## 2 Literature Review
(2020) proposed a rescheduling framework that integrates optimization algorithms and machine learning techniques to improve the overall production effectiveness [34]. [PERSON] et al. (2020) presented a conceptual model for a real-time decision-support system for production scheduling in the Industry 4.0 production environment with high-mix, low-volume behavior. The aim of the model is to minimize the total production process time (i.e., makespan). The developed model encompasses a hybrid of different scheduling techniques, where the best alternative will be selected using an analytical hierarchy process (AHP) analysis [30]. [PERSON] et al. (2021) Developed an Industry 4.0-based job shop scheduling model. Project management concepts were integrated into a mixed-integer linear programming model to design a dynamic automated scheduling system [38]. In this regard, the flexible job-shop scheduling problem has received more attention from researchers in the field of smart real-time production scheduling under Industry 4.0. For example, [PERSON] et al. (2019) investigated how real-time information in an industry 4.0 environment can be used to reschedule the orders in a turbulent job-shop production environment where the processing times are assumed to be variable. Several rescheduling policies were assumed and examined with the objective of improving the performance of rescheduling results within fewer rescheduling frequencies. The computational results show that the rescheduling of production processes will improve the performance of operations when the processing time variability ranges from low to medium. The results also confirmed the necessity of developing an efficient initial schedule [31]. [PERSON] et al. (2020) developed a real-time scheduling model for the flexible job-shop scheduling problem. The model utilizes different advanced scheduling strategies, policies, and techniques, and two production disturbances were addressed, namely machine availability (i.e., random breakdowns) and unexpected arrivals of new jobs. The efficiency of the developed model was also investigated under different scenarios. The obtained results confirmed that continued rescheduling (i.e., update on the initial schedule) is more much better than a fixed schedule generated initially. Finally, the computational results of his study demonstrate that the optimal utilization of real-time information of production will significantly enhance production scheduling decisions [32]. [PERSON] et al. (2018) introduced a dynamic job-shop scheduling problem framework by developing an intelligent system based on the use of Multi-Agent Systems and the Internet of Things (IoT) for real-time data collection and exchange to support decision making and to deal with disturbances in the production system [10]. Smart real-time scheduling in lean manufacturing was also addressed by [PERSON] et al. [39]. They developed a mathematical model for a smart real-time production scheduling and dispatching system based on lean concepts to achieve lean objectives where the materials flow along value stream mapping (VSM) was optimized, and thus the manufacturing lead time is minimized. Despite a large number of publications in the field of smart real-time production scheduling in industry 4.0, the adoption of industry 4.0 still requires further knowledge to be developed and specified [20]. Therefore, based on reviewing papers, there are still some research gaps that need to be addressed, for instance:  
1. Most of the conducted studies focus deeply just on a flexible job-shop production environment;
2. None of the reviewed studies investigated how scheduling influences a fully automated filling production systems where customer orders are highly customized with the target of changing production parameters to minimize makespan;
3. Few studies used linear integer programming to developed dynamic mathematical models for efficient real-time rescheduling.