Joint Optimization of Production and Maintenance for Effective Manufacturing Using a Genetic Algorithm

While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing industry. Maintenance activities can cost between 15% and 70% of the price of goods sold, thus it is essential to optimize both energy and maintenance costs. The purpose of this paper is to propose a production line optimization system that focuses on reducing the total costs (i.e., energy and maintenance costs) while also taking into account dynamic pricing, Renewable Energy Resource (RER) usage, and constraints applied in the production plan. In the proposed system, tasks and maintenance activities are scheduled by using a Genetic Algorithm. To validate the proposed system, a baseline scenario that uses real production data is considered. The obtained results show that the system is able to comply with imposed maintenance hours while also minimizing costs by shifting tasks to higher RER generation and lower energy price times, while the opposite is done to maintenance activities.