Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Multi-objective optimization of a regional biogas supply chain using organic waste
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 08 March 2026

Multi-objective optimization of a regional biogas supply chain using organic waste

  • Ivan P. Malashin1,
  • Dmitry Martysyuk1,
  • Vladimir Nelyub1,
  • Aleksei Borodulin1,
  • Andrei Gantimurov1 &
  • …
  • Vadim Tynchenko1 

Scientific Reports , Article number:  (2026) Cite this article

  • 1158 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental impact
  • Renewable energy

Abstract

The growing volume of agricultural and municipal organic residues presents both a waste-management challenge and an opportunity for low-carbon energy production. This study develops a multi-objective optimization model to support the strategic design of regional biogas supply chains, explicitly accounting for economic, environmental, and system-resilience criteria. The framework is demonstrated using data from a representative biogas plant case study to explore alternative system configurations and trade-offs among total cost, greenhouse gas emissions, energy revenue, resource consumption, and supply reliability. Results indicate that moderate investment levels allow substantial emission reductions, whereas beyond a certain threshold additional expenditure yields progressively smaller environmental benefits. Likewise, increasing plant throughput raises energy revenue, but the incremental financial return declines as capacity limits are approached. Sensitivity analysis shows that feedstock price and biogas yield have the strongest influence on overall system performance, while discount rate and transport-related emissions exert secondary effects. More balanced sourcing from multiple suppliers improves resilience and can enable further emission reductions without increasing capital cost. The proposed framework provides a transparent decision-support tool for designing economically viable and environmentally balanced biogas systems.

Data availability

The datasets and code used to support the findings of this study are available in the public GitHub repository: https://github.com/catauggie/BNG.

References

  1. Mahjoub, N., Sahebi, H., Mazdeh, M. & Teymouri, A. Optimal design of the second and third generation biofuel supply network by a multi-objective model. J. Clean. Prod. 256, 120355 (2020).

    Google Scholar 

  2. Durmaz, Y. G. & Bilgen, B. Multi-objective optimization of sustainable biomass supply chain network design. Appl. Energy 272, 115259 (2020).

    Google Scholar 

  3. Ferdous, J. et al. Development of a generic decision tree for the integration of multi-criteria decision-making (mcdm) and multi-objective optimization (moo) methods under uncertainty to facilitate sustainability assessment: A methodical review. Sustainability 16, 2684 (2024).

    Google Scholar 

  4. Tangi, M. & Amaranto, A. Designing integrated and resilient multi-energy systems via multi-objective optimization and scenario analysis. Appl. Energy 382, 125281 (2025).

    Google Scholar 

  5. Tırınk, S. Multi-objective optimization in sustainable energy. Optimization in Sustainable Energy: Methods and Applications 267–289 (2026).

  6. Zhou, T. et al. Multi-objective optimization of straw-based bio-natural gas supply chains considering cost, CO2 emission, and safety. J. Clean. Prod. 449, 141759 (2024).

    Google Scholar 

  7. Ramos, N. M., Del-Mazo-Alvarado, O., Bonilla-Petriciolet, A., de Lima Luz, L. F. Jr. & Corazza, M. L. Multi-objective optimization of syngas production for Fischer-Tropsch synthesis based on biogas catalytic reforming and upgrading. Chem. Eng. Process. Process Intensif. 199, 109758 (2024).

  8. Wang, M., Ji, L., Xie, Y. & Huang, G. Regional bioethanol supply chain optimization with the integration of gis-mcdm method and quantile-based scenario analysis. J. Environ. Manage. 351, 119883 (2024).

    Google Scholar 

  9. Van Veldhuizen, D. A. et al. Evolutionary computation and convergence to a pareto front. In Late breaking papers at the genetic programming 1998 conference, 221–228 (1998).

  10. Huang, X., Ji, L., Xie, Y. & Luo, Z. Robust optimization of regional biomass supply chain system design and operation with data-driven uncertainties. Food Bioprod. Process. 149, 176–189 (2025).

    Google Scholar 

  11. Christianides, D. et al. Biogenic CO2 emissions in the EU biofuel and bioenergy sector: Mapping sources, regional trends, and pathways for capture and utilisation. Energies 18, 1345 (2025).

    Google Scholar 

  12. Miller, J., Clark, C. M., Peterson, S. & Newes, E. Estimated attribution of the RFS program on soybean biodiesel in the US using the bioenergy scenario model. Energy Policy 192, 114250 (2024).

    Google Scholar 

  13. Nandimandalam, H., Aghalari, A., Gude, V. G. & Marufuzzaman, M. Multi-objective optimization model for regional renewable biomass supported electricity generation in rural regions. Energy Convers. Manag. 266, 115833 (2022).

    Google Scholar 

  14. Tesfamichael, B., Montastruc, L., Negny, S. & Yimam, A. Designing and planning of Ethiopia’s biomass-to-biofuel supply chain through integrated strategic-tactical optimization model considering economic dimension. Comput. Chem. Eng. 153, 107425 (2021).

    Google Scholar 

  15. Khan, S. A. & Maaz, M. A. M. Assessing risks in the circular economy of the advanced biofuel industry. In Advanced Biofuels and Circular Economy: Technoeconomic, Socioeconomic, and Environmental Implications, 49–75 (Springer, 2025).

  16. Aboytes-Ojeda, M., Castillo-Villar, K. K. & Cardona-Valdés, Y. Bi-objective stochastic model for the design of biofuel supply chains incorporating risk. Expert Syst. Appl. 202, 117285 (2022).

    Google Scholar 

  17. Jana, D. K., Bhattacharjee, S., Dostál, P., Janková, Z. & Bej, B. Bi-criteria optimization of cleaner biofuel supply chain model by novel fuzzy goal programming technique. Cleaner Logist. Supply Chain 4, 100044 (2022).

    Google Scholar 

  18. Kirkpatrick, S., Gelatt, C. D. Jr. & Vecchi, M. P. Optimization by simulated annealing. science 220, 671–680 (1983).

    Google Scholar 

  19. Bertsimas, D. & Tsitsiklis, J. Simulated annealing. Statistical science 8, 10–15 (1993).

    Google Scholar 

  20. Kim, Y. & Kim, S. Optimization and simulation in biofuel supply chain. Energies 18, 1194 (2025).

    Google Scholar 

  21. Savoji, H., Mousavi, S. M., Antucheviciene, J. & Pavlovskis, M. A robust possibilistic bi-objective mixed integer model for green biofuel supply chain design under uncertain conditions. Sustainability 14, 13675 (2022).

    Google Scholar 

  22. Garai, A., Chowdhury, S., Sarkar, B. & Roy, T. K. Cost-effective subsidy policy for growers and biofuels-plants in closed-loop supply chain of herbs and herbal medicines: An interactive bi-objective optimization in t-environment. Appl. Soft Comput. 100, 106949 (2021).

    Google Scholar 

  23. El Kourdi, S., Abderafi, S., Cheddadi, A., Mabrouki, J. & Abbassi, M. A. Simulation and multi-objective optimization of Argan residues slow pyrolysis for polygeneration of bio-oil, biochar, and gas products. Energy Convers. Manag. 304, 118206 (2024).

    Google Scholar 

  24. Esteves, E. M., Brigagão, G. V. & Morgado, C. R. Multi-objective optimization of integrated crop-livestock system for biofuels production: A life-cycle approach. Renew. Sustain. Energy Rev. 152, 111671 (2021).

    Google Scholar 

  25. Machado, R. & Abreu, M. Multi-objective optimization of the first and second-generation ethanol supply chain in Brazil using the water-energy-food-land nexus approach. Renew. Sustain. Energy Rev. 193, 114299 (2024).

    Google Scholar 

  26. Ge, Y., Li, L. & Yun, L. Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways. Appl. Energy 281, 116059 (2021).

    Google Scholar 

  27. Mamo, T. Integrated strategic, tactical and operational planning of sugarcane-based biomass to biofuel supply chains: a hybrid optimization-surrogate approach. Ph.D. thesis, Université de Toulouse; Addis Ababa university (2024).

  28. Gutierrez-Franco, E., Polo, A., Clavijo-Buritica, N. & Rabelo, L. Multi-objective optimization to support the design of a sustainable supply chain for the generation of biofuels from forest waste. Sustainability 13, 7774 (2021).

    Google Scholar 

  29. Lin, C.-C., Kang, J.-R., Huang, G.-L. & Liu, W.-Y. Forest biomass-to-biofuel factory location problem with multiple objectives considering environmental uncertainties and social enterprises. J. Clean. Prod. 262, 121327 (2020).

    Google Scholar 

  30. Co, D. V. Y., Lim, A. S. O., Ng, R. C. N., Sy, K. W. V. & San Juan, J. L. Integrating heterogenous robustness levels to the multi-objective target-oriented robust optimization of a microalgal biorefinery. J. Clean. Prod. 475, 143675 (2024).

    Google Scholar 

  31. Ransikarbum, K. & Pitakaso, R. Multi-objective optimization design of sustainable biofuel network with integrated fuzzy analytic hierarchy process. Expert Syst. Appl. 240, 122586 (2024).

    Google Scholar 

  32. Becker, T. et al. An integrated bi-objective optimization model accounting for the social acceptance of renewable fuel production networks. Eur. J. Oper. Res. 315, 354–367 (2024).

    Google Scholar 

  33. TransUT LLC. Company profile and project portfolio. https://transut.ru/o-kompanii (2023). Accessed: 30 October 2025.

  34. Yeng, F. F., Zainuddin, Z. M. & Pheng, H. S. Optimizing palm oil biomass supply chain logistics through multi-objective location-routing model. Malays. J. Fundam. Appl. Sci. 20, 247–265 (2024).

    Google Scholar 

  35. Raschke, A., Hernandez-Suarez, J. S., Nejadhashemi, A. P. & Deb, K. Multidimensional aspects of sustainable biofuel feedstock production. Sustainability 13, 1424 (2021).

    Google Scholar 

  36. English, B. C., Menard, R. J. & Wilson, B. The economic impact of a renewable biofuels/energy industry supply chain using the renewable energy economic analysis layers modeling system. Front. Energy Res. 10, 780795 (2022).

    Google Scholar 

  37. Czekała, W. et al. Digestate management in Polish farms as an element of the nutrient cycle. J. Clean. Prod. 242, 118454 (2020).

    Google Scholar 

  38. Winkler, M. S. et al. Baseline health conditions in selected communities of Northern Sierra Leone as revealed by the health impact assessment of a biofuel project. Int. Health 6, 232–241 (2014).

    Google Scholar 

  39. Feldmeyer, D., Wilden, D., Jamshed, A. & Birkmann, J. Regional climate resilience index: A novel multimethod comparative approach for indicator development, empirical validation and implementation. Ecol. Indic. 119, 106861 (2020).

    Google Scholar 

  40. Shi, Z. et al. Combined nitrogen and phosphorus management based on nitrate nitrogen threshold for balancing crop yield and soil nitrogen supply capacity. Agric. Ecosyst. Environ. 337, 108071 (2022).

    Google Scholar 

  41. Wang, M., Zhu, J. & Mao, X. Removal of pathogens in onsite wastewater treatment systems: A review of design considerations and influencing factors. Water 13, 1190 (2021).

    Google Scholar 

  42. Ratcliffe, J. et al. Valuing the child health utility 9d: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Soc. Sci. Med. 157, 48–59 (2016).

    Google Scholar 

  43. Agustina, F., Vanany, I. & Siswanto, N. Resilience assessment model for biodiesel supply chain: An Indonesian case study. Biofuels 15, 1117–1130 (2024).

    Google Scholar 

  44. Yan, Y., Liu, Z. & Liu, J. Computational analysis of ammonia-hydrogen blends in homogeneous charge compression ignition engine operation. Process Saf. Environ. Prot. 190, 1263–1272 (2024).

    Google Scholar 

  45. Francisco López, A., Lago Rodríguez, T., Faraji Abdolmaleki, S., Galera Martínez, M. & Bello Bugallo, P. M. From biogas to biomethane: An in-depth review of upgrading technologies that enhance sustainability and reduce greenhouse gas emissions. Appl. Sci. (Basel) 14, 2342 (2024).

    Google Scholar 

  46. Wang, H. et al. Model construction and multi-objective performance optimization of a biodiesel-diesel dual-fuel engine based on CNN-GRU. Energy 301, 131586 (2024).

    Google Scholar 

  47. Aslam, N., Yang, W., Saeed, R. & Ullah, F. Energy transition as a solution for energy security risk: Empirical evidence from BRI countries. Energy 290, 130090 (2024).

    Google Scholar 

  48. Garcia-Herrero, L., Gibin, D., Damiani, M., Sanye-Mengual, E. & Sala, S. What is the water footprint of EU food consumption? A comparison of water footprint assessment methods. J. Clean. Prod. 415, 137807 (2023).

    Google Scholar 

  49. Ro, J. W., Zhang, Y. & Kendall, A. Developing guidelines for waste designation of biofuel feedstocks in carbon footprints and life cycle assessment. Sustain. Prod. Consum. 37, 320–330 (2023).

    Google Scholar 

  50. Li, J., Xiong, F. & Chen, Z. An integrated life cycle and water footprint assessment of nonfood crops based bioenergy production. Sci. Rep. 11, 3912 (2021).

    Google Scholar 

  51. Biswas, A., Mailapalli, D. R. & Raghuwanshi, N. S. Treated municipal wastewater to fulfil crop water footprints and irrigation demand-a review. Water Supply 21, 1398–1409 (2021).

    Google Scholar 

  52. Wu, T., Liu, K., Cheng, X. & Zhang, J. Analysis of energy, carbon emissions and economics during the life cycle of biomass power generation: Case comparison from China. Biomass Bioenergy 182, 107098 (2024).

    Google Scholar 

  53. Arefizadeh, M., Behvandi, D., Shahhosseini, S. & Ghaemi, A. Enhancement of ultrasonic-assisted and agitation-assisted flaxseed oil extractions: Kinetic modeling and optimization. Results Eng. 24, 102847 (2024).

    Google Scholar 

  54. Katić, D., Krstić, H., Otković, I. I. & Juričić, H. B. Comparing multiple linear regression and neural network models for predicting heating energy consumption in school buildings in the Federation of Bosnia and Herzegovina. J. Build. Eng. 97, 110728 (2024).

    Google Scholar 

  55. Tayefeh, A., Aslani, A., Zahedi, R. & Yousefi, H. Reducing energy consumption in a factory and providing an upgraded energy system to improve energy performance. Clean. Energy Syst. 8, 100124 (2024).

    Google Scholar 

  56. Kim, S., Ofekeze, E., Kiniry, J. R. & Kim, S. Simulation-based capacity planning of a biofuel refinery. Agronomy (Basel) 10, 1702 (2020).

    Google Scholar 

  57. Bjelić, I. B. & Rajaković, N. Simulation-based optimization of sustainable national energy systems. Energy 91, 1087–1098 (2015).

    Google Scholar 

  58. Saaty, T. L. Decision-making with the AHP: Why is the principal eigenvector necessary. Eur. J. Oper. Res. 145, 85–91 (2003).

    Google Scholar 

  59. Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6, 182–197 (2002).

    Google Scholar 

  60. García-Cascallana, J., Carrillo-Peña, D., Morán, A., Smith, R. & Gómez, X. Energy balance of turbocharged engines operating in a WWTP with thermal hydrolysis. Co-digestion provides the full plant energy demand. Appl. Sci. 11, 11103 (2021).

    Google Scholar 

  61. Perpiña Castillo, C., Lavalle, C., Baranzelli, C. & Mubareka, S. Modelling the spatial allocation of second-generation feedstock (lignocellulosic crops) in Europe. Int. J. Geogr. Inf. Sci. 29, 1807–1825 (2015).

    Google Scholar 

  62. Cruz, J. B. Jr., Tan, R. R., Culaba, A. B. & Ballacillo, J.-A. A dynamic input-output model for nascent bioenergy supply chains. Appl. Energy 86, S86–S94 (2009).

    Google Scholar 

  63. Ranjbari, M. et al. Biofuel supply chain management in the circular economy transition: An inclusive knowledge map of the field. Chemosphere 296, 133968 (2022).

    Google Scholar 

  64. Timilsina, G. R., Csordás, S. & Mevel, S. Biofuel policies: subsidy vs. carbon tax. In The Impacts of Biofuels on the Economy, Environment, and Poverty: A Global Perspective 123–129 (Springer, 2014). https://doi.org/10.1007/978-1-4614-9280-6_6.

  65. Rasekh, A. & Fatemi Ghomi, S. Water-energy nexus approach for assessment of biodiesel production from jatropha via system dynamics modeling. Biofuels 1–19, (2025), https://doi.org/10.1080/17597269.2025.2547550.

  66. Habibi, F., Chakrabortty, R. K. & Abbasi, A. Towards facing uncertainties in biofuel supply chain networks: A systematic literature review. Environ. Sci. Pollut. Res. 30, 100360–100390 (2023).

    Google Scholar 

  67. Nair, S. K., Emerson, R. M. & Solomon, J. R. Biomass supply chain risk: Towards a better understanding of feedstock availability, cost, variability, and uncertainty to catalyze and de-risk biobased investment. In Handbook of Biorefinery Research and Technology: Biomass Logistics to Saccharification, 285–312 (Springer, 2024).

  68. Guo, C. et al. Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply. Renew. Energy 186, 378–393 (2022).

    Google Scholar 

  69. Vladimirou, H. & Zenios, S. A. Stochastic programming and robust optimization. In Advances in Sensitivity Analysis and Parametic Programming, 395–447 (Springer, 1997).

  70. Taghikhani, S., Baroughi, F. & Alizadeh, B. A hybrid modified pso algorithm for the inverse p-median location problem in fuzzy random environment. Theor. Comput. Sci. 1000, 114574 (2024).

    Google Scholar 

  71. Taheri, N., Jahani, H. & Pishvaee, M. S. Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach. Renew. Energy 227, 120481 (2024).

    Google Scholar 

  72. Pasupuleti, V., Thuraka, B., Kodete, C. S. & Malisetty, S. Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics 8, 73 (2024).

    Google Scholar 

  73. Cui, L., Chen, Y., Deng, J. & Han, Z. A novel attlstm framework combining the attention mechanism and bidirectional lstm for demand forecasting. Expert Syst. Appl. 254, 124409 (2024).

    Google Scholar 

  74. Stambe, R.-M., Marston, G., Snell, D. & Moss, J. Jobs aren’t enough: Redefining just transitions in Australia with community voices. Energy Res. Soc. Sci. 122, 103999 (2025).

    Google Scholar 

  75. Zemín, L. et al. An empirical study on the suitability of test-based patch acceptance criteria. ACM Trans. Softw. Eng. Methodol. 34, 1–20 (2025).

    Google Scholar 

  76. Huang, J.-J. & Chen, C.-Y. Resource allocation of cooperative alternatives using the Analytic Hierarchy Process and Analytic Network Process with Shapley values. Algorithms 17, 152 (2024).

    Google Scholar 

  77. Arrien, M. M., Aldaya, M. M. & Rodríguez, C. I. Livestock and water resources: A comparative study of water footprint in different farming systems. Sustainability 17, 2251 (2025).

    Google Scholar 

  78. Cahyadi, E. R., Hidayati, N., Zahra, N. & Arif, C. Integrating circular economy principles into agri-food supply chain management: A systematic literature review. Sustainability 16, 7165 (2024).

    Google Scholar 

  79. Silalertruksa, T., Gheewala, S. H., Hünecke, K. & Fritsche, U. R. Biofuels and employment effects: Implications for socio-economic development in Thailand. Biomass Bioenergy 46, 409–418 (2012).

    Google Scholar 

  80. Bonaiuto, M. et al. Beliefs about technological and contextual features drive biofuels’ social acceptance. Renew. Sustain. Energy Rev. 189, 113867 (2024).

    Google Scholar 

  81. Chaiyapa, W. et al. Public perception of biofuel usage in vietnam. Biofuels 12, 21–33. https://doi.org/10.1080/17597269.2018.1519357 (2021).

    Google Scholar 

  82. Giakoumis, E. G., Rakopoulos, D. C. & Rakopoulos, C. D. Combustion noise radiation during dynamic diesel engine operation including effects of various biofuel blends: A review. Renew. Sustain. Energy Rev. 54, 1099–1113. https://doi.org/10.1016/j.rser.2015.10.089 (2016).

    Google Scholar 

  83. Serrano-Torres, G. J., López-Naranjo, A. L., Larrea-Cuadrado, P. L. & Mazón-Fierro, G. Transformation of the dairy supply chain through artificial intelligence: A systematic review. Sustainability 17, 982 (2025).

    Google Scholar 

  84. Patil, A. & Madaan, J. A study on the research clusters in the humanitarian supply chain literature: A systematic review. Logistics 8, 128 (2024).

    Google Scholar 

  85. Basile, F., Pilotti, L., Ugolini, M., Lozza, G. & Manzolini, G. Supply chain optimization and ghg emissions in biofuel production from forestry residues in Sweden. Renew. Energy 196, 405–421 (2022).

    Google Scholar 

  86. Mohtashami, Z., Bozorgi-Amiri, A. & Tavakkoli-Moghaddam, R. A two-stage multi-objective second generation biodiesel supply chain design considering social sustainability: A case study. Energy 233, 121020 (2021).

    Google Scholar 

  87. Ibarra-Gonzalez, P., Rong, B.-G., Segovia-Hernández, J. G. & Sánchez-Ramírez, E. Multi-objective optimization methodology for process synthesis and intensification: Gasification-based biomass conversion into transportation fuels. Chem. Eng. Process. Process Intensif. 162, 108327 (2021).

    Google Scholar 

  88. Cram, A. et al. Multi-objective biofuel feedstock optimization considering different land-cover scenarios and watershed impacts. Clean Technol. Recycl. 2, 103–118 (2022).

    Google Scholar 

  89. Peña-Torres, D., Boix, M. & Montastruc, L. Multi-objective optimization and demand variation analysis on a water energy food nexus system. Comput. Chem. Eng. 180, 108473 (2024).

    Google Scholar 

  90. O’Neill, E. G. & Maravelias, C. T. Towards integrated landscape design and biofuel supply chain optimization. Curr. Opin. Chem. Eng. 31, 100666 (2021).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bauman Moscow State Technical University, Moscow, 105005, Russia

    Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov & Vadim Tynchenko

Authors
  1. Ivan P. Malashin
    View author publications

    Search author on:PubMed Google Scholar

  2. Dmitry Martysyuk
    View author publications

    Search author on:PubMed Google Scholar

  3. Vladimir Nelyub
    View author publications

    Search author on:PubMed Google Scholar

  4. Aleksei Borodulin
    View author publications

    Search author on:PubMed Google Scholar

  5. Andrei Gantimurov
    View author publications

    Search author on:PubMed Google Scholar

  6. Vadim Tynchenko
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, I.P.M., V.T., and D.M.; Data curation, I.P.M. and D.M.; Project administration, V.T. and A.B.; Resources, V.N.; Software, I.P.M., A.B., and A.G.; Supervision, V.T., A.B., R.V., I.P.M., and V.N.; Validation, I.P.M., V.T., A.B., and A.G.; Visualization, I.P.M. and D.M.; Writing-original draft preparation, I.P.M., D.M., V.T., V.N., A.B., and A.G. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Ivan P. Malashin.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Summary table

A Summary table

Multi-criteria optimization models provide structured approaches for assessing trade-offs in biofuel production. Table 4 summarizes key studies, detailing their optimization techniques, outcomes, and limitations.

Table 4 Summary of studies focused on sustainable supply chains and emission minimization.
Full size table

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Malashin, I.P., Martysyuk, D., Nelyub, V. et al. Multi-objective optimization of a regional biogas supply chain using organic waste. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42963-5

Download citation

  • Received: 20 May 2025

  • Accepted: 28 February 2026

  • Published: 08 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42963-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Multi-objective optimization
  • Bioenergy supply chain
  • Organic waste utilization
  • Greenhouse gas mitigation
  • Sensitivity analysis
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene