## Literature Review
### Major Concerns in Current Scheduling Research  
At present, many scholars have studied a lot about the problem of continuous casting and hot rolling scheduling in steel enterprises. [PERSON] et al. [12] modeled the production planning problems of continuous casting and hot rolling through mixed integer programming, using the output of the hot rolling production planning problem as the input to the continuous casting production planning problem, with the goal of minimizing transition time and maximizing equipment utilization. And numerical experiment results showed that the proposed method is very effective in continuous casting and hot rolling planning. [PERSON] et al. [6] decomposed the continuous casting and hot rolling problem into a coupling of two subproblems. One is to allocate casts for the continuous casting machine and determine the order and start time of casts and rolling units. The other is to allocate the reheating furnace and determine the starting time for heating the slab in the furnace. The goal is to maximize the number of slabs processed in hot-charging rolling or hot direct mode and minimize the residence time of slabs in the reheating furnace, thereby saving energy. From the above research, there are relatively few multi-process optimization models considering the reheating conversion mode of the reheating furnace in current steel production. Most of the studies on reheating furnace energy consumption are focused on optimizing them from the perspective of thermal energy, including the prediction of slab performance [13], furnace temperature optimization control [3], waste heat recovery [4], combustion process control, heat transfer mechanism analysis [14], and scheduling model optimization [15]. [PERSON] and [PERSON] [13] proposed a method that can find the optimal value of the reheating furnace zone temperature under steady-state operation. Comparison of the model with experimental results shows that the heat transfer model is suitable for predicting the heating performance of slabs, but the optimization of production scheduling for reheating furnaces has been less studied. [PERSON] et al. (2016) proposed a simplified biltet gas consumption intensity (BGCI) apportionment model for steel billets based on production state partitioning and suggested recommendations and measures to realize lean energy management for reheating furnaces. [PERSON] and [PERSON] (2017) explored the possibility of improving the energy efficiency by improving the relocating of thermocouples, reducing the gap between steel billets, introducing delay logic, and so on, ultimately achieving energy savings of the reheating furnace. [PERSON] et al. (2019) considered the objectives and constraints of reheating furnace and rolling scheduling and proposed an improved simulated annealing algorithm to minimize the number of rolling units and the total heating time in the soaking pits. The research did not consider the type of heated slab. [PERSON] et al. (2018) proposed a two-stage algorithm to solve the collaborative scheduling problem between reheating furnaces and the hot-rolling process. In the first stage, only the hot-rolling scheduling problem is considered and a decentralized search is conducted. In the second stage, a heuristic algorithm based on decision trees is constructed to obtain the corresponding reheating furnace plan. [PERSON] et al. (2019) proposed a modeling method for optimizing slab scheduling and reheating furnace thermal control. By transforming the approximate model into a mixed logic dynamic model, the optimal control problem is simplified into a mixed integer programming problem. The validity of the model is verified by numerical simulation. [PERSON] et al. (2019) studied the scheduling of the reheating furnace. In response to the previous research that only considered quality and did not consider financial impact, the reheating furnace scheduling system developed in their article takes into account both product quality and process cost and achieved good results.