Özpınar, AlperSerengil, Şefik İlgin2019-08-202019-08-202016https://hdl.handle.net/11467/2880In today’s world of competitive international economy sectors, service industry or service sector oriented businesses, the key point is to maximize the efficiency and sustainability of the business directly related with optimal planning of the workload and distributing them among the employees. Helpdesks and operation centers are one of the fastest developing service area of this sector. This paper compares the machine learning algorithms that can be used for the classification of workforce requirements for a bank operation center which provides support to reduce operational workload of bank branches. Classification of the workload based on the quantity of Money Order and EFT operations within time zones aids in the management of workforce teams and distribution of jobs between team members.eninfo:eu-repo/semantics/openAccessBank OperationsWorkforce PlanningClassificationMachine LearningANNBayesian NetworksSMOSVMArticle412152158