###### Abstract
The human uncertainty caused by this mode and the inherent EOL product quality uncertainty result in DLBP being a dynamic optimisation problem. Additionally, the uncertainty of product quality leads to possible differences between batches. We define a batch as a disassembly environment, with multiple different batches corresponding to multiple disassembly environments, constituting a dynamic disassembly environment in terms of time sequence. To simplify the problem, we assume that the product state remains constant in a single disassembly environment. Therefore, this paper studies the dynamic human-robot collaborative disassembly line balancing (D-HRDLB) problem so the constructed HRDL can track these uncertain factors and calculate the optimal solutions in the new environment. The literature lacks a study on the D-HRDLB problem, and the existing theories in the disassembly field are not well applicable to the problem. In the DLBP literature, the task precedence diagram (TPD) [6] and the transformed AND/OR graph (TAOG) [7] are two widely used disassembly process models for modelling disassembly operations and the precedence relationship between operations. Both models assume that the product information extracted from the original CAD model is known and fixed. This assumption is derived from and only applies to the assembly scenario. The disassembly scenario does not hold because the product quality is uncertain. A disassembly operation is the division of a subassembly into two subassemblies, representing a feasible cut-set on the connection diagram of the product. A disassembly operation includes the disconnection tasks of all edges in its cut-set and various auxiliary tasks. Generally, one disassembly operation corresponds to multiple feasible task sequences. Existing disassembly process models, such as the TPD and the TAOG, take the disassembly operation as the basic element, which makes it difficult to characterise all feasible task sequences for disassembly operations. Moreover, these disassembly process models are static or quasi-static models, and they do not consider the time-varying nature of attributes, such as the feasibility of the task sequence that is affected by the uncertain product quality. To bridge this gap, this study proposes a dynamic disassembly process model based on tasks. This proposed model can characterise all feasible task sequences of disassembly operations and the time-varying characteristics of tasks affected by uncertain product quality and human capabilities. In the uncertain DLBP literature, most of the existing methods dealing with uncertainty propose some nondeterministic models and algorithms based on statistical theories, such as stochastic numbers [8], fuzzy numbers [9], and interval numbers [10], for some uncertain parameters (e.g., the disassembly operation processing times). These methods need to presuppose appropriate statistical functions or know some statistical information, which makes them difficult to implement in industrial applications. Moreover, they cannot track multisource uncertain factors, such as product quality and human capabilities in real time. Therefore, it is difficult to respond to environmental changes. To fill this gap, this study proposes a solution method for the D-HRDLB problem based on evolutionary dynamic optimisation [11]. Evolutionary optimisation [12] is a metaheuristic method based on population evolution that is suitable for solving multiobjective combinatorial optimisation problems with irregular Pareto fronts, such as the DLBP. Evolutionary dynamic optimisation is based on evolutionary optimisation by further adding fast convergence strategies, such as generating a good initial population, so the optimal solution set changing with the dynamic environment can be obtained in time. This dynamic optimisation method can track and respond to various environmental uncertainties over time. At present, there is no research on dynamic DLBPs using this method. From the challenges described above, this paper examines the D-HRDLB problem in detail.