Strategies to reduce costs in the planning of house building projects with government financing for low-income populations
Abstract
The execution of housing construction projects for low-income populations in developing countries requires projects with low diversification, using quality materials and meeting project deadlines, qualified workforce are essential actions for the success of these projects. Thus, this work aims to simulate aggregate planning strategies in housing construction projects for low-income populations financed by the government. The methodology consisted of the simulation of four aggregate planning strategies (demand monitoring, constant workforce allowing for delays, constant workforce without delays and mixed) using the entire linear programming technique. The methodology used was a two-year case study in twelve civil construction projects for a civil construction company whose market segment is low-income housing with public funding. The chosen strategy with the lowest cost was the mixed strategy, which presented the lowest cost, with hiring employees and no layoffs. The aggregate planning simulation also did not show delays in the 12 projects, proving to be an efficient alternative for the planning management of companies that operate several projects at the same time.
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Prata, A. P., Pitombeira – Neto, A. R., Sales, C. J. M.G. An integer linear model for the multiperiod production planning of precast concrete beams. Journal of Construction Engineering and Management, 141 (4) 2015. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001280.
Firouzi, A., Yang, W., Li, C. Q. Prediction of Total Cost of Construction Project with Dependent Cost Items. Journal of Construction Engineering and Management, 142 (12), 2016. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001280.
Borges, W. F., Mário, P. C. Five project-duration control methods in time units: case study of a linearly distributed planned value. Journal of Construction Engineering and Management, 143 (6), 2017, https://doi.org/10.1061/(ASCE)CO.1943-7862.0001280.
Sinesilassiea, E. G., Tabishb, S. Z. S., Jhaa, K. N. Critical factors affecting cost performance: a case of Ethiopian public construction projects. International Journal of Construction Management, 17 (1), 24-49, 2017. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001280.
Love, P. E. D, Irani, Z., Smith,J., Regan, M., Liu, J. Cost performance of public infrastructure projects: the nemesis and nirvana of change-orders. Production Planning & Control, 28 (13), 1081-1092, 2017. https://doi.org/10.1080/09537287.2011.631595.
Dallasega, P., Marengo, E., Revolti, A. Strengths and shortcomings of methodologies for production planning and control of construction projects: a systematic literature review and future perspectives. Journal Production Planning & Control, 2020. https://doi.org/10.1080/09537287.2020.1725170.
Shouten, C., and R. Wilson. Almost Two Thirds of Projects were Late in Past 12 Months. Architect’s Journal. 15 (1). 2015. https://www.architectsjournal.co.uk/home/almost-two-thirds-of-projects-werelate-in-past-12-months/8689116.article.
Emuze, F. Qualitative Content Analysis from the Lean Construction Perspective: A Focus on Supply Chain Management. Acta Structilia, 19 (1), 1–18. 2021.
Olanrewaju, A. L., and A. R. Abulaziz. An Overview of the Construction Industry. In Building Maintenance Processes and Practices: The Case of a Fast Developing Country, edited by A. A.-A. Olanrewaju, 9–34. 2015. Singapore: Springer.
Hussin, J. M., I. A. Rahman, and A. H. Memon. The Way Forward in Sustainable Construction: Issues and Challenges. International Journal of Advances in Applied Sciences 2 (1), 15–24. 2013. doi:10.11591/ijaas.v2i1.1321.
Dixon, W. The Impacts of Construction and the Built Environment.Briefing Notes, Willmott-Dixon Group. 2010.
Ola-Adisa, E., Y. C. Sati, and I. I. Ojonigwa. An Architectural Approach to Solid Waste Management on Selected Building Construction Sites in Bauchi. International Journal of Emerging Engineering Research and Technology, 3 (12), 67–77, 2015.
Tian, X., Mohamed, Y., and AbouRizk, S. Simulation-based aggregate planning of batchplant operations. Canadian Journal of Civil Engineering, 37 (10), 1277-1288, 2010. https://doi.org/10.1139/L10-071.
United Nations. World Social Report 2020: Inequality: a major threat to social progress. 2018. UN. https://trello.com/c/JMvJtE8B/9-media-advisory.
Klink, J.,Denaldi, R. On financialization and state spatial fixes in Brazil. A geographical and historical interpretation of the housing program My House My Life. Habitat International, 44, 220-226, 2014.https://doi.org/10.1016/j.habitatint.2014.06.001.
Mezghania, M., Loukila, T., Aoun, B. Aggregate planning through the imprecise goal programming model: integration of the manager’s preferences. International transactions in operational research, 19, 581-597, 2021. https://doi.org/10.1111/j.1475-3995.2012.00844.x.
Chakrabortty, R. K., Hasin, A. A. M, Sarker, R. A., Essam, D. L. A possibilistic environment based particle swarm optimization for aggregate production planning. Computers & Industrial Engineering, 88(1), 366-377, 2015. https://doi.org/10.1016/j.cie.2015.07.021.
Makui, A. Heydari, M. Aazami, A., Dehghani, E. Accelerating Benders decomposition approach for robust aggregate production planning of products with a very limited expiration date. Computers & Industrial Engineering. 100 (1), 34-51, 2016. https://doi.org/10.1016/j.cie.2016.08.005
Jamalnia, A., Feili, A. A simulation testing and analysis of aggregate production planning strategies. Production Planning & Control, 24 (6), 423-448, 2013. https://doi.org/10.1080/09537287.2011.631595.
Cheraghalikhani, A., Khoshalhan, F. and Mokhtari, H. Aggregate production planning: A literature review and future research directions. International Journal of Industrial Engineering Computations. 10 (2) 309–330, 2018. https://doi.org/10.5267/j.ijiec.2018.6.002.
Phruksaphanrat, B. Ario, O. and Pisal, Y. Aggregate production planning with fuzzy demand and variable system capacity based on theory of constraints measures. International Journal of Industrial Engineering, 18(5), 219-231, 2011.
Munhoz, J. R., Morabito, R. Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study. Computers and Electronics in Agriculture, 107(6), 45-57, 2014. https://doi.org/10.1016/j.compag.2014.05.016.
Liang, T. F. Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives. Production Planning and Control, 18(7), 548-560, 2007.
Erfanian, M., Pirayesh, M. Integration aggregate production planning and maintenance using mixed integer linear programming. In Industrial Engineering and Engineering Management (IEEM), 2016 IEEE International Conference on (pp. 927-930). IEEE.
Wang, S. C., Yeh, M. F. A modified particle swarm optimization for aggregate production planning. Expert Systems with Applications, 41, 3069-3077, 2014. https://doi.org/10.1016/j.eswa.2013.10.038.
Entezaminia, A., Heydari, M., Rahmani, D. A multi-objective model for multi-product multi-site aggregate production planning in a green supply chain: Considering collection and recycling centers. Journal of Manufacturing Systems, 40 (7), 63-75, 2016. https://doi.org/10.1016/j.jmsy.2016.06.004.
Demirel, E. Ozekan, E. e Lim, C. Aggregate planning with flexibility requirements profile. International Journal of Production Economics, 19 (8), 45-58, 2018. https://doi.org/10.1016/j.ijpe.2018.05.001.
Fernandes, F.C.F., Goldinho Filho, M.. Planejamento e controle da Produção dos fundamentos ao essencial. 2010. São Paulo: Atlas.
Techawiboonwong, T., Yenradee, P. Aggregate production planning with workforce transferring plan for multiple product types. Production Planning & Control, 14 (5), 447-458, 2003. https://doi.org/10.1080/09537287.2011.631595.
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