## 2 Related Work
### Related Work for Machining Line Balancing Problem  
At present, flexible production lines based on advanced manufacturing equipment, such as machining centres, turning-milling combinations, and multi-axis motion, can be competent for the processing technology of most complex mechanical parts. However, on a production line consisting of multiple flexible machines and equipment, time-consuming bottlenecks often occur. The machine or equipment that undertakes the bottleneck process determines the rhythm of the entire production line and affects the production capacity of advanced flexible production lines [15]. Balancing the processing tasks assigned to parts among multiple machines and equipment such that the number of processing tasks allocated to each machine or equipment is similar eliminates the bottleneck process, which has always been one of the challenges faced by the manufacturing industry. In this study, a typical complex box part containing 73 machining tasks was used to evaluate the process-balance distribution of the machining line. Production lines are designed for mass production, the assembly, disassembly, or processing of products. Manufacturing production lines can be divided into assembly and disassembly, machining, and petrochemical metallurgical process lines. Many studies have been conducted on the assembly line/disassembly line balancing problem, and intelligent optimisation algorithms have been applied to the assembly line and disassembly line balancing problem, achieving good results [16; 17; 18; 19]. The optimisation goal of the machining line balancing problem (TLBP) is to balance the processing time of each station in order to reduce equipment idle time and minimise the production line cycle time [20], the number of stations [21; 22], and the production line cost [10]. Most studies on TLBPs have focused on the assumption that parts are machined by single or multi-spindle machines [11; 23; 24]. [PERSON] et al. [23] proposed the distribution of the machining tasks of parts to different workstations in a subset. [PERSON], [PERSON], and [PERSON] [25; 26] studied the procedure to distribute all manufacturing process tasks of a part equally among various workstations and to reduce the total number of processing equipment. [PERSON] et al. [27] considered the processing line balance objective as the minimisation of non-productive time and proposed ant colony optimisation to solve it. [PERSON] et al. [28] sought to minimise the cost of the TLBP within the feasible solution constraints. [PERSON] et al. [29] regarded the part clamping and positioning scheme as the clustering constraint of the machining elements, considered the machining auxiliary elements such as tool change and machining direction, and used ant colony optimisation to optimise and improve. [PERSON] et al. [30] proposed an improved genetic algorithm for the production line balance problem of mixed-flow parts in part families. [PERSON] et al. [31] used an improved genetic algorithm to study the multi-objective optimisation balance problem of part machining lines. [PERSON] et al. [32] proposed exclusion and inclusion constraints for the uncertainty TLBP and used a Heuristic method to solve this problem. [PERSON] et al. [33] also studied the uncertainty TLBP, but the 'and' and 'or' constraints are not considered. [PERSON] [34] studied this problem with parallel machines and a simple but effective genetic algorithm is proposed for solving this problem. [PERSON] et al. [35] studied this problem by the criteria of minimisation of machining times and costs and developed and compared two models of analogous manufacturing systems. It can be seen from the above literature research that the current research trend is to consider more complex constraints or a probability model based on the random processing time of tasks.