Production Plan Rescheduling for Machine Breakdown Events using a Genetic Algorithm

Manufacturing businesses rely on tight monetary expenses from energy costs and machine breakdown damages in order to remain competitive in the global market. Accordingly, the premise of the present paper is to propose an intelligent flexible job shop rescheduling system, using a Genetic Algorithm, to minimize energy costs and handle unexpected machine breakdowns. Energy cost minimization is accomplished by efficiently utilizing energy price volatility, available generated energy, and constraints imposed in the production plan. For unexpected machine breakdowns, a rescheduling processing data module is proposed focusing on rescheduling production plans by reshifting tasks from broken-down machines to other available machines. A case study with real-production data from the literature is used as the basis to simulate a machine breakdown event scenario. Results show that the proposed system was capable of shifting tasks away from a broken-down machine and into the other machines, while only increasing the energy costs by 4.2%.