Şakir Esnaf

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

For the solution of decision making problems with multi criteria, the literature presents many methodologies under the title of decision theory. In this context, AHP, TOPSIS, ELECTRE and Grey Theory are well-known and the most acceptable methodologies. Firstly, in this study; these methodologies are compared in terms of main characteristic of decision theory and thus advantages and disadvantages of these methodologies are offered. Later, the application of these methodologies on the warehouse selection problem, which is one of the main topics of logistics management that has a wide range of applications with multi-criteria decision making methodologies, is presented as a case study which is characterized in retail sector, that maintains high uncertainity and product variety and then how to choose the best warehouse location among many alternatives has been shown.

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 study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, customers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facilities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The center of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to compare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported.

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

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 centres. 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 optimizes 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 producers real-world data in Turkey are reported. Numerical results show that the proposed approach performs better than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.

Estimation of the manufacturing industry sub-sectors' capacity utilization rates using support vector machines

Capacity utilization rate is one of the most important indicators of the efficiency of the manufacturing industry, andtherefore of the return of the investments made. Estimation of these rates accurately renders it possible to make importanteconomic decisions such as taking sectorial investment decisions, defining the optimal distribution of sectorial credits,determining non-competitive sectors, making development plans and developing unemployment policies. In this study, weestimated the capacity utilization rates of 21 sub-sectors of the Turkish manufacturing industry using support vectormachines and compared the results with the results obtained from the methods of artificial neural networks and vectorauto-regression. This study is the first in the literature in that it was carried out using this method.

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

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 uncapacitated 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.