Simulation-based optimization of product integrations in line-less assembly systems based on digital twins

Line-less assembly systems enable increased resilience against uncertain events such as frequent integration of new product variants, material unavailability, personnel shortages and station failures. This can be achieved through the resolution of a general takt time, redundant workstations and flexible precedence graphs. Benefits arise in particular for use cases with a high number of product variants, parallel assembly of various products, and frequent integration of new products and product variants. This work presents a service-based framework for decision-support for product integrations in line-less assembly systems to meet these challenges. Based on a standardized data model serving as a basis for the digital twin, simulation models for system configurations are created automatically to reduce manual planning effort. The approach of simulation-based optimization is used to find feasible scenarios with least reconfiguration effort. Within the simulation, stochastic influences such as station failures and reactive scheduling decisions can be considered. The developed framework is verified by implementing all modules and a specific optimization model for the automated generation of scenarios after integrating a new product into the line-less assembly system. It can be shown, that recommended actions for adding, removing, or reconfiguring workstations are generated to support a production planner.