###### Abstract
Humans and robots can perform one or more disassembly operations collaboratively at any workstation of a disassembly line. To characterise the human-robot collaborative disassembly mode and dynamic factors, such as uncertain product quality, a task-based dynamic disassembly process model is proposed. Based on the dynamic disassembly process model, a multiobjective optimisation model of the D-HRDLB problem is developed. In the D-HRDLB problem, there is a correlation between the problems in different environments. Therefore, this study adopts the data distribution adaptation method [13] in feature-based transfer learning to generate a good initial population in a new environment. Furthermore, a transfer learning-assisted evolutionary dynamic optimisation algorithm is proposed to solve the D-HRDLB problem. To verify the effectiveness of the proposed algorithm, a new set of test instances for the D-HRDLB problem is generated, and the proposed algorithm is compared with several competitors in terms of the mean inverted generational distance (MIGD) and the mean hypervolume (MHV). Compared with the existing studies on DLBP, the main differences and contributions of this study are fourfold. 1. The D-HRDLB problem is introduced in this paper. This problem fully considers the characteristics of human-robot collaboration, and it can track and respond to the dynamic changes in the disassembly environment in real time. Compared with the traditional DLBPs, this problem is more applicable to the actual production environment in the remanufacturing enterprise. 2. A task-based dynamic disassembly process model is proposed. This model takes disassembly tasks as the basic elements instead of the traditional disassembly operations, and it can characterise the time-varying characteristics of the task, such as the task feasibility affected by the uncertain product quality. 3. A mathematical model for the D-HRDLB problem with three objectives is developed to optimise small-scale problem instances. 4. A feature-based transfer-assisted evolutionary dynamic optimisation algorithm is developed to obtain a dynamic Pareto-optimal solution set for the multiobjective optimisation of the D-HRDLB problem. The algorithm can transfer the knowledge of optimal solution sets between similar problems in different environments, thereby tracking and responding to environmental changes and obtaining the dynamic optimal solution set in a time-varying environment. The importance of this research is mainly reflected in the following two aspects. First, the human-robot cooperative disassembly line balance problem proposed by us is a dynamic multiobjective optimisation problem considering the uncertainty of product quality, which is more in line with the actual disassembly environment. Second, the optimisation algorithms used to solve the DLBP problem usually target a deterministic disassembly environment. The algorithm proposed in this study can effectively deal with the changing disassembly environment, and it has more practical significance. The remainder of this paper is structured as follows. Section 2 discusses the related work from two aspects: the disassembly process model and the DLBP under uncertainty. Subsequently, Section 3 presents and defines the related concepts of the task-based dynamic disassembly process model. The concept of the D-HRDLB problem is explained, and a three-objective optimisation model for this problem is developed in Section 4. Section 5 delivers insights into the implementation details of the proposed algorithm, including the basic algorithm framework, initial population generation, solution encoding and decoding, and solution variation. Afterwards, experimental studies are provided in Section 6. Finally, Section 7 concludes the study and highlights possible future research avenues.