Resilience-cost tradeoff supply chain planning for the prefabricated construction project
Abstract
Delivery of the prefabricated components may be disrupted by low productivity and various of traffic restrictions, thus delaying the prefabricated construction project. However, planning of the prefabricated component supply chain (PCSC) under disruptions has seldom been studied. This paper studies the construction schedule-dependent resilience for the PCSC plan by considering transportation costs and proposes a multi-objective optimization model. First, the PCSC planning problem regarding schedule-dependent resilience and resultant transportation cost is analyzed. Second, a quantification scheme of the schedule-dependent resilience of the PCSC plan is proposed. Third, formulation of the resilience-cost tradeoff optimization model for the PCSC planning is developed. Fourth, the multi-objective particle swarm optimization (MOPSO)-based method for solving the resilience-cost tradeoff model is presented. Finally, a case study is presented to demonstrate and justify the developed method. This study contributes to the knowledge and methodologies for PCSC management by addressing resilience at the planning stage.
Keyword : prefabricated construction, prefabricated component supply chain (PCSC), disruption, schedule-dependent resilience, resilience quantification, resilience-cost tradeoff, multi-objective particle swarm optimization (MOPSO)
How to Cite
Zhang, H., & Yu, L. (2021). Resilience-cost tradeoff supply chain planning for the prefabricated construction project. Journal of Civil Engineering and Management, 27(1), 45-59. https://doi.org/10.3846/jcem.2021.14114
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Li, C. Z., Hong, J., Xue, F., Shen, G. Q., Xu, X., & Mok, M. K. (2016). Schedule risks in prefabrication housing production in Hong Kong: a social network analysis. Journal of Cleaner Production, 134, 482–494. https://doi.org/10.1016/j.jclepro.2016.02.123
Luo, L., Qiping Shen, G., Xu, G., Liu, Y., & Wang, Y. (2019). Stakeholder-associated supply chain risks and their interactions in a prefabricated building project in Hong Kong. Journal of Management in Engineering, 35(2), 05018015. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000675
Meng, X. (2013). Change in UK construction: Moving toward supply chain collaboration. Journal of Civil Engineering and Management, 19(3), 422–432. https://doi.org/10.3846/13923730.2012.760479
Miller-Hooks, E., Zhang, X., & Faturechi, R. (2012). Measuring and maximizing resilience of freight transportation networks. Computers & Operations Research, 39(7), 1633–1643. https://doi.org/10.1016/j.cor.2011.09.017
Morlok, E. K., & Chang, D. J. (2004). Measuring capacity flexibility of a transportation system. Transportation Research Part A: Policy and Practice, 38(6), 405–420. https://doi.org/10.1016/j.tra.2004.03.001
Murino, T., Romano, E., & Santillo, L. C. (2011). Supply chain performance sustainability through resilience function. In Proceedings of the 2011 Winter Simulation Conference (pp. 1600–1611). IEEE. https://doi.org/10.1109/WSC.2011.6147877
Murray-tuite, P., & Mahmassani, H. (2004). Methodology for determining vulnerable links in a transportation network. Transportation Research Record: Journal of the Transportation Research Board, 1882, 88–96. https://doi.org/10.3141/1882-11
Murray-tuite, P. (2006). A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions. Proceedings of the 2006 Winter Simulation Conference (pp. 1398–1405). IEEE. https://doi.org/10.1109/WSC.2006.323240
Peeta, S., Sibel Salman, F., Gunnec, D., & Viswanath, K. (2010). Pre-disaster investment decisions for strengthening a highway network. Computers & Operations Research, 37(10), 1708–1719. https://doi.org/10.1016/j.cor.2009.12.006
Peng, P., Snyder, L. V., Lim, A., & Liu, Z., (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 45(8), 1190–1211. https://doi.org/10.1016/j.trb.2011.05.022
Polat, G. (2010). Precast concrete systems in developing vs. industrialized countries. Journal of Civil Engineering and Management, 16(1), 85–94. https://doi.org/10.3846/jcem.2010.08
Ratick, S., Meacham, B., & Aoyama, Y. (2008). Locating backup facilities to enhance supply chain disaster resilience. Growth and Change, 39(4), 642–666. https://doi.org/10.1111/j.1468-2257.2008.00450.x
Snyder, L. V., & Daskin, M. S. (2005). Reliability models for facility location: The expected failure cost case. Transportation Science, 39(3), 400–416. https://doi.org/10.1287/trsc.1040.0107
Shojaei, P., & Haeri, S. A. S. (2019). Development of supply chain risk management approaches for construction projects: A grounded theory approach. Computers & Industrial Engineering, 128, 837–850. https://doi.org/10.1016/j.cie.2018.11.045
Ta, C., Goodchild, A. V., & Pitera, K. (2009). Structuring a definition of resilience for the freight transportation system. Transportation Research Record: Journal of the Transportation Research Board, 2097, 19–25. https://doi.org/10.3141/2097-03
Taillandier, F., Taillandier, P., Tepeli, E., Breysse, D., Mehdizadeh, R., & Khartabil, F. (2015). A multi-agent model to manage risks in construction project (SMACC). Automation in Construction, 58, 1–18. https://doi.org/10.1016/j.autcon.2015.06.005
Taroun, A. (2014). Towards a better modelling and assessment of construction risk: Insights from a literature review. International Journal of Project Management, 32(1), 101–115. https://doi.org/10.1016/j.ijproman.2013.03.004
Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22–48. https://doi.org/10.1016/j.tre.2015.03.005
Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6), 317–325. https://doi.org/10.1016/S0020-0190(02)00447-7
Wang, Z., Hu, H., & Zhou, W. (2017a). RFID enabled knowledge-based precast construction supply chain. ComputerAided Civil and Infrastructure Engineering, 32, 499–514. https://doi.org/10.1111/mice.12254
Wang, T.-K., Zhang, Q., Chong, H.-Y., & Wang, X. (2017b). Integrated supplier selection framework in a resilient construction supply chain: An approach via Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA). Sustainability, 9(2), 289. https://doi.org/10.3390/su9020289
Wang, Y., Yuan, Z., & Sun, C. (2018). Research on assembly sequence planning and optimization of precast concrete buildings. Journal of Civil Engineering and Management, 24(2), 106–115. https://doi.org/10.3846/jcem.2018.458
Zhang, H., & Yu, L. (2020). Prefabricated component site layout planning subject to dynamic and interactive constraints. Automation in Construction (Submitted manuscript).
Arashpour, M., Bai, Y., Aranda-mena, G., Bab-Hadiashar, A., Hosseini, R., & Kalutara, P. (2017). Optimizing decisions in advanced manufacturing of prefabricated products: Theorizing supply chain configurations in off-site construction. Automation in Construction, 84, 146–153. https://doi.org/10.1016/j.autcon.2017.08.032
Berdica, K. (2002). An introduction to road vulnerability: what has been done, is done and should be done. Transport Policy, 9(2), 117–127. https://doi.org/10.1016/S0967-070X(02)00011-2
Brandon-Jones, E., Squire, B., Autry, C., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73. https://doi.org/10.1111/jscm.12050
Chen, C.-C. (Frank), Tsai, Y.-H. (Natalie), & Schonfeld, P. (2016). Schedule coordination, delay propagation, and disruption resilience in intermodal logistics networks. Transportation Research Record: Journal of the Transportation Research Board, 2548(1), 16–23. https://doi.org/10.3141/2548-03
Chen, L., & Miller-Hooks, E. (2012). Resilience: An indicator of recovery capability in intermodal freight transport. Transportation Science, 46(1), 109–123. https://doi.org/10.1287/trsc.1110.0376
Colicchia, C., Dallari, F., & Melacini, M. (2010). Increasing supply chain resilience in a global sourcing context. Production Planning & Control, 21(7), 680–694. https://doi.org/10.1080/09537280903551969
Davis, P. R. (2008). A relationship approach to construction supply chains. Industrial Management & Data Systems, 108(3), 310–327. https://doi.org/10.1108/02635570810858741
Ellram, L. M., Tate, W. L., & Billington, C. (2004). Understanding and managing the services supply chain. Journal of Supply Chain Management, 40(3), 17–32. https://doi.org/10.1111/j.1745-493X.2004.tb00176.x
Francis, R., & Bekera, B. (2014). A metric and frameworks for resilience analysis of engineered and infrastructure systems. Reliability Engineering & System Safety, 121, 90–103. https://doi.org/10.1016/j.ress.2013.07.004
Geng, L., Xiao, R., & Xu, X. (2014). Research on MAS-based supply chain resilience and its self-organized criticality. Discrete Dynamics in Nature and Society, Article ID 621341. https://doi.org/10.1155/2014/621341
Hackl, J., Adey, B. T., & Lethanh, N. (2018). Determination of near-optimal restoration programs for transportation networks following natural hazard events using simulated annealing. Computer-Aided Civil and Infrastructure Engineering, 33, 618–637. https://doi.org/10.1111/mice.12346
Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1), 1–23. https://doi.org/10.1146/annurev.es.04.110173.000245
Huang, M., Li, R., & Wang, X. (2011). Network construction for fourth-party logistics based on resilience with using Particle Swarm Optimization. In 2011 Chinese Control and Decision Conference (pp. 3924–3929). IEEE. https://doi.org/10.1109/CCDC.2011.5968907
Ip, W. H., & Wang, D. (2011). Resilience and friability of transportation networks: Evaluation, analysis and optimization. IEEE Systems Journal, 5(2), 189–198. https://doi.org/10.1109/JSYST.2010.2096670
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks (pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
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, 120527. https://doi.org/10.1016/j.jclepro.2020.120527
Kumar, V., & Viswanadham, N. (2007). A CBR-based decision support system framework for construction supply chain risk management. In 2007 International Conference on Automation Science and Engineering (pp. 980–985). IEEE. https://doi.org/10.1109/COASE.2007.4341831
Li, Z., Shen, G. Q., & Xue, X. (2014). Critical review of the research on the management of prefabricated construction. Habitat International, 43, 240–249. https://doi.org/10.1016/j.habitatint.2014.04.001
Li, C. Z., Hong, J., Xue, F., Shen, G. Q., Xu, X., & Mok, M. K. (2016). Schedule risks in prefabrication housing production in Hong Kong: a social network analysis. Journal of Cleaner Production, 134, 482–494. https://doi.org/10.1016/j.jclepro.2016.02.123
Luo, L., Qiping Shen, G., Xu, G., Liu, Y., & Wang, Y. (2019). Stakeholder-associated supply chain risks and their interactions in a prefabricated building project in Hong Kong. Journal of Management in Engineering, 35(2), 05018015. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000675
Meng, X. (2013). Change in UK construction: Moving toward supply chain collaboration. Journal of Civil Engineering and Management, 19(3), 422–432. https://doi.org/10.3846/13923730.2012.760479
Miller-Hooks, E., Zhang, X., & Faturechi, R. (2012). Measuring and maximizing resilience of freight transportation networks. Computers & Operations Research, 39(7), 1633–1643. https://doi.org/10.1016/j.cor.2011.09.017
Morlok, E. K., & Chang, D. J. (2004). Measuring capacity flexibility of a transportation system. Transportation Research Part A: Policy and Practice, 38(6), 405–420. https://doi.org/10.1016/j.tra.2004.03.001
Murino, T., Romano, E., & Santillo, L. C. (2011). Supply chain performance sustainability through resilience function. In Proceedings of the 2011 Winter Simulation Conference (pp. 1600–1611). IEEE. https://doi.org/10.1109/WSC.2011.6147877
Murray-tuite, P., & Mahmassani, H. (2004). Methodology for determining vulnerable links in a transportation network. Transportation Research Record: Journal of the Transportation Research Board, 1882, 88–96. https://doi.org/10.3141/1882-11
Murray-tuite, P. (2006). A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions. Proceedings of the 2006 Winter Simulation Conference (pp. 1398–1405). IEEE. https://doi.org/10.1109/WSC.2006.323240
Peeta, S., Sibel Salman, F., Gunnec, D., & Viswanath, K. (2010). Pre-disaster investment decisions for strengthening a highway network. Computers & Operations Research, 37(10), 1708–1719. https://doi.org/10.1016/j.cor.2009.12.006
Peng, P., Snyder, L. V., Lim, A., & Liu, Z., (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 45(8), 1190–1211. https://doi.org/10.1016/j.trb.2011.05.022
Polat, G. (2010). Precast concrete systems in developing vs. industrialized countries. Journal of Civil Engineering and Management, 16(1), 85–94. https://doi.org/10.3846/jcem.2010.08
Ratick, S., Meacham, B., & Aoyama, Y. (2008). Locating backup facilities to enhance supply chain disaster resilience. Growth and Change, 39(4), 642–666. https://doi.org/10.1111/j.1468-2257.2008.00450.x
Snyder, L. V., & Daskin, M. S. (2005). Reliability models for facility location: The expected failure cost case. Transportation Science, 39(3), 400–416. https://doi.org/10.1287/trsc.1040.0107
Shojaei, P., & Haeri, S. A. S. (2019). Development of supply chain risk management approaches for construction projects: A grounded theory approach. Computers & Industrial Engineering, 128, 837–850. https://doi.org/10.1016/j.cie.2018.11.045
Ta, C., Goodchild, A. V., & Pitera, K. (2009). Structuring a definition of resilience for the freight transportation system. Transportation Research Record: Journal of the Transportation Research Board, 2097, 19–25. https://doi.org/10.3141/2097-03
Taillandier, F., Taillandier, P., Tepeli, E., Breysse, D., Mehdizadeh, R., & Khartabil, F. (2015). A multi-agent model to manage risks in construction project (SMACC). Automation in Construction, 58, 1–18. https://doi.org/10.1016/j.autcon.2015.06.005
Taroun, A. (2014). Towards a better modelling and assessment of construction risk: Insights from a literature review. International Journal of Project Management, 32(1), 101–115. https://doi.org/10.1016/j.ijproman.2013.03.004
Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22–48. https://doi.org/10.1016/j.tre.2015.03.005
Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6), 317–325. https://doi.org/10.1016/S0020-0190(02)00447-7
Wang, Z., Hu, H., & Zhou, W. (2017a). RFID enabled knowledge-based precast construction supply chain. ComputerAided Civil and Infrastructure Engineering, 32, 499–514. https://doi.org/10.1111/mice.12254
Wang, T.-K., Zhang, Q., Chong, H.-Y., & Wang, X. (2017b). Integrated supplier selection framework in a resilient construction supply chain: An approach via Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA). Sustainability, 9(2), 289. https://doi.org/10.3390/su9020289
Wang, Y., Yuan, Z., & Sun, C. (2018). Research on assembly sequence planning and optimization of precast concrete buildings. Journal of Civil Engineering and Management, 24(2), 106–115. https://doi.org/10.3846/jcem.2018.458
Zhang, H., & Yu, L. (2020). Prefabricated component site layout planning subject to dynamic and interactive constraints. Automation in Construction (Submitted manuscript).