Production Line Energy Cost Optimization with Renewable Energy Resource Usage in a Flexible Job Shop Configuration

Ever-increasing electricity prices due to inflation has become one of the main problems for manufacturing industries to deal with, mainly in a competitive market. The usage of Renewable Energy Resources (RER)s can be a great way to mitigate the energy costs of the electricity market, by not only covering the energy consumption but also turning a profit with the surplus. This paper aims to address these issues by proposing an intelligent production line scheduling system, in a flexible job shop configuration, that focuses on reducing energy costs. It considers dynamic pricing, RERs usage and surplus selling, and complex constraints applied in the production plan. To achieve these results, a Genetic Algorithm (GA) is employed. A case study from the literature, that uses real production data, is presented which does not take into account energy selling. Results show that with the proposed GA energy costs can be further reduced by 24.6% when considering RERs surplus selling.