A Generic Model for the Human and AGV-Aided Reconfigurable Flexible Job Shop Scheduling Problems

Scheduling tasks for workshops has been a critical problem in modern factories, especially when many countries promote the development of Industry 4.0. Nowadays, many factories are shifting to flexible, intelligent manufacturing systems (IMS) to improve their ability to respond to customers’ demands of variation and individualization. Previous job shop scheduling problems always simplified the production scenarios by ignoring the machines’ reconfiguration abilities and the participation of human and automated guided vehicle systems (AGVS), which are critical parts of IMS. To address these issues, this paper derives a novel mix-integer programming model for highly-flexible job shop scheduling models in human and AGV-aided IMS to minimize the makespan. A dataset is generated to examine the effectiveness of our model. Furthermore, we conducted numerical experiments on the dataset. Through experiments and analysis, we explore the interaction between optimal values and key factors, including processing time and machine numbers.