Buffering policies for prefabricated construction supply chain subject to material lead time and activity duration uncertainties
Abstract
Supply chain management plays a pivotal role in the smooth execution of prefabricated construction. One key aspect involves strategically placing and sizing buffers to handle uncertainties (e.g., stochastic material lead-times and activity durations) within the prefabricated construction supply chain (PCSC). This study examines three buffering policies based on varying combinations of time and inventory buffers to mitigate stochastic material delays and activity prolongs in PSCS, namely, pure inventory buffering policy, pure time buffering policy, and mixed inventory-time buffering policy. To enable this analysis, we characterize how stochastic material delays originating from off-site supply chains impact project schedules, and then develop mathematical procedures for sizing inventory and/or time buffers under the three buffering policies. Case application and numerical analysis are conducted to investigate the performance of these buffering policies and the impact of the project characteristics on them (e.g., due date and arrival rate). Finally, insights are extracted to assist managers in choosing appropriate policies for projects with different characteristics. In general, combining inventory and time buffers results in better performance, particularly under tight project deadlines and high arrival rates. The pure time buffering policy can also be a viable option in specific situations, allowing managers to have more choices.
Keyword : prefabricated construction, supply chain management, time buffer, inventory buffer, uncertainty
How to Cite
Lu, H., Liu, D., & Li, J. (2024). Buffering policies for prefabricated construction supply chain subject to material lead time and activity duration uncertainties. Journal of Civil Engineering and Management, 30(2), 99–113. https://doi.org/10.3846/jcem.2024.20809
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hsu, P. Y., Aurisicchio, M., & Angeloudis, P. (2019). Risk-averse supply chain for modular construction projects. Automation in Construction, 106, Article 102898.
Kim, T., Kim, Y. W., & Cho, H. (2020). Dynamic production scheduling model under due date uncertainty in precast concrete construction. Journal of Cleaner Production, 257, Article 120527. https://doi.org/10.1016/j.jclepro.2020.120527
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Liang, Y., Cui, N., Hu, X., & Demeulemeester, E. (2020). The integration of resource allocation and time buffering for bi-objective robust project scheduling. International Journal of Production Research, 58(13), 3839–3854. https://doi.org/10.1080/00207543.2019.1636319
Liu, Q., & Tao, Z. (2015). A multi-objective optimization model for the purchasing and inventory in a three-echelon construction supply chain. In Proceedings of the 9th International Conference of Management Science and Engineering Management (pp. 245–253). Springer, Cham. https://doi.org/10.1007/978-3-662-47241-5_20
Liu, J., & Lu, M. (2018). Constraint programming approach to optimizing project schedules under material logistics and crew availability constraints. Journal of Construction Engineering and Management, 144(7), 4018041–4018049. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001507
Liu, J., Gong, E., Wang, D., & Teng, Y. (2018). Cloud model-based safety performance evaluation of prefabricated building project in China. Wireless Personal Communications, 102, 3021–3039. https://doi.org/10.1007/s11277-018-5323-3
Lu, H., Wang, H., Xie, Y., & Li, H. (2016). Construction material safety-stock determination under nonstationary stochastic demand and random supply yield. IEEE Transactions on Engineering Management, 63(2), 201–212. https://doi.org/10.1109/TEM.2016.2536146
Lu, H., Wang, H., Xie, Y., & Wang, X. (2018). Study on construction material allocation policies: A simulation optimization method. Automation in Construction, 90, 201–212. https://doi.org/10.1016/j.autcon.2018.02.012
Ma, Z., Demeulemeester, E., He, Z., & Wang, N. (2019). A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments. Computers & Industrial Engineering, 131, 382–390. https://doi.org/10.1016/j.cie.2019.04.014
Moradi, H., & Shadrokh, S. (2019). A robust scheduling for the multi-mode project scheduling problem with a given deadline under uncertainty of activity duration. International Journal of Production Research, 57(10), 3138–3167. https://doi.org/10.1080/00207543.2018.1552371
Newbold, R. C. (1998). Project management in the fast lane-applying the theory of constraints. The St. Lucie Press.
Ning, M., He, Z., Jia, T., & Wang, N. (2017). Metaheuristics for multi-mode cash flow balanced project scheduling with stochastic duration of activities. Automation in Construction, 81, 224–233. https://doi.org/10.1016/j.autcon.2017.06.011
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