A green bi-objective model for facility location in linear construction projects considering mobile and immobile facilities
Mahdi Mohammadi Zanjani
1
(
Department of Industrial Engineering, Payam-e-Noor University, Tehran, Iran
)
Roozbeh Azizmohammadi
2
(
Department of Industrial Engineering, Payam-e-Noor University, Tehran, Iran
)
seyed mohamad hasan hosseini
3
(
Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran
)
Mostafa Soltani
4
(
Department of Industrial Engineering, Payam-e-Noor University, Tehran, Iran
)
کلید واژه: bi-objective, CO2 emissions, facility location, linear construction projects,
چکیده مقاله :
Facility location in linear construction projects plays a vital role in optimizing operational efficiency. In real-world conditions, both mobile and immobile concrete batching facilities are typically used to construct viaducts, bridges, and tunnels along a planned route over a specified planning horizon. This study aims to propose a bi-objective model for the green locating of facilities in linear construction projects, considering both mobile and immobile batching. All types of facilities have a given capacity, operational cost, and CO2 emissions. Two objective functions are considered for simultaneous optimization: total expected cost and total CO2 emissions. The performance of the proposed model is confirmed using the data of a real case in linear construction projects in Iran. Moreover, sensitivity analysis represents the effect of variations in four key parameters on two objective functions and the final values of decision variables. The result confirms the accuracy and efficiency of the proposed model and represents the conflict between the two objective functions. In addition, the result indicates that the solution approach could provide proper alternatives for managers in various conditions. The findings underline the importance of green practices in facility management within linear construction, ultimately contributing to the broader discourse on eco-friendly infrastructure development.
چکیده انگلیسی :
Facility location in linear construction projects plays a vital role in optimizing operational efficiency. In real-world conditions, both mobile and immobile concrete batching facilities are typically used to construct viaducts, bridges, and tunnels along a planned route over a specified planning horizon. This study aims to propose a bi-objective model for the green locating of facilities in linear construction projects, considering both mobile and immobile batching. All types of facilities have a given capacity, operational cost, and CO2 emissions. Two objective functions are considered for simultaneous optimization: total expected cost and total CO2 emissions. The performance of the proposed model is confirmed using the data of a real case in linear construction projects in Iran. Moreover, sensitivity analysis represents the effect of variations in four key parameters on two objective functions and the final values of decision variables. The result confirms the accuracy and efficiency of the proposed model and represents the conflict between the two objective functions. In addition, the result indicates that the solution approach could provide proper alternatives for managers in various conditions. The findings underline the importance of green practices in facility management within linear construction, ultimately contributing to the broader discourse on eco-friendly infrastructure development.
Al Hawarneh, A., Bendak, S., & Ghanim, F. (2019). Dynamic facilities planning model for large scale construction projects. Automation in Construction, 98, 72-89.
Almahameed, B. A., & Bisharah, M. (2024). Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management. Asian Journal of Civil Engineering, 25(2), 1281-1294.
Chan, J., Bachmann, C., & Haas, C. (2020). Potential economic and energy impacts of substituting adaptive reuse for new building construction: A case study of Ontario. Journal of cleaner production, 259, 120939.
Daskin, M. (1997). Network and discrete location: models, algorithms and applications. Journal of the Operational Research Society, 48(7), 763-764.
Dias, J., Captivo, M. E., & Clímaco, J. (2007). Efficient primal-dual heuristic for a dynamic location problem. Computers & operations research, 34(6), 1800-1823.
Drezner, Z., & Wesolowsky, G. O. (1996). Location-allocation on a line with demand-dependent costs. European Journal of Operational Research, 90(3), 444-450.
Falcão, V. A., Nobre Júnior, E. F., & Prata, B. D. A. (2016). Optimization techniques applied to earthmoving and highway construction: A survey. International Review of Civil Engineering (I. RE. CE), 7(5).
Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European journal of operational research, 230(2), 201-211.
Ghorbani, R., Ghousi, R., & Makui, A. (2019). Location of compressed natural gas stations using multi-objective flow refueling location model in the two-way highways: A case study in Iran. Journal of Industrial and Systems Engineering, 12(2), 95-112.
Golpîra, H. (2020). Optimal integration of the facility location problem into the multi-project multi-supplier multi-resource Construction Supply Chain network design under the vendor managed inventory strategy. Expert systems with applications, 139, 112841.
Güden, H., & Süral, H. (2014). Locating mobile facilities in railway construction management. Omega, 45, 71-79.
Hasani, A., & Hosseini, S. M. H. (2020). A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption. Applied Mathematics and Computation, 386, 125533.
Hasani, A., & Hosseini, S. M. H. (2022). Auxiliary resource planning in a flexible flow shop scheduling problem considering stage skipping. Computers & Operations Research, 138, 105625.
Heidari, A., Imani, D. M., Khalilzadeh, M., & Sarbazvatan, M. (2023). Green two-echelon closed and open location-routing problem: application of NSGA-II and MOGWO metaheuristic approaches. Environment, Development and Sustainability, 25(9), 9163-9199.
Khural, R. A., Ertz, M., & Cerchione, R. (2024). Moving toward sustainability and circularity in hill road construction: a study of barriers, practices and performance. Engineering, Construction and Architectural Management, 31(4), 1608-1641.
Liu, S., Lin, B., Wang, J., & Wu, J. (2018). Modeling the multi-period and multi-classification-yard location problem in a railway network. Symmetry, 10(5), 135.
Mansour, A. G., Eid, M. S., & Elbeltagi, E. E. (2019, June). Optimal construction facilities location selection for linear infrastructure projects. In ASCE International Conference on Computing in Civil Engineering 2019 (pp. 482-490). Reston, VA: American Society of Civil Engineers.
Mavrotas, G. (2009). Effective implementation of the ε constraint method in Multi Objective Mathematical Programming problems, Applied Mathematics and Computation, 213(2):455–465.
Pham, V. H. S., & Pham, D. H. (2025). Advanced construction site layout planning for prefabricated projects: an application of the new algorithm. Engineering, Construction and Architectural Management.
Rajabian, A., Hosseini, S. M. H., Amoozad Khalili, H., & Amirkhan, M. (2024). Designing a Multi-echelon and Multi-Product Sustainable Biomass Supply Chain Network Considering Input Material Diversity. Scientia Iranica.
ReVelle, C. S., & Eiselt, H. A. (2005). Location analysis: A synthesis and survey. European journal of operational research, 165(1), 1-19.
Soltani, M., Mohammadi, R. A., Hosseini, S. M. H., & Zanjani, M. M. (2021). A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation. International Journal of Industrial Engineering & Production Research, 32(2), 1-28.
Vardan, F., & Hosseini, S. M. H. (2024). A bi-objective model for designing a green closed-loop supply chain network in a petroleum complex considering transportation constraints: A case study. https://doi.org/10.21203/rs.3.rs-3903554/v1
Vu Hong Son, P., & Soulisa, F. V. (2023). A hybrid ant lion optimizer (alo) algorithm for construction site layout optimization. Journal of Soft Computing in Civil Engineering, 7(4), 50-71.
Wang, S., Gong, M., Wu, Y., & Zhang, M. (2020). Multi-objective optimization for location-based and preferences-aware recommendation. Information Sciences, 513, 614-626.
Yilmaz, G., Akcamete, A., & Demirors, O. (2023). BIM-CAREM: Assessing the BIM capabilities of design, construction and facilities management processes in the construction industry. Computers in Industry, 147, 103861
Yoo, S., Kumagai, J., Aki, R., & Managi, S. (2025). Railway network expansion reduces air pollution in Tokyo over 25 years. Sustainable Cities and Society, 106408.
ZahediAnaraki, A., & Esmaeilian, G. (2021). A Benders-Decomposition and Meta-Heuristic Algorithm for a Bi-Objective Stochastic Reliable Capacitated Facility Location Problem Not Dealing with Benders Feasibility-Cut Stage. Journal of Optimization in Industrial Engineering, 14(1), 105-119.
Zhang, W., Cao, K., Liu, S., & Huang, B. (2016). A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Computers, Environment and Urban Systems, 59, 220-230.
