# Planning and Scheduling with Uncertainty in the Steel Sector: A Review
## 4 Scheduling Solutions in Steel Sector Considering Uncertainty: Literature Review
Specific agents are defined for each process step, capable of generating local schedules and use a negotiation protocol to obtain global near optimal solutions. [PERSON] et al. [38] used multiagent systems for steel dynamic scheduling. [PERSON] and [PERSON]. [26] consider a solution based on three types of agents: a scheduling agent that creates the basic schedules, a temperature evaluation agent that calculates the temperature of the molten steel, and the evaluation agent that combines results from the other agents to evaluate the viability of the schedule. [PERSON] et al. [63] present a framework of intelligent agents that detect changes in the dynamic environment and communicate the virtual system developed with the existing real production scheduling systems. Different artificial intelligence approaches include [PERSON] et al. [15] who created a knowledge-based model that tries to replicate the expert knowledge used in the rescheduling of the steelshop operations in the presence of disturbances. References summarized in Table 3 cover the proactive scheduling solutions focused on modeling the uncertainty in the steelmaking process. 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. In relation to the modeling techniques used in these solutions, about half of them (13/24) apply classic approaches like stochastic and robust programming or fuzzy systems, explained in Section 2 of this review. Some of these solutions are also combined with other techniques. [PERSON] and [PERSON] [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. [PERSON] 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. [PERSON] [41] creates a prediction model to analyze the potential disturbance delays affecting operational time of steelmaking charges and propose a prediction method to estimate abnormal conditions which reduces the frequency of the reschedules and the modifications performed to the initial schedule when readjusts are required. [PERSON] et al. [48] consider a model based on historical data to estimate the raw material demand and establish a purchasing model using on MILP. [PERSON] and [PERSON] [49] use Bayesian Networks (BN) to predict the distribution variables for production loads and production times at the different stages of a plate mill process. [PERSON] and [PERSON] [56] apply the Cross Entropy (CE) algorithm in a two-layer approach to solve the steelmaking scheduling problem, with an outer