Data-Driven Optimization of Resource Allocation in Wastewater Treatment Plant Construction Projects Using Hybrid Simulation Models

References

[1] AbouRizk, S. M. (2010). Role of simulation in construction engineering and management. Journal of Construction Engineering and Management, 136(10), 1140–1153. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000220

[2] Ahmad, I., Azhar, S., & Lukauskis, P. (2004). Development of a decision support system using data warehousing to assist builders in marketing new housing projects. Automation in Construction, 13(4), 525–541. https://doi.org/10.1016/j.autcon.2003.12.003

[3] Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-event system simulation (5th ed.). Pearson Education.

[4] Cherukuri, B. R. Enhancing Web Application Performance with AI-Driven Optimization Techniques.

[5] Forrester, J. W. (1961). Industrial dynamics. MIT Press.

[6] Gambatese, J. A., & Hallowell, M. (2011). Enabling and measuring innovation in the construction industry. Construction Management and Economics, 29(6), 553–567. https://doi.org/10.1080/01446193.2011.570357

[7] González, V. A., Alarcón, L. F., & Molenaar, K. R. (2013). Multi-objective optimization for resource allocation in project scheduling. Automation in Construction, 30, 177–186. https://doi.org/10.1016/j.autcon.2012.11.027

[8] Love, P. E. D., Edwards, D. J., & Smith, J. (2016). Rework causation: Emergent theoretical insights and implications for practice. Building Research & Information, 44(5–6), 568–583. https://doi.org/10.1080/09613218.2016.1139580

[9] Lv, J., Wang, Y., Yang, J., & Zhang, Z. (2020). A hybrid simulation-based decision support model for wastewater treatment plant expansion planning. Journal of Environmental Management, 270, 110906. https://doi.org/10.1016/j.jenvman.2020.110906

[10] Mohagheghi, V., Mousavi, S. M., & Vahdani, B. (2020). A robust simulation-based multi-objective optimization model for resource allocation in project scheduling under uncertainty. Computers & Industrial Engineering, 149, 106837. https://doi.org/10.1016/j.cie.2020.106837

[11] Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Irwin/McGraw-Hill.

[12] Masurkar, P. P., Chatterjee, S., Sherer, J. T., Chen, H., Johnson, M.L., & Aparasu, R. R. (2022). Risk of serious adverse events associated with individual cholinesterase inhibitors use in older adults with dementia: A population-based cohort study. Drugs & Aging, 39(6), 453-465.

[13] Zhang, H. (2007). A reengineering framework for construction scheduling using a hybrid simulation approach. Journal of Construction Engineering and Management, 133(1), 73–81. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:1(73)

[14] Alvanchi, A., Lee, S., & AbouRizk, S. M. (2012). Dynamics of working hours in construction. Construction Management and Economics, 30(10), 777–790. https://doi.org/10.1080/01446193.2012.703701

[15] Barton, D. N., Saloranta, T. M., Moe, S. J., Eggestad, H. O., & Kuikka, S. (2008). Bayesian belief networks as a meta-modelling tool in integrated river basin management—Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin. Ecological Economics, 66(1), 91–104. https://doi.org/10.1016/j.ecolecon.2008.02.012

[16] Cherukuri, B. R. Developing Intelligent Chatbots for Real-Time Customer Support in E-Commerce.

[17] Cheng, M. Y., & O'Connor, J. T. (1996). Scheduling of multiple projects with resource constraints. Journal of Construction Engineering and Management, 122(3), 236–246. https://doi.org/10.1061/(ASCE)0733-9364(1996)122:3(236)

[18] Coelho, D., Relvas, S., Barbosa-Póvoa, A. P. (2012). Simulation-based optimization approach for designing and planning a waste collection system. Computers & Chemical Engineering, 45, 162–174. https://doi.org/10.1016/j.compchemeng.2012.06.003

[19] Kilari, S. D. (2022, February). Optimizing manufacturing systems with AI: Reducing human errors and enhancing response times in MES and supply chain ordering systems. International Journal of Engineering Technology Research & Management, 6(2). IJETRM.

[20] de Carvalho, M. M., Patah, L. A., & de Souza Bido, D. (2015). Project management and its effects on project success: Cross-country and cross-industry comparisons. International Journal of Project Management, 33(7), 1509–1522. https://doi.org/10.1016/j.ijproman.2015.04.004

[21] Fayek, A. R., Dissanayake, M., & Campero, O. (2003). Development of a fuzzy expert system for selection of construction projects. Automation in Construction, 12(5), 597–611. https://doi.org/10.1016/S0926-5805(03)00050-2

[22] Cherukuri, B. R. (2019). Future of cloud computing: Innovations in multi-cloud and hybrid architectures.

[23] Hajjar, D., & AbouRizk, S. M. (2002). Unified modeling methodology for construction simulation. Journal of Construction Engineering and Management, 128(2), 174–185. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:2(174)

[24] Hassanein, A., & Moselhi, O. (2004). Planning and scheduling of construction projects using simulation. Canadian Journal of Civil Engineering, 31(3), 471–478. https://doi.org/10.1139/l04-008

[25] Jahangoshai Rezaee, M., Yousefi, S., & Eivazy, H. (2019). A data-driven simulation-based optimization model for workforce scheduling in construction projects. Engineering, Construction and Architectural Management, 26(7), 1362–1381. https://doi.org/10.1108/ECAM-12-2017-0261

[26] Kannan, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(1), 28–36. https://doi.org/10.1016/j.resconrec.2009.06.003

[27] Jangid, J. (2020). Efficient Training Data Caching for Deep Learning in Edge Computing Networks.

[28] Cherukuri, B. R. (2020). Ethical AI in cloud: Mitigating risks in machine learning models.

[29] Love, P. E. D., Sing, C. P., & Irani, Z. (2005). An empirical analysis of the barriers to implementing e-commerce in small-medium sized construction contractors in the state of Victoria, Australia. Construction Innovation, 5(1), 31–41. https://doi.org/10.1108/14714170510815079

[30] Lu, M., Lam, H. C., & Dai, F. (2008). Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization. Automation in Construction, 17(6), 670–681. https://doi.org/10.1016/j.autcon.2008.01.003

[31] Ruwanpura, J. Y., & Mohamed, Y. (2003). Construction project planning: A hybrid simulation optimization approach. Proceedings of the 2003 Winter Simulation Conference, 1533–1540. https://doi.org/10.1109/WSC.2003.1261591

[32] Sawhney, A., & AbouRizk, S. M. (1995). Simulation modeling process for construction operations. Journal of Construction Engineering and Management, 121(3), 289–296. https://doi.org/10.1061/(ASCE)0733-9364(1995)121:3(289)

[33] Zhao, Z. Y., & Lv, C. Q. (2016). Decision-making model for public infrastructure project selection based on a hybrid multi-criteria decision-making approach. International Journal of Project Management, 34(5), 847–856. https://doi.org/10.1016/j.ijproman.2016.03.005

@article{1703893,
      author = {Pratik Bhikhubhai Panchal},
      title = {Data-Driven Optimization of Resource Allocation in Wastewater Treatment Plant Construction Projects Using Hybrid Simulation Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {5},
      pages = {210-222},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703893.pdf},
      abstract = {Continued development of wastewater treatment plants (WWTPs) as essential sustainable urban infrastructure encounters leading barriers from poor resource planning and resulting budget excesses and building delays. Traditionally, Planning methods show difficulty adjusting for complex WWTP projects mainly because uncertainty exists regarding labor productivity, material availability, and environmental limitations. The article investigates data-based optimization techniques for WWTP resource distribution through combination models of Discrete Event Simulation (DES) with System Dynamics (SD). Such integrated models help projects reach more accurate risk assessments by analyzing both stepwise operational occurrences and sustained interlocking processes that influence extended execution needs. This hybrid simulation framework uses construction data history, site inputs, predictive analytics, and optimization algorithms to analyze several resource allocation options. The approach used in a case study shows that it elevates decision-making abilities, resulting in better projects, expenses, and eq resource management. A sensitivity analysis method within the research checks how different conditions affect the model's stability. Hybrid simulation techniques lead to positive project results and sustainable infrastructure development by aligning construction methods with environmental and operational objectives. This article ends with recommendations for project managers and policymakers, followed by proposed research avenues involving connecting with digital twin technology and live IoT data streams.},
      keywords = {Wastewater Treatment Plants, Construction Optimization, Resource Allocation, Hybrid Simulation Models, Discrete Event Simulation, System Dynamics, Data-Driven Decision Making.},
      month = {November},
  }