IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.23616.html

Value-focused modelling to generate alternatives -- Coupling multi-criteria decision analysis and optimisation models to support strategic decisions

Author

Listed:
  • Emily Bergup
  • Jonas Finke
  • Sebastian Schar
  • Valentin Bertsch

Abstract

Decision support methods from operations research are widely used to support complex planning decisions. Within the energy sector, energy system models (ESMs) applying modelling to generate alternatives (MGA) generate large sets of near-optimal, different system configurations. However, they typically generate and analyse alternatives in the model variable space without ensuring stakeholder relevance. Multi-criteria decision analysis (MCDA), in contrast, provides a structured means to account for conflicting objectives and heterogeneous stakeholder interests but often relies on a limited set of pre-defined alternatives that may not appropriately represent the feasible solution space. To address these limitations, this work proposes value-focused modelling to generate alternatives (VF-MGA), a novel methodology that bidirectionally couples MGA and MCDA. Stakeholder objectives elicited within the MCDA inform the MGA-algorithm, enabling a stakeholder-orientated diversification of the alternatives, which are subsequently evaluated within the MCDA based on elicited stakeholder preferences, thereby providing a comprehensive decision basis. Applied to a case study on the decarbonised energy supply of a large university campus, involving eleven stakeholders representing diverse institutional groups, VF-MGA (i) systematically integrates stakeholder objectives into the generation of 691 alternatives reflecting stakeholder-relevant interests, (ii) enables the identification of stakeholder-relevant alternatives from this large set through MCDA-based evaluation, and (iii) provides more differentiated stakeholder preference information by evaluating a large and diverse set of alternatives, thereby revealing acceptable ranges for system options. With this, VF-MGA provides a generalisable methodology for complex planning decision integrating quantitative modelling with participatory decision analysis.

Suggested Citation

  • Emily Bergup & Jonas Finke & Sebastian Schar & Valentin Bertsch, 2026. "Value-focused modelling to generate alternatives -- Coupling multi-criteria decision analysis and optimisation models to support strategic decisions," Papers 2605.23616, arXiv.org.
  • Handle: RePEc:arx:papers:2605.23616
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2605.23616
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Franz Eisenführ & Martin Weber & Thomas Langer, 2010. "Rational Decision Making," Springer Books, Springer, number 978-3-642-02851-9, January.
    2. Niina Helistö & Juha Kiviluoma & Jussi Ikäheimo & Topi Rasku & Erkka Rinne & Ciara O’Dwyer & Ran Li & Damian Flynn, 2019. "Backbone—An Adaptable Energy Systems Modelling Framework," Energies, MDPI, vol. 12(17), pages 1-34, September.
    3. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    4. Esser, Katharina & Finke, Jonas & Bertsch, Valentin & Löschel, Andreas, 2025. "Participatory modelling to generate alternatives to support decision-makers with near-optimal decarbonisation options," Applied Energy, Elsevier, vol. 395(C).
    5. Nayyar Hussain Mirjat & Mohammad Aslam Uqaili & Khanji Harijan & Mohd Wazir Mustafa & Md. Mizanur Rahman & M. Waris Ali Khan, 2018. "Multi-Criteria Analysis of Electricity Generation Scenarios for Sustainable Energy Planning in Pakistan," Energies, MDPI, vol. 11(4), pages 1-33, March.
    6. Danae Diakoulaki & Carlos Henggeler Antunes & António Gomes Martins, 2005. "MCDA and Energy Planning," International Series in Operations Research & Management Science, in: Multiple Criteria Decision Analysis: State of the Art Surveys, chapter 0, pages 859-890, Springer.
    7. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa & Zhao, Jun-Hong, 2009. "Review on multi-criteria decision analysis aid in sustainable energy decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2263-2278, December.
    8. Lerche, Nils & Wilkens, Ines & Schmehl, Meike & Eigner-Thiel, Swantje & Geldermann, Jutta, 2019. "Using methods of Multi-Criteria Decision Making to provide decision support concerning local bioenergy projects," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    9. Lerede, Daniele & Pinto, Giuseppe & Saccone, Mirko & Bustreo, Chiara & Capozzoli, Alfonso & Savoldi, Laura, 2021. "Application of a Stochastic Multicriteria Acceptability Analysis to support decision-making within a macro-scale energy model: Case study of the electrification of the road European transport sector," Energy, Elsevier, vol. 236(C).
    10. McKenna, R. & Bertsch, V. & Mainzer, K. & Fichtner, W., 2018. "Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1092-1110.
    11. Zexin Lei & Lijun Li & Yanrou Wei & Wenzheng Zhang & Junjie Luo & Xuqiang Zhao, 2025. "Optimization and Benefit Assessment of LID Layout Based on the MCDA Approach at a Campus Scale," Land, MDPI, vol. 14(7), pages 1-25, July.
    12. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    13. Prina, Matteo Giacomo & Manzolini, Giampaolo & Moser, David & Nastasi, Benedetto & Sparber, Wolfram, 2020. "Classification and challenges of bottom-up energy system models - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    14. Francesco Lombardi & Stefan Pfenninger, 2025. "Human-in-the-loop MGA to generate energy system design options matching stakeholder needs," PLOS Climate, Public Library of Science, vol. 4(2), pages 1-19, February.
    15. L. Alberto Franco & Etiënne A. J. A. Rouwette, 2022. "Problem Structuring Methods: Taking Stock and Looking Ahead," Springer Books, in: Saïd Salhi & John Boylan (ed.), The Palgrave Handbook of Operations Research, chapter 0, pages 735-780, Springer.
    16. Berntsen, Philip B. & Trutnevyte, Evelina, 2017. "Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives," Energy, Elsevier, vol. 126(C), pages 886-898.
    17. McGookin, Connor & Ó Gallachóir, Brian & Byrne, Edmond, 2021. "Participatory methods in energy system modelling and planning – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    18. Grochowicz, Aleksander & van Greevenbroek, Koen & Benth, Fred Espen & Zeyringer, Marianne, 2023. "Intersecting near-optimal spaces: European power systems with more resilience to weather variability," Energy Economics, Elsevier, vol. 118(C).
    19. Haag, Fridolin & Zürcher, Sara & Lienert, Judit, 2019. "Enhancing the elicitation of diverse decision objectives for public planning," European Journal of Operational Research, Elsevier, vol. 279(3), pages 912-928.
    20. E. Downey Brill, Jr. & Shoou-Yuh Chang & Lewis D. Hopkins, 1982. "Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning," Management Science, INFORMS, vol. 28(3), pages 221-235, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Esser, Katharina & Finke, Jonas & Bertsch, Valentin & Löschel, Andreas, 2025. "Participatory modelling to generate alternatives to support decision-makers with near-optimal decarbonisation options," Applied Energy, Elsevier, vol. 395(C).
    2. Finke, Jonas & Kachirayil, Febin & McKenna, Russell & Bertsch, Valentin, 2024. "Modelling to generate near-Pareto-optimal alternatives (MGPA) for the municipal energy transition," Applied Energy, Elsevier, vol. 376(PA).
    3. de Tomás-Pascual, Alexander & Pérez-Sánchez, Laura À. & Sierra-Montoya, Miquel & Lombardi, Francesco & Pfenninger-Lee, Stefan & Campos, Inês & Madrid-López, Cristina, 2025. "The optimal is not always the best: Life cycle impacts of near-optimal energy systems," Applied Energy, Elsevier, vol. 399(C).
    4. Schwaeppe, Henrik & Thams, Marten Simon & Walter, Julian & Moser, Albert, 2024. "Finding better alternatives: Shadow prices of near-optimal solutions in energy system optimization modeling," Energy, Elsevier, vol. 292(C).
    5. Dubois, Antoine & Dumas, Jonathan & Thiran, Paolo & Limpens, Gauthier & Ernst, Damien, 2023. "Multi-objective near-optimal necessary conditions for multi-sectoral planning," Applied Energy, Elsevier, vol. 350(C).
    6. Jan-Philipp Sasse & Evelina Trutnevyte, 2023. "A low-carbon electricity sector in Europe risks sustaining regional inequalities in benefits and vulnerabilities," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    7. Torralba-Díaz, Laura & Schimeczek, Christoph & Kochems, Johannes & Hufendiek, Kai, 2024. "Iterative coupling of a fundamental electricity market model and an agent-based simulation model to reduce the efficiency gap," Energy, Elsevier, vol. 310(C).
    8. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    9. Fodstad, Marte & Crespo del Granado, Pedro & Hellemo, Lars & Knudsen, Brage Rugstad & Pisciella, Paolo & Silvast, Antti & Bordin, Chiara & Schmidt, Sarah & Straus, Julian, 2022. "Next frontiers in energy system modelling: A review on challenges and the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    10. Lombardi, Francesco & Pickering, Bryn & Pfenninger, Stefan, 2023. "What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment," Applied Energy, Elsevier, vol. 339(C).
    11. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    12. David Huckebrink & Valentin Bertsch, 2021. "Integrating Behavioural Aspects in Energy System Modelling—A Review," Energies, MDPI, vol. 14(15), pages 1-26, July.
    13. Wen, Xin & Contreras, Julia Gonzalez & Stadelmann-Steffen, Isabelle & Sasse, Jan-Philipp & Trutnevyte, Evelina, 2025. "High sensitivity to methodological choices when integrating social acceptance data in electricity system modeling," Applied Energy, Elsevier, vol. 402(PA).
    14. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Kerschbaum, Alina & Trentmann, Lennart & Hanel, Andreas & Fendt, Sebastian & Spliethoff, Hartmut, 2025. "Methods for analysing renewable energy potentials in energy system modelling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(C).
    16. Buenau, K.E. & Sather, N.K. & Arkema, K.K., 2025. "A marine energy and ecosystem service framework for coastal communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    17. Muhammad Riaz & Wojciech Sałabun & Hafiz Muhammad Athar Farid & Nawazish Ali & Jarosław Wątróbski, 2020. "A Robust q-Rung Orthopair Fuzzy Information Aggregation Using Einstein Operations with Application to Sustainable Energy Planning Decision Management," Energies, MDPI, vol. 13(9), pages 1-39, May.
    18. Nikas, A. & Gambhir, A. & Trutnevyte, E. & Koasidis, K. & Lund, H. & Thellufsen, J.Z. & Mayer, D. & Zachmann, G. & Miguel, L.J. & Ferreras-Alonso, N. & Sognnaes, I. & Peters, G.P. & Colombo, E. & Howe, 2021. "Perspective of comprehensive and comprehensible multi-model energy and climate science in Europe," Energy, Elsevier, vol. 215(PA).
    19. Manley, Dawn K. & Hines, Valerie A. & Jordan, Matthew W. & Stoltz, Ronald E., 2013. "A survey of energy policy priorities in the United States: Energy supply security, economics, and the environment," Energy Policy, Elsevier, vol. 60(C), pages 687-696.
    20. Govorukha, Kristina & Mayer, Philip & Rübbelke, Dirk & Vögele, Stefan, 2020. "Economic disruptions in long-term energy scenarios – Implications for designing energy policy," Energy, Elsevier, vol. 212(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2605.23616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.