Harun Reşit Yazgan

An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software. Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of experts opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation. An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.

Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem

The flexible manufacturing system (FMS) scheduling problem is one of the most difficult NP-hard combinatorial optimisation problems. The exact solution of an FMS scheduling problem cannot be found within a reasonable amount of time, even for small size problems. Therefore, a metaheuristic algorithm is required to solve such a problem. The objective of this study is to develop a genetic algorithm (GA) approach to minimise makespan of the scheduling problem. A Taguchi orthogonal array is proposed instead of a full factorial experimental design for determining the parameters of the GA. The effects of the GA parameters on the minimum makespan values are determined and an analysis of variance is performed to investigate significance factors on the results.

A sequence dependent single machine scheduling problem with fuzzy axiomatic design for the penalty costs

Applicability of the schedules was raised using FAD.The FAD technique enables to take into account the experiences of scheduling experts.We proposed an approach for a real life problem that cannot be modelled mathematically.We employed the approach for the problem with 4.7% heuristic error. In this paper, a scheduling problem on a single machine producing dairy products subject to variable due dates, earliness and tardiness penalty costs and sequence dependent setup times was studied. The main purpose was to meet demands of customers just in time whilst total penalty costs of earliness and tardiness were minimized. In addition, applicability of the schedules was appraised using Fuzzy Axiomatic Design (FAD) to determine earliness and tardiness penalty costs. A hierarchical approach consisting of meta-heuristic algorithms such as a tabu search and a genetic algorithm was proposed to generate proper schedules. In the first stage, an initial solution was found by the tabu search algorithm. The solution was improved using the genetic algorithm to find an optimum or a near optimum solution in the second stage. The proposed approach was employed on a cheese production process in the food industry. An instance of the algorithm was demonstrated to illustrate the applicability of the proposed approach and the results obtained were highly favourable.

A solution approach for vehicle routing problem with simultaneous pickup and delivery

Es zamanli topla dagit arac rotalama problemi; musterilerin dagitim ve toplama taleplerinin es zamanli olarak karsilandigi bir arac rotalama problemidir. Bu tez kapsaminda bir ana depo uzerinden 76 musteriye hizmet saglayacak bir firmanin arac rotalama problemi ele alinmistir. Minimum sayida arac kullanimi ile gidilen mesafeyi en kucukleyecek arac rotalarinin olusturulmasi hedeflenmistir. Problem cozumu icin literaturde yer alan karisik tamsayili matematiksel model kullanilmistir ve sezgisel bir algoritma gelistirilmistir. Farkli buyuklukteki veri setlerine algoritma uygulanmis elde edilen cozumler regresyon analizi ile degerlendirilmistir.