Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns - Ghaleb, Mageed; Zolfagharinia, Hossein; Taghipour, Sharareh (Computers & operations research, 2020)
•The use of real-time information can significantly improve scheduling decisions.•Baseline schedules quality is essential for the quality of the realized schedules.•Both event-driven and continuous rescheduling policies show similar performance.•The event-driven rescheduling policy is more computationally efficient.•Complete-rescheduling policy performs better than left/right shifting policy.•The predictive-reactive and proactive–reactive models show similar performance. The utilization of real-time information in production scheduling decisions becomes possible with the help of new developments in Information Technology and Industrial Informatics, such as Industry 4.0. Regardless of the beliefs that the availability of such information will enhance scheduling decisions, several questions and concerns have been reported. One such question is to what extent can the availability of real-time information enhance scheduling decisions? Another concern is how can such information be utilized to advance scheduling decisions and when should it be used? Moreover, there is a general assumption that continuous rescheduling using real-time system updates is beneficial to some extent. However, this general assumption has not been extensively investigated in complex manufacturing systems, such as flexible job shops. Therefore, in this paper, our objective is to study the above-mentioned research questions by developing real-time scheduling (RTS) models for the flexible job-shop scheduling problem (FJSP) with unexpected new job arrivals and machine random breakdowns. We investigate how real-time updates on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations can be utilized to generate new schedules (i.e., rescheduling). The performance of the developed RTS models is also investigated under different settings for shop-floor events, different rescheduling strategies, rescheduling policies, and scheduling methods. Lastly, results, conclusions, and several promising research avenues are provided.