Job Shop Sequencing in Manufacturing Plants by Timed Coloured Petri Nets and Particle Swarm Optimization

The increasing diversity of products that can be machined in a plant poses scheduling challenges in modern manufacturing environments. The problem, which is known in literature as Flexible Job Shop Scheduling, is NP-Hard in general and can be efficiently solved by meta-heuristic approaches. This paper models the production plant in a Timed Coloured Petri Nets (TCPN) framework to describe production systems including flexible Computerized Numerical Control machines. Then, an algorithm is introduced to simulate the TCPN and compute the system throughput. Finally, the throughput is maximized by optimizing the job type sequencing and the amount of units of each job type that enters the system by implementing a Particle Swarm Optimization algorithm. The proposed simulation and optimization approaches are applied to a real manufacturing system producing ophthalmic lenses in order to show the effectiveness and the benefits of the proposed method.