Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm
Source abstract
Abstract
Renovation is known to be a complicated type of construction project and prone to errors compared to new constructions. The need to carry out renovation work while keeping normal business activities running, coupled with strict governmental building renovation regulations, presents an important challenge affecting construction performance. Given the current availability of robust hardware and software, building information modeling (BIM) and optimization tools have become essential tools in improving construction planning, scheduling, and resource management. This study explored opportunities to develop a multi-objective genetic algorithm (MOGA) on existing BIM. The data were retrieved from a renovation project over the 2018–2020 period. Direct and indirect project costs, actual schedule, and resource usage were tracked and retrieved to create a BIM-based MOGA model. After 500 generations, optimal results were provided as a Pareto front with 70 combinations among total cost, time usage, and resource allocation. The BIM-MOGA can be used as an efficient tool for construction planning and scheduling using a combination of existing BIM along with MOGA into professional practices. This approach would help improve decision-making during the construction process based on the Pareto front data provided.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: BIM and Construction Integration · Construction Project Management and Performance · Resource-Constrained Project Scheduling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Optimization Theory and Applications for Engineering SYStems Research Group (OASYS), Chiang Mai University, Chiang Mai 50200, Thailand
Department of Civil and Environmental Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Mai 50300, Thailand
School of Civil Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
References (54)
Show source-provided references
1. PCIM: Project Control and Inhibiting-Factors Management Model10.1061/(ASCE)ME.1943-5479.0000125 ↗
2. Marle, F., and Vidal, L.-A. (2016). Managing Complex, High Risk Projects. Manag. Complex High Risk Proj.10.1007/978-1-4471-6787-7 ↗
3. Traditional Design versus BIM Based Design10.1016/j.proeng.2014.12.048 ↗
4. Hardin, B., and Mccool, D. (2015). BIM and Construction Management ProvenTools, Methods, and Workflws, John Wiley & Sons, Inc.. [2nd ed.].
5. Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry10.1061/(ASCE)LM.1943-5630.0000127 ↗
6. Renovation Projects: Design Process Problems and Improvement Mechanisms10.1061/(ASCE)0742-597X(2002)18:4(179) ↗
7. Construction Project Complexity: Research Trends and Implications10.1061/(ASCE)CO.1943-7862.0001306 ↗
8. Shiue, F.-J., Zheng, M.-C., Lee, H.-Y., Khitam, A.F., and Li, P.-Y. (2019). Renovation Construction Process Scheduling for Long-Term Performance of Buildings: An Application Case of University Campus. Sustainability, 11.10.3390/su11195542 ↗
9. Dasović, B., Galić, M., and Klanšek, U. (2020). A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling. Sustainability, 12.10.3390/su12083405 ↗
10. BIM-Based Construction Information Management Framework for Site Information Man-agement10.1155/2018/5249548 ↗
11. Arayici, Y., and Aouad, G. (2010). Building information modelling (BIM) for construction lifecycle management. Construction and Building: Design, Materials, and Techniques, Nova Science Publishers.
12. Benefits of Building Information Modelling in the Project Lifecycle: Construction Projects in Asia10.5772/58447 ↗
13. Building Information Management (BIM), A New Approach to Project Management10.29187/jscmt.2019.36 ↗
14. A suggested solution to improve the traditional construction planning approach
15. Public Sector Project MANAGEMENT EFFICIENCY PROBLEMS, CASE OF LATVIA
16. Building information model implementation for existing buildings for facilities management: A framework and two case studies
17. Using BIM to Retrofit Existing Buildings10.1016/j.proeng.2016.04.192 ↗
18. Building Information Modeling (BIM) for existing buildings—Literature review and future needs10.1016/j.autcon.2013.10.023 ↗
19. Combined 3D building surveying techniques-Terrestrial laser scanning (TLS) and total station sur-veying for BIM data management purposes10.3846/13923730.2013.795187 ↗
20. Cloud-to-BIM-to-FEM: Structural simulation with accurate historic BIM from laser scans10.1016/j.simpat.2015.06.004 ↗
21. A review of methods and algorithms for optimizing construction scheduling10.1057/jors.2012.174 ↗
22. Fahmy, A.M. (2016). Optimization Algorithms in Project Scheduling. Optim. Algorithm. Methods Appl.10.5772/63108 ↗
23. Werner, F., Burtseva, L., and Sotskov, Y.N. (2018). Special Issue on Algorithms for Scheduling Problems. Algorithms, 11.10.3390/a11060087 ↗
24. Multiobjective Linear Programming Model for Scheduling Linear Repetitive Projects10.1061/(ASCE)0733-9364(2007)133:6(417) ↗
25. Particle Swarm Optimization for Resource Constrained-project Scheduling Problem with Varying Resource Levels10.1016/j.protcy.2016.08.185 ↗
26. Ant Colony Optimization for Multimode Resource-Constrained Project Scheduling10.1061/(ASCE)ME.1943-5479.0000089 ↗
27. Optimization of Resource Allocation and Leveling Using Genetic Algorithms10.1061/(ASCE)0733-9364(1999)125:3(167) ↗
28. Chan, W.T., and Hu, H. (2002, January 16–19). Precast production scheduling with genetic algorithms. Proceedings of the 2000 Congress on Evolutionary Computation, CEC00 (Cat. No.00TH8512), La Jolla, CA, USA.
29. Optimizing Resource Leveling in Construction Projects10.1061/(ASCE)CO.1943-7862.0000097 ↗
30. Construction process optimisation—Review of methods, tools and applications
31. Obradović, D. (2018). Review of Nature-Inspired Optimization Algorithms Applied in Civil Engineeering. Elektron. Časopis Građevinskog Fak. Osijek, 74–88.10.13167/2018.17.8 ↗
32. A selective disassembly multi-objective optimization approach for adaptive reuse of building components10.1016/j.resconrec.2019.104605 ↗
33. Liang, C., Xu, X., Chen, H., Wang, W., Zheng, K., Tan, G., Gu, Z., and Zhang, H. (2021). Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion. Appl. Sci., 11.10.3390/app11020835 ↗
34. Murata, T., and Ishibuchi, H. (December, January 29). MOGA: Multi-objective genetic algorithms. Proceedings of the 1995 IEEE International Conference on Evolutionary Computation, Perth, Australia.
35. Fuzzy-based MOGA approach to stochastic time–cost trade-off problem10.1016/j.autcon.2009.02.001 ↗
36. A method to optimize mobile crane and crew interactions to minimize construction cost and time10.1016/j.autcon.2018.07.015 ↗
37. Zhao, S., and Li, Z. (2014, January 4–6). Multi-objective Optimization for Construction Site Layout Planning Problem under Fuzzy Random Environment. Proceedings of the 2014 Seventh International Joint Conference on Computational Sciences and Optimization, Beijing, China.10.1109/CSO.2014.148 ↗
38. A novel multi criteria decision making model for optimizing time-cost-quality trade-off problems in construction projects10.1016/j.eswa.2014.11.032 ↗
39. Tachaudomdach, S., Upayokin, A., Kronprasert, N., and Arunotayanun, K. (2021). Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems. Sustainability, 13.10.3390/su13063172 ↗
40. Construction scheduling using Genetic Algorithm based on Building Information Model10.1016/j.eswa.2014.05.047 ↗
41. BIM-Based Construction Scheduling Method Using Optimization Theory for Reducing Activity Overlaps10.1061/(ASCE)CP.1943-5487.0000342 ↗
42. Offsite construction: Developing a BIM-Based optimizer for assembly10.1016/j.jclepro.2019.01.113 ↗
43. Asl, M.R., Bergin, M., Menter, A., and Yan, W. (2014, January 10–12). BIM-based Parametric Building Energy Performance MultiObjective Optimization. Proceedings of the 32nd eCAADe Conference—Volume 2, Newcastle, UK.10.52842/conf.ecaade.2014.2.455 ↗
44. De BIM-based Applications of Metaheuristic Algorithms to Support the Decision-making Process: Uses in the Planning of Construction Site Layout10.1016/j.proeng.2017.08.030 ↗
45. Genetic Algorithms for Optimization of Resource Allocation in Large Scale Construction Project Management10.4304/jcp.5.12.1916-1924 ↗
46. Multi-objective genetic optimization for scheduling a multi-storey building10.1016/j.autcon.2014.04.005 ↗
47. Simultaneous multi-criteria optimization for scheduling linear infrastructure projects
48. Development of a Method for Construction Management in Refurbishment Projects
49. BIM-Based multi-objective optimization process for energy and comfort simulation: Existing tools analysis and workflow proposal on a case study
50. A fast and elitist multiobjective genetic algorithm: NSGA-II10.1109/4235.996017 ↗
51. Multi-objective optimization for repetitive scheduling under uncertainty10.1108/ECAM-05-2018-0217 ↗
52. Rao, S.S. (2019). Engineering Optimization: Theory and Practice, John Wiley & Sons.10.1002/9781119454816 ↗
53. Palisade Corporation (2015). Palisada Corporation Evolver User’s Guide. The Genetic Algorithm Solver for Microsoft Excel, Version 7, Palisade Corporation.
54. Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm10.1016/j.autcon.2012.11.014 ↗