Simulation-optimization approach for the decision-support on the planning and scheduling of automated assembly lines

This work explores a decision-support solution for the management of advanced flexible manufacturing systems operated by mobile robotic resources with a simulation-optimization methodology for the production planning and scheduling of automated assembly lines. The methodology explores an iterative procedure based on literature combining a mathematical programming model with a discrete-event simulation model, in order to provide optimal a production schedule regarding line efficiency and number of robotic resources required. This hybrid approach proposes an optimization model to provide the initial production planning for a set of products, while the detailed simulation model iteratively validates a capacity-feasible schedule with the effective workload and utilization of workstations. The results discuss the advantages of this hybrid methodology to improve overall process performance metrics towards the operational optimization of real-world flexible manufacturing systems.