Integration of Scheduling Methods and Simulation in Ferrari’s Plant

This study examines the painting process at the Ferrari plant, with the aim of developing a vehicle sequencing solution based on the plant's specific constraints. The main objectives of the research include the analysis and mapping of the various paths a vehicle can take during the production process, the definition of weights associated with each path, model, and paint, the identification of constraints within the plant, the determination of the plant's capacity, and the drafting of an algorithm to achieve optimal vehicle sequencing. The methodology followed includes an initial phase of analysis and data collection to identify the available information necessary to solve the problem. First, the possible vehicle paths are identified and subsequently, appropriate clusters are defined for their identification based on analyzable cases. The paths, reflecting the processes to which the vehicles are subjected, are then associated with a specific weight. Subsequently, the weights related to painting are identified based on the vehicle model, thus allowing a value to be associated with the complexity of the painting process for each model, color, and procedure. Finally, through both plant and process analysis, the plant's capacity and existing constraints are identified, enabling the use of scheduling and sequencing algorithms for the vehicles. In this research, after examining various algorithms, a code based on Johnson's algorithm is implemented for vehicle sequencing. The results obtained demonstrate that the proposed sequencing offers a better balance of the plant, enabling an automatic decision-making system for vehicle insertion, which was essential as such a system was absent prior to this study. In conclusion, such a system also allows for the analysis of hypothetical scenarios through simulations, showing how the plant would behave under different conditions. This provides a decision-making tool for future plant variations or expansions, thanks to the identification of constraints that limit optimization.