Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Marmara Üniversitesi, Türkiye
Tezin Onay Tarihi: 2019
Tezin Dili: İngilizce
Öğrenci: Esra Danacı
Danışman: Ali Fuat Alkaya
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Özet:This thesis work is inspired from real life project which had been undertaken for a large Turkish bank. Determining when to visit and how much to load to each ATM of the bank in an efficient way has become a problem for all banks in real life. Therefore, objective of the problem turns out to be forecasting how much money will be withdrawn from the ATM in the next days. This forecasting task is no way an easy task to cope with since the past withdrawal patterns are too volatile. In essence, the classical time series methods may not perform well on our data. As a result, we decided to implement several swarm intelligence techniques since they are known as well performing optimizers being implemented to broad range of problems successfully already. In this thesis, novel meta-heuristic based solution approaches are developed to cope with this problem. Specifically, the techniques utilized in this study are; artificial bee colony, differential evolution, migrating birds optimization, particle swarm optimization and simulated annealing. In this study, aforementioned algorithms are implemented first by designing the operators of the algorithms by considering the nature of the problem. Then, parameters of the algorithms are fine-tuned with computational tests. In the last phase, all meta-heuristics are applied to the problem instances with their best performing parameter values and compared through extensive computational experiments. As a result of the comparison, it has been observed that the differential evolution and migrating birds optimization algorithm give close and best values. -