# Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning
## 1 Introduction  
An assembly line (AL) is an arrangement of workstations in a streamlined manner according to the product assembly process sequence for the organization and the arrangement of production. A workstation is an assembly unit that focuses on a specific segment of production, and the workpiece is assembled in all the workstations to form a complete product. Due to the assembly line adopting the flow operation mode, the assembly operation process is standardized, and the assembly workers are generally fixed in a single station or several adjacent stations for repeated operations, which increases the utilization rate of workers, thus greatly improving production efficiency [1]. In the two-sided assembly line (TAL), the left and right sides of the same workstation can independently execute assembly of the same product with different processes in parallel, as shown in Figure 1. These are called mated stations [2]. Compared with the one-sided AL, the TAL can effectively shorten the length of theAL, improve the utilization rate of auxiliary tools, reduce the time loss caused by the movement of workers between various stations, and reduce the transportation cost of assembly parts [2]. To balance the TAL is to distribute a group of tasks evenly to each station as far as possible under certain constraints to pursue the optimization of one or more objectives [3]. However, after the assembly line is put into operation, the original balance parameters, such as cycle time, operation content, operation time, and assembly process, may change with the improvement of assembly workers' technology, product upgrades, customer demand change, etc. In such cases, the original assembly line balance may be disrupted, prohibitingthe assembly line from being a stable, efficient, and high-quality operation, and thus greatly reducing the economic benefits of the relevant enterprises. Therefore, it is necessary to optimize and improve the original balancing scheme, which involves reassigning tasks to achieve the assembly line load balance and improve the operation efficiency of the TAL. TALBP is one of the NP-hard problems belonging to combinatorial optimization [4]. Since it was proposed in 1993 [5], it has been widely studied. The main research methods include exact algorithm, heuristic algorithm, and meta-heuristic algorithm [1; 2]. Although the exact algorithm can obtain the optimal solution, its solving speed is slow and can can be used for small-sized problems; the heuristic algorithm is fast, simple, and efficient, but the solution results cannot reach the global optimal; and the meta-heuristic algorithm is relatively fast and effective, but the iterative search process is usually time-consuming and needs to be solved iteratively for each problem case. Moreover, these traditional optimization algorithms rarely make use of historical information to adjust behavior and cannot effectively use historical solving experience for learning; hence, there is great room for improvement in terms of solving large-scale problems. In addition, different cases of problems have the same combinatorial optimization structure (the objective function or the coefficient of constraint conditions); there are only differences in specific values; for example, improving assembly workers' skills will reduce the working time of tasks, but the correlation between tasks does not change. As an artificial intelligence algorithm closer to the way of human thinking, the deep reinforcement learning algorithm has deep association learning ability compared with the traditional optimization algorithm. When solving the cases at a same scale, historical experience of different cases--that is, existing task assignment schemes--can be associated and learned, the essential information of problems can be mined, and task assignment strategies can be obtained and automatically updated to achieve efficient solutions to similar cases.