Application of Digitally Supported Linear Programming and Decision Matrix in Road Transportation Project Planning
Keywords:
Applied mathematics, civil engineering, decision matrix, linear programming, road transportation planningAbstract
This study investigates the application of linear programming and decision matrix methods, supported by digital tools, in the context of road transportation project planning. The objective is to demonstrate how applied mathematical models can be integrated within a digital framework to optimize decision-making processes in civil infrastructure projects. A quantitative research design was employed using real data from a road development project, including cost estimates, resource requirements, and scheduling components. Technical and financial data were collected from project documentation and validated through expert consultation. The study utilized linear programming to determine the optimal allocation of resources under multiple constraints, and applied decision matrix analysis to evaluate alternative execution plans based on criteria such as cost efficiency, time effectiveness, and environmental impact. The findings showed that the integrated model successfully minimized project cost and duration while satisfying all technical constraints. Among the evaluated alternatives, the highest-ranked execution plan offered a balance between speed and sustainability. The use of mathematical models in combination with digital analysis tools enabled a transparent, structured, and data-driven decision-making process that reflects modern demands in infrastructure planning. This research presents a digitally supported framework that integrates optimization and multicriteria decision-making models, emphasizing the critical role of applied mathematics in improving planning quality and resource efficiency in transportation engineering.Downloads
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References
Ahankoob, A., Niazi, M., & Bakhtiyari, M. (2022). Integrating digital decision-making tools into civil infrastructure planning: A framework proposal. Journal of Infrastructure Systems, 28(1), 04021063. https://doi.org/10.1061/JIS.0000347
Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, A. O., Akinade, O. O., & Pasha, M. (2016). Big data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500–521. https://doi.org/10.1016/j.aei.2016.07.001
Nazari, A., Fallah, M. M., & Hashemkhani Zolfani, S. (2020). Sustainable road construction: A decision-making model using AHP and TOPSIS. Journal of Cleaner Production, 267, 122019. https://doi.org/10.1016/j.jclepro.2020.122019
Samet, R., & Shojaei, A. (2020). Mathematical modeling and optimization in civil engineering: A review. Journal of Civil Engineering and Management, 26(5), 417–432. https://doi.org/10.3846/jcem.2020.12635
Shang, Y., & Abourizk, S. M. (2018). Modeling construction project decision processes using digital simulations. Automation in Construction, 94, 75–84. https://doi.org/10.1016/j.autcon.2018.06.005
Sharma, A., & Tiwari, M. K. (2019). Linear programming techniques in construction management. Journal of Construction Engineering and Management, 145(2), 04018132. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001586
Taha, H. A. (2017). Operations Research: An Introduction (10th ed.). Pearson Education.
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2016). Multi-criteria decision-making (MCDM) methods in civil engineering: Part I–A state-of-the-art survey. Archives of Civil and Mechanical Engineering, 16(1), 1–13. https://doi.org/10.1016/j.acme.2015.10.003
Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, A. O., Akinade, O. O., & Pasha, M. (2016). Big data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500–521. https://doi.org/10.1016/j.aei.2016.07.001
Nazari, A., Fallah, M. M., & Hashemkhani Zolfani, S. (2020). Sustainable road construction: A decision-making model using AHP and TOPSIS. Journal of Cleaner Production, 267, 122019. https://doi.org/10.1016/j.jclepro.2020.122019
Samet, R., & Shojaei, A. (2020). Mathematical modeling and optimization in civil engineering: A review. Journal of Civil Engineering and Management, 26(5), 417–432. https://doi.org/10.3846/jcem.2020.12635
Shang, Y., & Abourizk, S. M. (2018). Modeling construction project decision processes using digital simulations. Automation in Construction, 94, 75–84. https://doi.org/10.1016/j.autcon.2018.06.005
Sharma, A., & Tiwari, M. K. (2019). Linear programming techniques in construction management. Journal of Construction Engineering and Management, 145(2), 04018132. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001586
Taha, H. A. (2017). Operations Research: An Introduction (10th ed.). Pearson Education.
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2016). Multi-criteria decision-making (MCDM) methods in civil engineering: Part I–A state-of-the-art survey. Archives of Civil and Mechanical Engineering, 16(1), 1–13. https://doi.org/10.1016/j.acme.2015.10.003
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Published
2025-06-30
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How to Cite
Application of Digitally Supported Linear Programming and Decision Matrix in Road Transportation Project Planning. (2025). Proceeding of International Conference on Economics, Technology, Management, Accounting, Education, and Social Science (ICETEA), 1, 43-50. https://conference.unita.ac.id/index.php/icetea/article/view/328
