Extensive research has been conducted in the field of Flexible Manufacturing Systems (FMSs) planning, most of it has primarily concentrated on well-established academic scheduling systems. When it comes to the selection process, which often relies on fundamental principles within the intelligent system JSSE (Workshop Planning Environment), there is a notable scarcity of literature concerning their performance within an FMS. This article aims to address this gap by examining the performance model of machine and Automated Guided Vehicle (AGV) scheduling in terms of mean flow time using the Artficial Neural Network (ANN) strategy. The study conducts experiments through an FMS simulation model, involving 40 scenarios to assess these concepts.