LAUSR

With the surging energy cost and environmental impacts, strategies to achieve energy-efficient production have attracted increasing concerns of the manufacturing enterprises. For the fact that most manufacturing systems operate in a dynamic and nondeterministic environment, rescheduling strategies may be beneficial as it serves for adaption of initial schedule to dynamic events. Besides that, the development of modern information technology in manufacturing practice enables the flexibility of production toward alternative process plans. However, very little research has focused on the rescheduling problem integrated with process planning for energy saving. Hence, this work undertakes this challenge by proposing rescheduling decision mechanisms in response to two typical dynamic events with alternative process plans for energy-efficient flexible job shops. More specifically, by modeling the energy consumption of the flexible manufacturing system, the problem is first formulated as a mixed-integer programming optimization model. Rescheduling mechanisms for both new job arrivals and machine tool breakdowns are then designed, based on which a rescheduling algorithm is proposed in the form of a heuristic framework. The significance of the proposed algorithm is exemplified by a comparative case study under various scenarios. Note to Practitioners—The complex process plan selection and dynamic events in flexible job shops make the energy-aware schedule decision a challenging problem. Rescheduling addresses this issue, however, most existing rescheduling algorithms assume that only one process plan is given with a predetermined process route and machine tool allocation. This reduces the effectiveness of energy-saving for such a dynamic and flexible manufacturing system. This article, for the first time, to the best of our knowledge, proposes rescheduling decision mechanisms to generate schedules that can adapt to dynamic events and are energy-efficient with process plan flexibility. It may assist decision-makers to provide more practical and applicable energy-efficient schedules for flexible job shops, particularly when dynamic variations of the production environment occurred frequently.