## 2 Literature Review
### Disassembly Line Balancing under Uncertainty  
Although there is a high level of environmental uncertainty in the DLBP, few studies in the DLBP literature consider the uncertain environment. These studies follow the idea of statistical modelling; establish nondeterministic models for specific uncertain parameters, such as the operation processing time, demand, revenue, and workload at the workstations; and use stochastic numbers [22; 23; 24], fuzzy numbers [25; 26; 27], interval numbers [10], and other nondeterministic approaches to address uncertainty in DLBP models. [PERSON] et al. [28] studied a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, using only the knowledge of the mean, standard deviation, and upper bound of stochastic task processing times. Based on the characteristics of the DLBP, a method based on fuzzy set theory is developed to evaluate the performance scores and determine the ranking of disassembly tasks [27]. Some of these studies establish relationships between uncertain product quality and one of the above uncertain parameters. For instance, [PERSON], and [PERSON] [29] studied DLBP considering different product quality categories. It was assumed that the operation processing time under different categories was a random variable obeying a normal distribution. [PERSON], [PERSON], and [PERSON] [30] studied the relation between the condition of a subassembly and the disassembly revenue, and they used a stochastic approach to model revenue to solve the nondeterministic disassembly sequence planning. [PERSON], [PERSON], and [PERSON] [31]also established a link between product condition and revenue, creating a disassembly line that can handle product quality uncertainty. [PERSON] et al. [32] established a connection between operation processing time and product condition based on a probability-weighted approach. These studies need to presuppose appropriate statistical functions or know some statistical information. Their time-varying abilities are limited to updating statistics, such as probability. Moreover, they are based on disassembly operations rather than tasks, making it challenging to estimate uncertain parameters in real time. In addition, [PERSON] and [PERSON] [33] and [PERSON] et al. [34] studied the DLBP in the case of disassembly operation failure and supply change, respectively, but they did not consider the uncertainty of product quality. Although some existing research considers the uncertainty of product quality, they usually transform the uncertain problem into a deterministic one, which cannot effectively deal with the dynamic environment in actual disassembly. The D-HRDLB problem proposed in this paper combines multiple disassembly environments into a set of dynamic environments in time order, which is closer to the real-life disassembly scenario. Research on the human-robot collaborative disassembly line has just started in recent years. [PERSON] et al. [35] studied the disassembly information model of human-robot collaborative disassembly considering the safe strategy. Likewise, [PERSON] et al. [36] improved the safety of the operators working in a collaborative workspace. [PERSON] et al. [37] pointed out that human-robot collaborative solutions allow operators to coexist and interact safely with robots of various payloads. Additionally, [PERSON] et al. [38] and [PERSON] et al. [39] studied the influence of the distance between humans and robots and human fatigue on the operation processing time, belonging to the scope of deterministic disassembly planning. Most human-robot collaborative assembly studies adopt timeline-based planning approaches to cope with human uncertainty. However, even if based on timelines, most planners cannot manage uncontrollable events and human factors in real time [3]. Therefore, this study develops a solution method for the D-HRDLB problem based on evolutionary dynamic optimisation to track environmental changes and obtain the dynamic optimal solution set in real time.