# Planning and Scheduling with Uncertainty in the Steel Sector: A Review
## 5 Discussion and Future Research  
Planning and scheduling under uncertainty in the steel industry has been a topic of interest in recent years. Reactive approaches have been researched along the past 15 years, while proactive solutions have gained more attention during the latest period (2013 onwards). Due to the relative novelty of the application of proactive techniques in this sector, hybrid solutions which integrate aspects from both approaches are not quite common yet. Considering which part of the complete steel industry production has been more studied by the different authors, most of the papers reviewed in the present work are focused on the primary segment of steelmaking (specially the casting process). There can be several reasons that explain this:  
* Primary steelmaking is the process in which disruptions and uncertainty have a greater impact on the scheduling operations. Any disruption in the planning will affect not only the installation or machine subject to the uncertainty, but also all the following downstream processes required for the job delayed or cancelled. * Due to the nature and the complexity of the primary steel making process itself, the number of uncertainty factors that are present in this step is higher than in other finishing processes. This situation causes the need to propose more robust scheduling solutions. In terms of uncertainty factors, most of the studies focus on the disturbances affecting processing times, while paying less attention to other elements such as machine related events and deviations from product specifications. There can be a couple of reasons to explain this interest in the literature to focus on this factor:  
* Many of the papers analyzed use the minimization of the makespan as the objective function to optimize the scheduling. Not being capable of properly represent the process time of the different jobs used to calculate this makespan objective will have an important impact on the realization of the calculated scheduling. * The existing techniques (such as forecasting) and the available data from the process environments can be better suited to model an accurate estimation of the process times, instead of detecting potential disruptions on machines availability or product composition. Considering the time horizon of the problem solved, most of the papers analyzed are focused on short and mid-term scheduling scenarios, while a very small number propose a solution for long term planning problems. This could be explained by the high quantity of reactive approach solutions and the huge impact that uncertainty has on short and mid-term scenarios, making this type of problems more appealing for the research community. Future research in this field should focus on increasing the efficiency of the reaction capacity of the industry to provide realistic schedules. Real production environments require fast solutions to readapt to the disturbances in their operations. This increase in the scheduling efficiency should come from several sources:  
* Growing implementation of Industry 4.0 and digitalization paradigms in the steel industry shall improve the level of control over the manufacturing process [42]. This will provide access to additional new data and information not available before that should lead to a better understanding of the potential disruptions. New techniques based on new trends like Big Data and the Internet of Things will create better models to predict uncertainties, providing the opportunity for better proactive solutions. * Evolution of techniques used in rescheduling solutions (specially metaheuristics) should allow for faster time response on the search of an improved reschedule upon the apparition of a disruption. * Integration of both proactive and reactive solutions into hybrid solutions.