Scheduling Model for Prefabricated Component Assembly, Production, and Transportation Stage Based on GA for Prefabricated Buildings

With the development of prefabricated buildings in China, the demand for prefabricated components is also increasing. The construction schedule of prefabricated components has heterogeneity and timeliness, which makes the traditional scheduling models not applicable. In order to control the construction process and reduce costs, research is conducted on controlling the construction process of prefabricated components in prefabricated buildings. This study divides the construction process into three stages according to the construction characteristics of prefabricated buildings. The scheduling models of these three stages are established, namely assembly, production, and transportation stages scheduling models. The scheduling model of the three stages is related to each other through the duration constraints. In addition, an improved genetic algorithm is developed to solve the scheduling model of the assembly stage. Then an improved particle swarm optimization is designed to solve the scheduling model in the production and transportation stages. The results show that the minimum duration of the assembly phase was 8 days. The duration and cost of the production phase cannot be minimized at the same time. The minimum carbon emission duration and transportation cost in the transportation phase are 93.8 hours and 22516 yuan, respectively. The improved genetic algorithm tended to flatten out after nearly 180 iterations. The maximum running time of the improved particle swarm algorithm on the training set is 4.23s, the maximum hyper volume is 0.736, and the maximum anti generation distance is
$2.35\times 10 ^{-3}$
. The scheduling models of different stages and corresponding solving algorithms are effective and provide technical support for the construction process control of assembly parts. The technical contribution of this study is to optimize the genetic algorithm based on weed invasion algorithm and improve the local search ability of genetic algorithm. Then, the differential evolution algorithm is used to improve the particle swarm optimization algorithm and continuously generate new particles to replace the optimal position.