An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns

With rising energy prices and environmental concerns, reduction of energy consumption has become a critical manufacturing focus. One appropriate way to reduce energy consumption in manufacturing systems is to develop energy-conscious optimization strategies for production planning. In a flexible machining job shop, this planning must accommodate common dynamic events, such as new job arrivals and machine breakdowns. Dynamic events could change production energy consumption, thus require plan changes in pursuit of energy consumption reduction. To this end, this paper proposes an energy-conscious optimization method in flexible machining job shops considering dynamic events. In this paper, a optimization method which updates the jobs and machine plan status when dynamic events occur is proposed. The method considers two states for machine tool energy consumption: actual machining and machine idling/stand-by. The optimization model considers the total energy consumption and makespan, and employs Non-dominated Sorting Gene Algorithm II (NSGA-II) approach to obtain a solution. The proposed method is evaluated with a test case in which a flexible machining job shop experiences new dynamic job arrivals and machine breakdowns. The results show that the proposed method is effective at adjusting the schedule in response to dynamic events.