Hybrid GA–MILP Model for Community Building Retrofit Planning Towards Carbon Neutrality

Authors

  • Chairini Aisyah Department of Visual Communication Design and Architecture, Institut Modern Arsitektur dan Teknologi, Indonesia
  • Adhita Nugraha Mestika Department of Visual Communication Design and Architecture, Institut Modern Arsitektur dan Teknologi, Indonesia

DOI:

10.33395/sinkron.v9i4.15315

Keywords:

Building retrofit; Genetic algorithm; Emission reduction; Energy planning; Carbon neutrality

Abstract

Retrofitting community buildings is a key pathway toward carbon neutrality, yet most existing retrofit planning models lack adaptability to the diverse urban contexts of the Global South, where building typologies are heterogeneous and resources limited. Addressing this gap requires approaches that are both computationally efficient and context-sensitive. This study introduces a hybrid optimization framework that integrates Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) to tackle the multidimensional multiple-choice knapsack problem inherent in retrofit planning. The GA explores high-level system configurations, while MILP ensures precise component-level selection under budget and technical constraints. Compared to conventional single-method approaches, the hybrid GA–MILP achieves near-optimal emission reduction with reduced computation time and greater feasibility, offering a balanced trade-off between performance and scalability. Importantly, the framework demonstrates that medium-cost retrofit strategies provide the most cost-effective path to scalable carbon savings, making it highly relevant for resource-constrained urban environments. By situating retrofit planning within the realities of the Global South, this study advances methodological innovation and provides a robust decision-support tool aligned with sustainable development goals for inclusive and low-carbon urban futures.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Aruta, G., Ascione, F., Bianco, N., & Mauro, G. M. (2025). Incentive policies for building energy retrofit: A new multi-objective optimization framework to trade-off private and public interests. Journal of Cleaner Production, 498, 145142. https://doi.org/10.1016/J.JCLEPRO.2025.145142

Cardoso de Oliveira, G., Bertone, E., Stewart, R. A., Sanjari, M. J., & Bortoni, E. C. (2025). Adaptive nested robust multi-objective optimisation for integrated precinct-scale energy–water system planning. Energy, 332, 136552. https://doi.org/10.1016/J.ENERGY.2025.136552

Casalicchio, V., Barchi, G., Calabria, F., Manzolini, G., Prina, M. G., & Moser, D. (2025). Advancing renewable energy community planning through integrated sector-coupling and economies of scale. Applied Energy, 395, 125942. https://doi.org/10.1016/J.APENERGY.2025.125942

Cerezo, C., Sokol, J., AlKhaled, S., Reinhart, C., Al-Mumin, A., & Hajiah, A. (2017). Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City. Energy and Buildings, 154, 321–334. https://doi.org/10.1016/J.ENBUILD.2017.08.029

Charles, H., Bouzarovski, S., Bellamy, R., & Gormally-Sutton, A. (2025). ‘Although it’s my home, it’s not my house’ – Exploring impacts of retrofits with social housing residents. Energy Research & Social Science, 119, 103869. https://doi.org/10.1016/J.ERSS.2024.103869

Cosic, A., Stadler, M., Mansoor, M., & Zellinger, M. (2021). Mixed-integer linear programming based optimization strategies for renewable energy communities. Energy, 237, 121559. https://doi.org/10.1016/J.ENERGY.2021.121559

D’Agostino, D., Minelli, F., & Minichiello, F. (2025). An innovative multi-stakeholder decision methodology for the optimal energy retrofit of shopping mall buildings. Energy and Buildings, 344, 115958. https://doi.org/10.1016/J.ENBUILD.2025.115958

Dell’Anna, F. (2025). Machine learning framework for evaluating energy performance certificate (EPC) effectiveness in real estate: A case study of Turin’s private residential market. Energy Policy, 198, 114407. https://doi.org/10.1016/J.ENPOL.2024.114407

Energy Performance of Buildings Directive. (n.d.). Retrieved June 22, 2025, from https://energy.ec.europa.eu/topics/energy-efficiency/energy-efficient-buildings/energy-performance-buildings-directive_en

Fan, Y., & Xia, X. (2018). Building retrofit optimization models using notch test data considering energy performance certificate compliance. Applied Energy, 228, 2140–2152. https://doi.org/10.1016/J.APENERGY.2018.07.043

Follador, V., Donà, M., Carpanese, P., Saler, E., D’Alpaos, C., & da Porto, F. (2024). Seismic retrofit cost model for Italian masonry residential buildings to support territorial-scale risk analysis. International Journal of Disaster Risk Reduction, 105, 104373. https://doi.org/10.1016/J.IJDRR.2024.104373

Golzar, F., Heeren, N., Hellweg, S., & Roshandel, R. (2018). A novel integrated framework to evaluate greenhouse energy demand and crop yield production. Renewable and Sustainable Energy Reviews, 96, 487–501. https://doi.org/10.1016/J.RSER.2018.06.046

Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–72. https://doi.org/10.1038/SCIENTIFICAMERICAN0792-66

International Energy Agency. (2023). IEA (2021), World Energy Balances: Overview, IEA, Paris. World Energy Balances.

Jiang, T., Dong, X., Zhang, R., & Li, X. (2023). Strategic active and reactive power scheduling of integrated community energy systems in day-ahead distribution electricity market. Applied Energy, 336, 120558. https://doi.org/10.1016/J.APENERGY.2022.120558

Lima, R. A. de O., & Guirardello, R. (2025). Long term turnaround planning for an oil refinery using a MILP model. Computers & Chemical Engineering, 194, 108999. https://doi.org/10.1016/J.COMPCHEMENG.2025.108999

Liu, Y., Mei, Y., Zhang, M., & Zhang, Z. (2019). A predictive-reactive approach with genetic programming and cooperative coevolution for the uncertain capacitated arc routing problem. Evolutionary Computation, 28(2). https://doi.org/10.1162/evco_a_00256

Martello, S., & Toth, P. (1990). An exact algorithm for large unbounded knapsack problems. Operations Research Letters, 9(1). https://doi.org/10.1016/0167-6377(90)90035-4

Mohseni-Gharyehsafa, B., Hussain, S., Fahy, A., De Rosa, M., & Pallonetto, F. (2025). A hybrid Gaussian process-integrated deep learning model for retrofitted building energy optimization in smart city ecosystems. Applied Energy, 388, 125643. https://doi.org/10.1016/J.APENERGY.2025.125643

Park, S., Choi, S. W., & Choi, I. (2024). Seismic retrofitting optimization model using fiber-reinforced polymer jacketing and NSGA-III. Developments in the Built Environment, 19, 100508. https://doi.org/10.1016/J.DIBE.2024.100508

Perger, T., Zwickl-Bernhard, S., Golab, A., & Auer, H. (2022). A stochastic approach to dynamic participation in energy communities. Elektrotechnik Und Informationstechnik, 139(8), 644–661. https://doi.org/10.1007/S00502-022-01069-2/TABLES/3

Seok, U., Byun, J. E., & Song, J. (2025). Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy. Structural Safety, 114, 102556. https://doi.org/10.1016/J.STRUSAFE.2024.102556

Sesana, M. M., Salvalai, G., Brutti, D., Mandin, C., & Wei, W. (2021). ALDREN: A methodological framework to support decision-making and investments in deep energy renovation of non-residential buildings. Buildings, 11(1). https://doi.org/10.3390/buildings11010003

Shakeel, H. M., Farid, H. M. A., Iram, S., Hill, R., & Simic, V. (2025). Multi-stage decision support system for evaluating visual energy performance certificate platforms. Sustainable Energy, Grids and Networks, 43, 101767. https://doi.org/10.1016/J.SEGAN.2025.101767

Shen, P., & Wang, H. (2024). Archetype building energy modeling approaches and applications: A review. Renewable and Sustainable Energy Reviews, 199, 114478. https://doi.org/10.1016/J.RSER.2024.114478

Torres-Rivas, A., Palumbo, M., Jiménez, L., & Boer, D. (2022). Self-consumption possibilities by rooftop PV and building retrofit requirements for a regional building stock: The case of Catalonia. Solar Energy, 238, 150–161. https://doi.org/10.1016/J.SOLENER.2022.04.036

Zuhanda, M. K., Hartono, Hasibuan, S. A. R. S., & Napitupulu, Y. Y. (2024). An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence. Decision Analytics Journal, 13. https://doi.org/10.1016/j.dajour.2024.100517

Zuhanda, M. K., Mawengkang, H., Suwilo, S., Mardiningsih, & Sitompul, O. S. (2023). Logistics distribution supply chain optimization model with VRP in the context of E-commerce. AIP Conference Proceedings, 2714. https://doi.org/10.1063/5.0128465

Zwickl-Bernhard, S., & Auer, H. (2021). Open-source modeling of a low-carbon urban neighborhood with high shares of local renewable generation. Applied Energy, 282, 116166. https://doi.org/10.1016/J.APENERGY.2020.116166

Downloads


Crossmark Updates

How to Cite

Aisyah, C., & Mestika, A. N. (2025). Hybrid GA–MILP Model for Community Building Retrofit Planning Towards Carbon Neutrality. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(4), 2139-2148. https://doi.org/10.33395/sinkron.v9i4.15315

Issue

Vol. 9 No. 4 (2025): Articles Research October 2025

Section

Articles

License

Copyright (c) 2025 Chairini Aisyah, Adhita Nugraha Mestika

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC