Optimization of precast Component Production Scheduling: A Comparative Study of Genetic Algorithm and Simulated Annealing

Junyong Liang; Shicen Liu; Meixue Lai; Yingmei Liao; Ziwei Wu; Yurui Yuan; Yong Liu

doi:10.54097/rp43ps08

Authors

Junyong Liang
Shicen Liu
Meixue Lai
Yingmei Liao
Ziwei Wu
Yurui Yuan
Yong Liu

DOI:

https://doi.org/10.54097/rp43ps08

Keywords:

Precast Components, Production Scheduling Optimization, Genetic Algorithm, Simulated Annealing, Flow Shop Scheduling

Abstract

The production of precast components is a core link in the industrialization of construction, and the rationality of production scheduling directly determines project costs, construction progress, and resource utilization efficiency. Faced with challenges such as intricate production processes, multiple resource constraints, and the need for flexible response to market demands, traditional scheduling methods are increasingly incompetent. To address these issues, this study explores the application of two intelligent optimization algorithms—Genetic Algorithm (GA) and Simulated Annealing (SA)—in precast component production scheduling. Based on a systematic analysis of the production process and constraints of precast components, corresponding mathematical models are established for each algorithm. Through case studies using real production data from precast component manufacturers, the effectiveness of the two algorithms is verified. The results show that both GA and SA can significantly optimize the production sequence, shorten the maximum makespan, and improve equipment utilization. Specifically, GA achieves a makespan of 45 hours with a more efficient convergence rate, while SA effectively avoids local optima and obtains a makespan of 46.8 hours. This research provides a scientific and flexible scheduling solution for precast component manufacturers and enriches the application of intelligent algorithms in the construction industry.

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References

[1] Liang, W., Zhao, Y., & Yin, X. (2023). Precast production scheduling in off-site construction: Mainstream contents and optimization perspective. Journal of Cleaner Production, 405, 137054.

[2] Ban, D. (2020). Research on the optimization method of precast concrete components production for assembled buildings [D]. Xi'an University of Architecture and Technology.

[3] Wang, R., Ban, D., & Wang, Y. (2020). Research on precast concrete component production scheduling problem considering resource constraints. Manufacturing Automation, 42(12), 63-67.

[4] Chen, J., Zhang, Y., & Gao, X. (2024). Production scheduling optimization of PC components based on improved genetic algorithm. Mechanical Design and Manufacturing Engineering, 53(1), 95-99.

[5] Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.

[6] Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.

[7] Qin, X., Zhu, Q., & Han, J. (2023). Construction scheduling optimization for integrated precast component production-transportation-assembly. Journal of Huaqiao University (Natural Science Edition), 44(3), 366-373.

[8] Chan, W. T., & Hu, H. (2002). Production Scheduling for Precast Plants using a Flow Shop Sequencing Model. Journal of Computing in Civil Engineering, 16(3), 165-174.

[9] Benjaoran, V., & Dawood, N. (2006). Intelligence approach to production planning system for bespoke precast concrete products. Automation in Construction, 15(6), 737-745.

[10] Zhang, R., Feng, X., & Ma, G. (2023). Optimization of precast components production scheduling and transportation considering carbon emission based on NSGA-III algorithm. Journal of Engineering Management, 37(2), 153-158.

[11] Phanden, K. R., Gupta, S., & Wolde, B. (2025). Optimisation of job shop scheduling problem using genetic algorithm and simulated annealing: a case study of manufacturing industry. International Journal of System Assurance Engineering and Management, (prepublish), 1-10.

[12] Zhang, Z., Fu, Y., & Gao, K. (2024). A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution. Swarm and Evolutionary Computation, 85, 101467.

[13] Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321-329.

[14] Wang, Z., & Chen, X. (2019). Research on optimization of mold arrangement in plate precast component production based on genetic algorithm. Journal of Engineering Management, 33(1), 45-49.

[15] Yang, D. (2004). Solving production scheduling problems with simulated annealing algorithm. Journal of Huanggang Normal College, 24(3), 45-47.

[16] Jing, H., & Wang, Y. (2020). Simulated annealing algorithm to optimize PSO-GA algorithm to solve flexible flow shop scheduling problem. Small Microcomputer Systems, 41(5), 996-999.

[17] Yao, G., Qin, W., & Zhou, M. (2019). Optimization of BIM technology positioning for industrial production of PC components. Journal of Civil and Environmental Engineering (in Chinese and English), 41(2), 140-146+166.

[18] Deng, H., Liu, H., & Dong, Z. (2025). Optimized genetic algorithm for solving flexible flow shop scheduling problem. Computer Knowledge and Technology, 21(2), 15-18.

[19] Chen, Z., Hu, Y., & Liu, J. (2024). Solving flexible job shop scheduling problem using improved genetic algorithm with reinforcement learning. Science, Technology and Engineering, 24(25), 10848-10856.

[20] Tian, D., & Chi, H. (2006). Hybrid genetic algorithm and simulated annealing method.