# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 2 Literature Review  
### Resource-Constrained Project Scheduling Problem  
Project scheduling is a complex process involving multiple resources and activities that need to be optimized. Resource constraints have a wide range of impacts and inevitable obstacles during a project implementation because unreasonable resource allocation can easily lead to certain problems such as construction delays and budget overruns [26]. Therefore, RCPSP is an important and challenging problem in theoretical and practical application research [3; 27]. RCPSP is defined as scheduling a set of activities linked by priority relationships to minimize project duration while meeting priority and resource constraints [28]. This issue is a typical combinatorial optimization problem and belongs to the NP-hard problem [29; 30]. In previous research, the resource-constrained project scheduling problem has received great attention [31; 32; 33]. [PERSON] et al. [34] used mixed integer programming to optimize the integration of project scheduling and material supply by taking the material and project related costs of a single project as the objective functions. [PERSON] et al. [35] proposed a new algorithm for the rush problem of repetitive construction projects. The calculation examples show that the algorithm can easily identify various types of control subprocesses and obtain the exact solution of the corresponding minimum total cost with a small amount of calculation. [PERSON] et al. [36] studied the combination of a multimode project scheduling problem and material ordering, and used multiobjective evolutionary algorithms, including intensity Pareto evolution II, multiobjective particle swarm optimization and multiobjective evolution based on decomposition, to solve the developed three-objective problem. [PERSON] et al. [37] proposed an improved critical chain technology for solving resource-constrained multiproject scheduling by studying the resource flow characteristics of single project, multiproject and activity. GAs are based on biological evolution compared with the other algorithms. As a meta-heuristic search optimization method to effectively solve RCPSP, GA has good convergence, and its search space is not limited. When the calculation accuracy requirements are clear, GA calculation time is less. Therefore, GA is more widely favored by research scholars. [PERSON] et al. [38] established a multimode material ordering resource constrained project scheduling model based on the reward and punishment mechanism, and designed a hierarchical search hybrid GA to solve the problem. The result of the model showed good performance. [PERSON] et al. [39] proposed a GA with two crossover operators to solve the problem based on the technical constraints and resource constraints of project activities for effectively shortening the project duration. The GA considers the critical degree of resources and improves the resource utilization rate on the premise of ensuring the completion quality. With the development of resource-constrained project scheduling problem, many research results have been obtained. The GA, which is widely used in the field of project scheduling, has many excellent characteristics. The spatial solution search performance and calculation ability of GA are excellent, and its robustness is high. It provides a new and effective way to solve complex optimization problems [40; 41]. However, the traditional GA often has the defects of premature convergence and local optimum. In some previous studies, the initial population is often random for the design of GA, regardless of the priority logic relationship between project activities, resulting in a large number of infeasible solutions, which greatly increases the calculation time of the algorithm. The unreasonable design of genetic operation mode also reduces the efficiency of the algorithm to a certain extent.