## 4 Experiments and Discussion  
In order to verify the effects of the proposed TSCS-BD, nine large-scale inter-cell scheduling problems of different sizes were generated randomly. The results were compared with those yielded by a CS and two efficient large-scale optimization algorithms, a small-world genetic algorithm (SWGA) [15] and restart particle swarm optimization with  
Figure 5: Neighborhood structure construction. Figure 6: Crossover operation of machine encoding segments. Figure 7: Mutation operation of machine-encoding segments. velocity modulation (RPSO-VM) [40]. To verify the effects of the proposed bottleneck-degree guidance for the tabu search neighborhood structure, the results were compared with those yielded by an improved cuckoo search algorithm with general neighborhood structure (TSCS) and an improved cuckoo search algorithm with node-load guidance for neighborhood structure (TSCS-NL). Parameters for the experiments were set as follows. Parts, machines, and manufacturing cells were randomly set from the ranges of 50-90, 30-50, and 7-12 respectively. Exceptional parts were randomly set from 15-25. Operations of parts were uniformly integer-distributed with range of [3; 6]. The processing times of operations were integers and obeyed normal distribution of \(N\) (30, 5). Each exceptional part had from three to seven alternative flexible routes for inter-cell movements. The experiments and parameters are shown in Table 3.  
L1 is taken as an example; machines in manufacturing cells are shown in Table 4. Parts, their candidate manufacturing cells, candidate processing machines, and processing time are shown in Table 5. For example, operations of part /2 are processed on machines \(m_{1}\), \(m_{3}\), \(m_{4}\), and \(m_{5}\) of manufacturing cell \(U_{1}\) and the processing times are 27, 33, 34, and 28 min respectively. Distances between machines in two different cells are shown in Figure 8. For example, the transportation time for parts between manufacturing cells \(U_{1}\) and \(U_{2}\) is 36 min. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cell** & **Machines** & **Cell** & **Machines** & **Cell** & **Machines** & **Cell** & **Machines** \\ \hline \(U_{1}\) & \(1,2,3,4,5\) & \(U_{2}\) & \(6,7,8,9\) & \(U_{3}\) & \(10,11,12,13\) & \(U_{4}\) & \(14,15,16,17\) \\ \(U_{5}\) & \(18,19,20,21,22\) & \(U_{6}\) & \(23,24,25,26\) & \(U_{7}\) & \(27,28,29,30\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Machines in manufacturing cells. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parts** & **Manufacturing Cell, Processing Machines** & **Processing Time (min)** \\ \hline \(l_{1}\) & \(U_{1}(M_{1},M_{2},M_{3},M_{4},M_{5})\) & \((29,31,26,35,37)\) \\ \(l_{2}\) & \(U_{1}(M_{1},M_{3},M_{4},M_{5})\) & \((27,33,34,28)\) \\ \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(l_{8}\) & \(U_{7}(M_{27},M_{82},M_{29},M_{30})\) & \((27,31,22,34)\) \\ \(l_{90}\) & \(U_{7}(M_{27},M_{82},M_{29},M_{30})\) & \((28,32,28,25)\) \\ \hline \hline \multicolumn{2}{c}{Exceptional parts} & Manufacturing Cell, Processing Machines & Processing Time (min) \\ \hline \(l_{5}\) & \(U_{1}(M_{1},M_{2},M_{3})+\begin{pmatrix}U_{3}(M_{11})\\ U_{4}(M_{16})\\ U_{8}(M_{20})\end{pmatrix}+U_{1}(M_{4})\) & \((25,\ 27,\ 27)+\begin{pmatrix}25\\ 27\\ 36\end{pmatrix}+39\) \\ \(\vdots\) & \(\vdots\) \\ \(l_{41}\) & \(U_{6}(M_{23},M_{24},M_{5})+\begin{pmatrix}U_{1}(M_{3})\\ U_{2}(M_{7})\\ U_{8}(M_{20})\\ U_{7}(M_{28})\end{pmatrix}+\) & \((27,\ 24,\ 24)+\begin{pmatrix}31\\ 25\\ 26\\ 33\end{pmatrix}+32\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parts processing time. Parameters for TSCS-BD, TSCS-NL, and TSCS were set as follows: nest size \(N=50\), step factor \(\alpha=1\), tabu rate \(p_{a}=0.1\), length of tabu table \(L=10\), times of iteration of tabu search \(I_{T}=6\). In SWGA, population size \(N=50\). In RPSO-VM, population size \(N=50\), inertia weight \(W=0.5\), learning factor \(C_{1}=C_{2}=1.5\). In CS, nest size \(N=50\), step factor \(\alpha=1\), nest detection rate \(p_{a}=0.1\).