# Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning
## 2 Literature Review  
### Two-Sided Assembly Line Balance Oriented to Load Balancing  
According to different production stages and research objectives, TALBPs are mainly divided into two categories [8]. The first type of balancing problem occurs in the design and planning stage, the aim was to explore that how to use the minimum number of workstations and/or stations to achieve production under the premise of a given cycle time. For this type problem and its variants, many scholars have designed exact algorithms [9; 10], heuristic algorithms [11; 12], and meta-heuristic algorithms [13; 14]. With the  
Figure 1: Two-sided assembly line. assembly line officially put to use, some potential problems (task assignment, resource allocation, etc.) that could not be predicted in time in the design stage of the assembly line become increasingly obvious. Moreover, assembly workers' skills, product configuration, and customer demands may also change, and thus the task content and/or operation time changes and the original balancing plan needs to be further optimized. The second type of balance problem occurs in this stage, which is the problem of how to obtain a better cycle time by optimizing task assignment under the premise of definite workstations [15, 16, 17]. However, the above studies did not consider load balancing, which is the initial goal of balance research [5] and the source of maintaining the stable and efficient operation of the assembly line [18, 19]. Considering the importance of load balancing, some scholars have specifically studied this kind of problem and defined it as the third type of balance problem--that is, how to optimize task assignment and balance the load of all stations as much as possible under the premise of fixed cycle time and line length. [PERSON] and [PERSON] [20] used the assembly line smoothness index to measure load balancing and combined it with assembly line efficiency as optimization objectives to build a mathematical model of TAL load balancing. The minimum deviation method (MDM) was used to combine multi-objective problems into single-objective problems, and a tabu search algorithm was proposed. [PERSON] and [PERSON] [21] designed a nonlinear integer programming model to maximize the smoothness of the line for the third-type TALBP and solved it using Lingo. [PERSON] and [PERSON] [22] designed a harmonic search (HS), combining non-inferior sorting rules for TALBP considering zone constraints. The goal was to optimize the production efficiency and balance the load of stations. [PERSON] et al. [23] used an improved version of teaching and learning optimization (ITLBO) to balance multi-constraint and multi-objective TAL. The goals were the optimization of assembly line efficiency, assembly line smoothness, and total associated unit production costs. [PERSON] et al. [24] proposed an iterative local search algorithm (ILS) to realize the load balancing of TALs. A heuristic algorithm was applied to obtain the better initial population; the local search and disturbance factors were used iteratively until a local optimal solution was found. This algorithm also applies the priority decoding method based on the combination of orientation selection rules and task selection rules to reduce the load difference between the left and right stations of a workstation as far as possible so as to ensure that the sequence-related waiting time is reduced to a certain extent. [PERSON] et al. [25] used a hybrid algorithm based on variable neighborhood search and gravity search for the TALBP considering zone constraints to achieve load balancing among workstations and reduce the series-dependent completion time of tasks as much as possible. [PERSON] et al. [26] provided the dictionary bottleneck hybrid TALBP, balancing the load of all workstations by gradually minimizing the weighted load of the workstations with the maximum load and finally achieving the load balancing of all workstations on the assembly line.