine başvurmaktadır. Geri dönüşüm terim olarak, kullanım dışı kalan geri dönüştürülebilir atık malzemelerin çeşitli geri dönüşüm yöntemleri ile hammadde olarak tekrar imalat süreçlerine kazandırılmasıdır. Bu makalede bir asfalt firmasının kurulacak geri dönüşüm tesisleri için kapasite, maliyet, talep ve coğrafi konum kısıtlarına bağlı olarak öncelikle optimum yerle, Gustafson-Kessel bulanık öbekleme algoritması-Konveks programlama melez modeli ile belirlenmiş , daha sonra da belirlenen yer veya yerlere bağlı olarak çeşitli koşullar altında gerçek sisteme ait lojistik performansı, maliyet, darboğaz noktaları ve makine/araç gereksinimi gibi parametreler bir simülasyon uygulaması ile incelenmiştir.

Research paper thumbnail of Data Clustering by Particle Swarm Optimization with the Focal Particles

Clustering is an important technique in data mining. In unsupervised clustering, data is divided ... more Clustering is an important technique in data mining. In unsupervised clustering, data is divided into several subsets (clusters) without any prior knowledge. Heuristic optimization based clustering algorithms tries to minimize an objective function, generally a clustering validity index, in the search space defined by the dimensions of the data vectors. If the number of the attributes of the data is large, then this will decrease the clustering performance. This study presents a new clustering algorithm, particle swarm optimization with the focal particles (PSOFP). Contrary to the standard particle swarm optimization (PSO) approach, this new clustering technique ensures high quality clustering results without increasing the dimensions of the search space. This new clustering technique handles communication among the particles in a swarm by using multiple focal particles. The number of focal particles equals to the number of clusters. This approach simplifies the candidate solution representation by a particle and therefore reduces the effect of ‘curse of dimensionality’. Performance of the proposed method on the clustering analysis is benchmarked against K-means, K-means++, hybrid PSO and the CLARANS algorithms on five datasets. Experimental results show that the proposed algorithm has an acceptable efficiency and robustness and superior to the benchmark algorithms.