Worst case performance scheduling facing uncertain disruption in a continuous casting process

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

“…Hou and Li [16] study the different disturbance events that can arise in the steelmaking process classifying them attending to the origin of the event: internal to the steelshop (machine breakdowns, steel grade variations) and external to the steelshop (urgent orders, unavailability of slabs). Worapradya and Thanakijkasem [17] divide the main daily disruptions events that can happen in this environment into 4 groups: machine failure, rush orders, excessive defects during an operation and order cancellations. Tang et al [18] consider the differentiation of real-time events affecting the steel making phase between resource-related and job-related but propose also a third category, which are the quality-related (processing time changes, steel grade changes, process route changes).…”

“…However, proactive studies seem to be a more recent approach, starting on 2008 and growing in frequency during the following years, while reactive scheduling solutions are present during the whole period covered by this review. Only a few authors consider the possibility of combining both approaches in their works (hybrid approaches), such as Jiang et al [53], Worapradya et al [17,27] or Yu et al [41]. This can be explained by the focus in recent years on the application of metaheuristics and simulation solutions to reactive approaches (see Table 2) which could be motivated by the necessity of improving the solutions to be executed dynamically in real production environments.…”

“…The uncertainty factor most studied is the disturbance of the process time, due to the huge impact that it has on the final makespan of the scheduling, which is considered by most of the objective functions in the analyzed papers. Machine and equipment events, along with deviation from product specifications are also considered by several authors, while only four works [17,33,44,48] analyze the uncertainty associated directly with the orderbook and demand.…”

“…Some of these solutions are also combined with other techniques. Worapradya and Thanakijkasem [17] use Montecarlo simulation with historical data to model the stochastic variables for the potential uncertainties, proposing afterwards a robust approach to solve the worst-case scenario. Krumeich et al [42] propose a model based on Big Data, in which Complex Event Processing (CEP) techniques are applied to improve the stochastic forecasting methods by providing more information from the real environment, introducing event-based forecasting which reduce the generation of potential invalid production plans.…”