An Energy-Efficient Scheduling Approach for Flexible Job Shop Problem in an Internet of Manufacturing Things Environment

This paper addresses the energy-efficient scheduling and real-time control of flexible job shop that requires rescheduling affected operations and updating the scheduling. For energy-efficient scheduling shop floor efficiently, we propose a metaheuristic algorithm called PN-ACO algorithm, which combines a timed transition Petri nets (TTPN) based representation tool and an ant colony optimization (ACO) heuristic search method. To address the real-time control problem in energy-efficient scheduling of the shop floor, we apply the Internet of Things (IoT) technology to product production to form an Internet of Manufacturing Things environment (IoMT). In the IoMT environment, there are usually many abnormal event disturbances, including machine breakdown and urgent order arrival. To quickly handle the disturbance problem of abnormal events, the distributed control system architecture is proposed, the core of which is the negotiation and cooperation between manufacturing resources based on the wireless communication network. The proposed approach is further illustrated by a case energy-efficient of scheduling for a flexible job shop through which the optimal scheduling and correct supervisory control instructions can be obtained easily and quickly. In sum, the proposed optimization algorithm obtains a good effect in engineering applications while the validity of optimization is proved.