## 6 Computational Experiments  
### Experimental Settings  
The TD-TAOG and the D-HRDLB problem are studied for the first time in this paper, so it is necessary to generate new problem instances for algorithm comparison. By adding a task graph for each normal node in the TAOGs of six realistic EOL products from the literature, six new TD-TAOGs are generated. They are named TD-TAOG 1 [7], TD-TAOG 2 [49], TD-TAOG 3 [45], TD-TAOG 4 [50], TD-TAOG 5 [51], and TD-TAOG 6 [51]. These six products are disassembled on a human-robot collaborative disassembly line. The number of workstations is set to three, and the number of optional robots and humans is set to 24 and 10, respectively. The range of the number of robots and humans that can be allocated in each workstation is [1, 4] and [1, 3], respectively. The processing times of tasks by robots and humans are randomly generated in [0.7-\(t_{ub}\), 1.3-\(t_{ub}\)] and [0.4-\(t_{ub}\), 0.6-\(t_{ub}\)], respectively, where \(t_{ub}\) is the referenced task processing time. In this section, we build dynamic disassembly environments based on the first uncertainty, which is the uncertain product quality. Specifically, uncertain product quality may change the states of some disassembly operations, which may cause the corresponding task sequences to be infeasible. Therefore, it is necessary to reselect feasible task sequences for these operations. Table 4 describes this dynamic factor. In Table 4, the disassembly operations that may be in a damaged or missing state in each TD-TAOG are given. For example, in the first environment (\(\ell=0\)), disassembly operation nine in TD-TAOG 1 is damaged, while operations 20 and 21 are missing, and the remaining operations are in a normal state. In the second environment (\(\ell=1\)), operation nine in TD-TAOG 1 is missing, while operations 21 and 23 are damaged, and the rest of the operations are normal. We set up eight different environments (\(\ell=0,\ldots,7\)) for each D-HRDLB problem instance. The disassembly operations in a damaged or missing state in each environment are randomly determined based on Table 4. A set of D-HRDLB problem instances, including small-scale and large-scale instances, are generated based on the above settings. In the set, 15 small-scale instances are composed of any two of the six TD-TAOGs, and 24 large-scale instances are composed of any three to four TD-TAOGs. The details of the 39 instances are shown in Table 5.  
The proposed B-DMOEA is an evolutionary dynamic optimisation algorithm that is assisted by feature-based transfer learning. In fact, any population-based metaheuristic, including evolutionary approaches, such as genetic algorithms and swarm intelligence-based methods (including particle swarm optimisation algorithms), can be embedded in the B-DMOEA. However, this study mainly focuses on the design of the evolutionary dynamic optimisation method. Therefore, three representative MOEAs, including NSGA-II [48], RVEA-iGNG [52], and IBEA [53], are selected, leading the evolution via the Pareto criterion, decomposition-based criterion, and indicator-based criterion, respectively. Thesethree MOEAs are embedded in the Tr-DMOEA proposed by [PERSON] et al. [46] and our B-DMOEA to analyse the performance difference between B-DMOEA and Tr-DMOEA in solving the D-HRDLB problem. The three algorithms embedded in Tr-DMOEA are called Tr-NSGA-II, Tr-REVA-i, and Tr-IBEA, respectively, and the three algorithms embedded in B-DMOEA are called B-NSGA-II, B-REVA-i, and B-IBEA, respectively. It should be noted that few evolutionary dynamic optimisation algorithms are assisted by feature-based transfer learning, such as Tr-DMOEA and B-DMOEA. Therefore, Tr-DMOEA is chosen as the competitor of B-DMOEA in this study. Our previous work shows that the POFs of combinatorial optimisation problems, such as the D-HRDLB problem, are generally irregular [5]. Traditional decomposition-based evolutionary algorithms, such as MOEA/D [54], assume that the distribution of reference vectors can be predefined.