journal article·2024-11-27Open access

Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty

Taehoon Kim, Yong-Woo Kim

Applied Sciences· Volume 14· Issue 23

Abstract

This study proposes a robust precast concrete (PC) production scheduling model for PC construction projects with schedule uncertainty by adopting a proactive scheduling approach. The proposed model consists of a PC production simulation module that simulates and evaluates the total tardiness of the schedule at a certain confidence level with contractor schedule uncertainty and a GA-based production schedule optimization module that finds an optimal schedule through iterative schedule generation and evaluation using a PC production simulation module. The experimental study shows that the proposed model can find the best schedule with 303.8 h of tardiness at a 100% confidence level, followed by NEHedd 324.7 h, basic GA 328.5 h, and EDD 335.8 h. The results of this study will help PC production schedulers to perform robustly despite contractors’ schedule changes and will thus contribute to the successful completion of PC construction projects.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Scheduling and Optimization Algorithms · Advanced Manufacturing and Logistics Optimization · Manufacturing Process and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Architectural Engineering Program, School of Architecture, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

Department of Construction Management, College of Built Environments, University of Washington, Seattle, WA 98195, USA

Funding

Seoul National University of Science and Technology

References (25)

Show source-provided references
1. Toward productivity in future construction: Mapping knowledge and finding insights for achieving successful off-site construction projects
2. Dynamic production scheduling model under due date uncertainty in precast concrete construction10.1016/j.jclepro.2020.120527 ↗
3. Reinforcement learning approach to scheduling of precast concrete production10.1016/j.jclepro.2022.130419 ↗
4. Robustness and stability measures for scheduling: Single-machine environment10.1080/07408170701283198 ↗
5. Robust and reactive project scheduling: A review and classification of procedures10.1080/00207540310001638055 ↗
6. Proactive approach to address the uncertainty in short-term scheduling10.1016/j.compchemeng.2007.08.014 ↗
7. A reactive scheduling approach based on domain-knowledge10.1016/S1570-7946(09)70348-7 ↗
8. Robust optimization model and algorithm for logistics center location and allocation under uncertain environment
9. Chan, W.T., and Wee, T.H. (2003, January 9–13). A multi-heuristic GA for schedule repair in precast plant production. Proceedings of the 13th International Conference on Automated Planning and Scheduling (ICAPS), Trento, Italy.
10. GA-based decision support systems for precast production planning10.1016/j.autcon.2010.06.004 ↗
11. Dynamic response to demand variability for precast production rescheduling with multiple lines10.1080/00207543.2017.1414970 ↗
12. Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components10.1016/j.autcon.2018.08.002 ↗
13. Dynamic production scheduling for prefabricated components considering the demand fluctuation10.32604/iasc.2020.010105 ↗
14. Hybrid rescheduling optimization model under disruptions in precast production considering real-world environment10.1061/(ASCE)CO.1943-7862.0001976 ↗
15. Green optimization for precast production rescheduling based on disruption management10.1016/j.jclepro.2023.138406 ↗
16. Chen, J., and Liu, X. (2023, January 7–9). GEP algorithm-based optimization method for PCs production scheduling under due date variation. Proceedings of the 2023 4th International Conference on Computer Engineering and Application (ICCEA), Hangzhou, China.10.1109/ICCEA58433.2023.10135444 ↗
17. Framework for modeling operational uncertainty to optimize off-site production scheduling of precast components10.1016/j.autcon.2017.10.026 ↗
18. Flowshop scheduling model for bespoke precast concrete production planning10.1080/0144619042000287732 ↗
19. Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap
20. Inventory management for new products with triangularly distributed demand and lead-time10.1016/j.cor.2015.10.017 ↗
21. Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives10.1080/09537280701530033 ↗
22. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley.
23. A genetic algorithm for flowshop sequencing10.1016/0305-0548(93)E0014-K ↗
24. Genetic algorithms for flowshop scheduling problems10.1016/0360-8352(96)00053-8 ↗
25. Heuristics for flowshop scheduling problems minimizing mean tardiness10.1057/jors.1993.3 ↗