The Internet of Things Enabled Shop Floor Scheduling and Process Control Method Based on Petri Nets

Abstract

Shop floor scheduling requires consideration of the dynamic, time-varying, and unpredictable natures of the manufacturing environment. A shop floor scheduling/rescheduling method based on Petri net and ant colony optimization (PN-ACO) is proposed given an abnormal event represented by machine breakdown. Because of the difficulty to schedule in the complex and changeable internal and external environment of the shop floor, an Internet-of-Things (IoT)-enabled process control method is proposed, using sensors, RFID, industrial wireless communication, automatic identification, and other technologies to perceive the shop floor field. The value of the mean relative error is 1.77, which illustrates the feasibility and efficiency of the proposed PN-ACO algorithm to solve flexible job shop scheduling problems. To represent operation sequencing information, a schedule timed transition Petri net (TTPN) is proposed which is evolved from the TTPN. On the basis of the mapping mechanism between the Petri net model and the XML, manufacturing resources become autonomous and interactive distributed intelligent manufacturing resources. The experimental results confirm that the proposed method is effective for scheduling and process control of the IoT-enabled shop floor.