Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints

Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production.

Automated summary

This study proposed a novel proactive-reactive methodology for dynamic job-shop scheduling in flexible manufacturing systems, which formulated a joint optimisation model and designed a flowchart for dynamic decision-making, and developed a particle swarm optimisation algorithm integrated with genetic operators to generate a reschedule plan in time. Computational results demonstrate the efficiency of the developed methodology in practical production.