Service flow simulation using reinforcement learning models and scene transition nets

Recently, a new academic field, ldquoservice engineeringrdquo has been very actively investigated. However, there are few effective software tools to simulate and evaluate services designed based on the concept of service engineering. In the past, the authors proposed a service flow simulation method using scene transition nets(STN) which is a graphic modeling and simulation method for discrete-continuous hybrid system. However, this method cannot simulate complex service flows including customerspsila decision-making. Nowadays, ldquoneuro economicsrdquo and ldquoneuro marketingrdquo have gotten a lot of attention as new study fields to understand customerspsila behaviors from a viewpoint of brain science. In these studies, it turned out that mechanism of reinforcement learning concerns behavioral selections of customers. In this paper, the authors propose to develop decision-making processes models of customers and to simulate customerspsila behaviors and service flows by using reinforcement learning models and STN.