# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 5 Computational Experiments
### Experimental Results
#### 5.2.2 Results  
Three subproblem sets of J30, J60 and J120 were selected for numerical experiments, 48 examples were randomly selected from the J30 and J60 question sets, and 60 examples were randomly selected from the J120 question set for algorithm testing. Table 3 shows the calculation results of the J30, J60 and J120 test cases using the GA proposed in this research. The performance of other excellent algorithms in the J30, J60 and J120 data sets in recent years is compared to illustrate the effectiveness of the algorithm. In previous studies, the effectiveness of the algorithm was mostly measured by calculating the deviation rate between the optimal solution and the solution of the CPM. The comparison of the average deviation of the algorithm is shown in Table 4, except for J30 which is measured by the _Avg_DeV_lb_ standard, J60 and J120 are measured by the _Avg_DeV_mpm_ standard. The \begin{table}
\begin{tabular}{c c c c c} \hline _Parameters_ & _Pop_Size_ & _Max_gen_ & _Pc_ & _Pmt_ \\ \hline J30 & 100 & 500 & 0.8 & 0.01 \\ J60 & 100 & 500 & 0.8 & 0.01 \\ J120 & 50 & 1000 & 0.8 & 0.005 \\ \hline \end{tabular}
\end{table} 
Table 2: Algorithm parameter setting. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**J30 (\%)** & \multicolumn{2}{c}{**J60 (\%)**} & \multicolumn{2}{c}{**J120 (\%)**} \\ \hline _Avg\_DeV\_lb_ & _Avg\_DeV\_lb_ & _Avg\_DeV\_mpm_ & _Avg\_DeV\_lb_ & _Avg\_DeV\_mpm_ \\
0.00 & 2.23 & 9.32 & 9.61 & 30.16 \\ \hline \end{tabular}
\end{table} 
Table 3: Optimization results of PSPLIB instance. symbol \"_\" in the table indicates that the corresponding index results were not available in the original text. Table 4 illustrates that the average deviation rate increases as the scale of the problem increases, which indicates that the difficulty of solving the problem gradually increases with the scale increase. The average deviation rate of the algorithm proposed in this study in the J30, J60 and J120 problem sets are 0.00%, 9.32% and 30.16%, and the rankings are, respectively, the first, the first and the second in the compared algorithms. The J30 problem set has been accurately solved. In the previous research, many scholars' algorithms have been able to achieve a test accuracy of 0.00%. The performance of the algorithm proposed in this study on the J30 test case shows that the algorithm is effective and has certain advantages in solution stability and accuracy. The performance of the J60 test case further shows that the proposed algorithm still has high solution accuracy and stability in solving medium-scale resource-constrained project scheduling problems. The results on the J120 test case show that the proposed algorithm is still effective and relatively stable when solving large-scale solutions. Figure 7 shows the average computing time performance of the algorithm on the J30, J60, and J120 problem sets. It can be seen from Figure 7 that the calculation time of the proposed algorithm of the research increases approximately linearly with the increase in project activities. The algorithm in this study is more efficient in terms of time compared with the other algorithms that have exponentially increasing computing time [80,87,88]. Therefore, the algorithm has more advantages in solving large-scale project scheduling problems. The analysis of the above results indicated that the proposed algorithm shows a good computing performance in small and medium-scale problem sets or large-scale problem set tests. The algorithm has more stable and efficient advantages in solution quality and calculation time. The main reason is that in the design of GA, DSM and random priority are used to generate the initial population for ensuring that the initial individuals (the activity sequence of project scheduling) satisfy the constraints of predecessor logical relationship. In the genetic evolution operation of the algorithm, the design of the crossover operator and the mutation operator in this study ensures that the newly generated offspring conforms to the constraints of a predecessor logical relationship.