Warianty tytułu
Języki publikacji
Abstrakty
To save resources and protect the environment to the maximum extent, green buildings came into being. Among them, prefabricated building is the only way for traditional buildings to transform into green buildings. The construction scheduling of traditional buildings is mostly focused on the control of on-site resources, which cannot scientifically and reasonably complete the construction goal of prefabricated building. In response to the above issues, a resource constrained scheduling model based on genetic algorithm is designed by sustainable development, and an improved non dominated sorting genetic algorithm with elite strategy is introduced. It is used to solve the time cost weight balance scheduling model and the low-cost low-carbon scheduling model. The research results indicated that this algorithm had a reverse generation distance value of 0.35 when evaluated 4000 times, and a super volume value of 0.43 when evaluated 10000 times. In the application of a certain affordable housing project, the resource constrained scheduling model based on genetic algorithm can shorten the assembly phase to 8 days, and the low-cost low-carbon scheduling model using proposed algorithm can reduce the transportation cost and carbon emission duration of transportation vehicles to 22501 yuan and 93.75 h, respectively. Resource constrained scheduling models based on genetic algorithms and low-cost low-carbon scheduling models have potential in the field of green buildings, which can achieve significant results in saving time, cost, and reducing carbon emissions. These research results can provide reference for the promotion and practice of green buildings, and guide the formulation and implementation of relevant policies.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
459--472
Opis fizyczny
Bibliogr. 25 poz., il.
Twórcy
autor
- College of Architecture and Engineering, The Open University of Shaanxi, Xi’an, China, 17729097506@163.com
Bibliografia
- [1] M. Barma and U.M. Modibbo, “Multiobjective mathematical optimization model for municipal solid waste management with economic analysis of reuse/recycling recovered waste materials”, Journal of Computational and Cognitive Engineering, vol. 1, no. 3, pp. 122-137, 2022, doi: 10.47852/bonviewJCCE149145.
- [2] F.L. Guribie, J.T. Akubah, C. Tengan, and A.V.K. Blay Jnr, “Demand for green building in Ghana: A conceptual modeling and empirical study of the impediments”, Construction Innovation: Information, Process, Management, vol. 22, no. 2, pp. 342-360, 2022, doi: 10.1108/CI-11-2020-0180.
- [3] N. Naud, L. Sorelli, A. Salenikovich, and S. Cuerrier-Auclair, “Fostering a cast-in-place steel-UHPFRC connector for ductile timber-concrete composite structures: parametric study of the shear behaviour and design considerations”, Canadian Journal of Civil Engineering, vol. 48, no. 9, pp. 1081-1092, 2021, doi: 10.1139/cjce-2019-0173.
- [4] J. Yrjola and J. Bujnak, “Shear tests on demountable precast column connections”, Structural Concrete, vol. 22, no. 4, pp. 2432-2442, 2021, doi: 10.1002/suco.202000635.
- [5] H.A. Bazar and H. Abdel-Jaber, “A developed uncapacitated scheduling algorithm of building timetables for different exam kinds”, Indian Journal of Computer Science, vol. 16, no. 8, pp. 1139-1149, 2020, doi: 10.3844/jcssp.2020.1139.1149.
- [6] F.S. Bataglin, D.D. Viana, C.T. Formoso, and I.R. Bulhőes, “Model for planning and controlling the delivery and assembly of engineer-to-order prefabricated building systems: Exploring synergies between Lean and BIM 1”, Canadian Journal of Civil Engineering, vol. 47, no. 2, pp. 165-177, 2020, doi: 10.1139/cjce-2018-0462.
- [7] H. Wang, H. Wang, and Y. Li, “Production decision rescheduling of prefabricated building parts subject to interference from the arrival of new orders”, International Journal of Industrial Engineering, vol. 27, no. 5, pp. 791-809, 2020, doi: 10.23055/ijietap.2020.27.5.6547.
- [8] J. Du, P. Dong, and V. Sugumaran, “Dynamic production scheduling for prefabricated components considering the demand fluctuation”, Intelligent Automation and Soft Computing, vol. 26, no. 4, pp. 715-723, 2020, doi: 10.32604/iasc.2020.010105.
- [9] W. Yi, S. Wang, and A. Zhang, “Optimal transportation planning for prefabricated products in construction”, Computer-Aided Civil and Infrastructure Engineering, vol. 35, no. 4, pp. 342-353, 2020, doi: 10.1111/mice.12504.
- [10] H. Zhang and L. Yu, “Resilience-cost trade off supply chain planning for the prefabricated construction project”, Journal of Civil Engineering and Management, vol. 27, no. 1, pp. 45-59, 2021, doi: 10.3846/jcem.2021.14114.
- [11] X. Manman, Z. Li, Q. Yaning, Y. Zhenmin, C. Yuanlong, Z. Lemeng, and L. Julei, “Analysis of factors affecting the quality of precast components based on structural equation modeling”, Arabian Journal for Science and Engineering, vol. 47, no. 4, pp. 4171-4185, 2022, doi: 10.1007/s13369-021-05991-z.
- [12] S. Shokoohyar and J. Amiri, “Developing a multi-mode doubly resource constrained project scheduling problem using meta-heuristic approaches”, International Journal of Project Organisation and Management, vol. 13, no. 1, pp. 31-59, 2021, doi: 10.1504/IJPOM.2021.114724.
- [13] N.P. Marri and N.R. Rajalakshmi, “MOEAGAC: An energy aware model with genetic algorithm for efficient scheduling in cloud computing”, International Journal of Intelligent Computing and Cybernetics, vol. 15, no. 2, pp. 318-329, 2022, doi: 10.1108/IJICC-07-2021-0134.
- [14] H. Kazemi, M. M. Mazdeh, M. Rostami, and M. Heydari, “The integrated production-distribution scheduling in parallel machine environment by using improved genetic algorithms”, Journal of Industrial and Production Engineering, vol. 38, no. 3, pp. 157-170, 2021, doi: 10.1080/21681015.2020.1848930.
- [15] M. Nikseresht and M. Raji, “MOGATS: A multi-objective genetic algorithm-based task scheduling for heterogeneous embedded systems”, International Journal of Embedded Systems, vol. 14, no. 2, pp. 171-184, 2021, doi: 10.1504/IJES.2021.113811.
- [16] B. Su, N. Xie, and Y. Yang, “Hybrid genetic algorithm based on bin packing strategy for the unrelated parallel workgroup scheduling problem”, Journal of Intelligent Manufacturing, vol. 32, no. 4, pp. 957-969, 2021, doi: 10.1007/s10845-020-01597-8.
- [17] F. Biermann, T. Hickmann, C. A. Sénit, et al., “Scientific evidence on the political impact of the sustainable development goals”, Nature Sustainability, vol. 5, no. 9, pp. 795-800, 2022, doi: 10.1038/s41893-022-00909-5.
- [18] S.Weiland, T. Hickmann, M. Lederer, J. Marquardt, and S. Schwindenhammer, “The 2030 agenda for sustainable development: transformative change through the sustainable development goals?”, Politics and Governance, vol. 9, no. 1, pp. 90-95, 2021, doi: 10.17645/pag.v9i1.4191.
- [19] S. Maulidiah, Monalisa, and Z. Ali, “Environmental management: A study on the precautionary principle in siak regency of Indonesia towards sustainable development”, Ecology, Environment and Conservation, vol. 26, no. 3, pp. 1085-1089, 2020.
- [20] H. Baba and Y. Asami, “Cost-efficient factors in local public spending: Detecting relationships between local environments, population size and urban area category”, Environment and Planning B: Urban Analytics and City Science, vol. 49, no. 1, pp. 241-258, 2022, doi: 10.1177/23998083211003883.
- [21] K. Sriram and P.A. Insel, “Risks of ACE inhibitor and ARB usage in COVID-19: evaluating the evidence”, Clinical Pharmacology and Therapeutics, vol. 108, no. 2, pp. 236-241, 2020, doi: 10.1002/cpt.1863.
- [22] A.M. Davidson, J. Wysocki, and D. Batlle, “Interaction of SARS-CoV-2 and other coronavirus with ACE (angiotensin-converting enzyme)-2 as their main receptor: therapeutic implications”, Hypertension, vol. 76, no. 5, pp. 1339-1349, 2020, doi: 10.1161/HYPERTENSIONAHA.120.15256.
- [23] J. Hippisley-Cox, D. Young, C. Coupland, et al., “Risk of severe COVID-19 disease with ACE inhibitors and angiotensin receptor blockers: cohort study including 8.3 million people”, Heart, vol. 106, no. 19, pp. 1503-1511, 2020, doi: 10.1136/heartjnl-2020-317393.
- [24] J. Liu and X. Chen, “An improved NSGA-II algorithm based on crowding distance elimination strategy”, International Journal of Computational Intelligence Systems, vol. 12, no. 2, pp. 513-518, 2019, doi: 10.2991/ijcis.d.190328.001.
- [25] W. Deng, J. Xu, H. Zhao, and Y. Song, “A novel gate resource allocation method using improved PSO-based QEA”, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 1737-1745, 2022, doi: 10.1109/TITS.2020.3025796.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-93fdfc86-cd98-4a0a-b959-b60df9e8fcfe