## 6 Computational Experiments
### Experimental Settings
However, the distribution of reference vectors may not match the irregular POFs, resulting in wasted vectors that have no reasonable solutions nearby and bad vectors that may bias the search direction. Therefore, we choose the adaptive reference vector-guided RVEA-iGNG instead of MOEA/D as a representative algorithm for decomposition-based MOEAs. In addition, the fine performance of NSGA-II and IBEA in solving DLBPs has been proven by [PERSON] et al. [5]. Consequently, choosing these three MOEAs can strengthen the confidence level of the experimental results. In this paper, MIGD and MHV are used to quantify the performance of B-DMOEA. They are variants of IGD [55] and HV [53] for dynamic multiobjective optimisation algorithms. MIGD is defined as the average value of the IGD values in all environments [46]. MHV is defined as the average value of the HV values in all environments. Note that the exact Pareto font of the D-HRDLB problem is unknown. Therefore, we use a collection of all the solutions, which we obtained by executing all the runs for all the algorithms and picking the nondominated solutions from the collection as the ideal solution set to obtain the approximate Pareto front. We normalise the solution set obtained by each algorithm \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Uncertain Disassembly Operations** \\ \hline TD-TAOG 1 & 9 \\ TD-TAOG 2 & 20, 21, 22, 23 \\ TD-TAOG 3 & 6, 11 \\ TD-TAOG 4 & 7, 11 \\ TD-TAOG 5 & 9, 10, 11 \\ TD-TAOG 6 & 6, 7, 17, 18, 19, 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Disassembly operations that may be in the damaged or missing state. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problem** & **Scale** & **Products** & **Tasks** & **Problem** & **Scale** & **Products** & **Tasks** \\ \hline S1 & Small & \(1,2\) & 241 & L6 & Large & \(1,3,5\) & 375 \\ S2 & Small & \(1,3\) & 217 & L7 & Large & \(1,3,6\) & 435 \\ S3 & Small & \(1,4\) & 164 & L8 & Large & \(1,4,5\) & 322 \\ S4 & Small & \(1,5\) & 232 & L9 & Large & \(1,4,6\) & 382 \\ S5 & Small & \(1,6\) & 292 & L10 & Large & \(1,5,6\) & 450 \\ S6 & Small & \(2,3\) & 310 & L11 & Large & \(2,3,4\) & 400 \\ S7 & Small & \(2,4\) & 257 & L12 & Large & \(2,3,5\) & 468 \\ S8 & Small & \(2,5\) & 325 & L13 & Large & \(2,3,6\) & 528 \\ S9 & Small & \(2,6\) & 385 & L14 & Large & \(2,4,5\) & 415 \\ S10 & Small & \(3,4\) & 233 & L15 & Large & \(2,4,6\) & 475 \\ S11 & Small & \(3,5\) & 301 & L16 & Large & \(2,5,6\) & 543 \\ S12 & Small & \(3,6\) & 361 & L17 & Large & \(3,4,5\) & 391 \\ S13 & Small & \(4,5\) & 248 & L18 & Large & \(3,4,6\) & 451 \\ S14 & Small & \(4,6\) & 308 & L19 & Large & \(3,5,6\) & 519 \\ S15 & Small & \(5,6\) & 376 & L20 & Large & \(4,5,6\) & 466 \\ L1 & Large & \(1,2,3\) & 384 & L21 & Large & \(1,2,3,4\) & 474 \\ L2 & Large & \(1,2,4\) & 331 & L22 & Large & \(1,2,3,5\) & 542 \\ L3 & Large & \(1,2,5\) & 399 & L23 & Large & \(1,2,4,5\) & 489 \\ L4 & Large & \(1,2,6\) & 459 & L24 & Large & \(1,2,4,6\) & 549 \\ L5 & Large & \(1,3,4\) & 307 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Details of the 39 instances. with the approximate Pareto front and calculate the corresponding MIGD and MHV based on these normalised values. The reference point is set to 1.1 times the nadir point of the approximate Pareto front. For each problem instance, each algorithm is executed with ten independent runs to obtain the mean and standard deviation values of each metric that evaluates the algorithm. The size of the population in all the algorithms is set to 150. In each environment, the presearch, single-objective search, and multiobjective search are repeated for 3, 3, and 30 generations, respectively. The kernel function in Tr-DMOEA and B-DMOEA selects the linear kernel function, while the dimensionality is set to 100, and the weight \(\lambda\) of the regularisation term is set to 0.1. Other parameters are set according to the suggestions given by the developers.