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Moha GHADERI

Personal Details

First Name:Moha
Middle Name:
Last Name:Ghaderi
Suffix:
RePEc Short-ID:pgh259
[This author has chosen not to make the email address public]
https://www.upf.edu/en/web/econ/statistics-econometrics-and-quantitative-methods/-/asset_publisher/Z

Affiliation

Departament d'Economia i Empresa
Universitat Pompeu Fabra
Barcelona School of Economics (BSE)

Barcelona, Spain
http://www.econ.upf.edu/
RePEc:edi:deupfes (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Kadziński, Miłosz & Wójcik, Michał & Ghaderi, Mohammad, 2025. "From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems," Omega, Elsevier, vol. 131(C).
  2. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.
  3. Bagherzadeh, Mehdi & Ghaderi, Mohammad & Fernandez, Anne-Sophie, 2022. "Coopetition for innovation - the more, the better? An empirical study based on preference disaggregation analysis," European Journal of Operational Research, Elsevier, vol. 297(2), pages 695-708.
  4. Ghaderi, Mohammad & Kadziński, Miłosz, 2021. "Incorporating uncovered structural patterns in value functions construction," Omega, Elsevier, vol. 99(C).
  5. Kadziński, Miłosz & Ghaderi, Mohammad & Dąbrowski, Maciej, 2020. "Contingent preference disaggregation model for multiple criteria sorting problem," European Journal of Operational Research, Elsevier, vol. 281(2), pages 369-387.
  6. Ghaderi, Mohammad & Ruiz, Francisco & Agell, Núria, 2017. "A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1073-1084.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Kadziński, Miłosz & Wójcik, Michał & Ghaderi, Mohammad, 2025. "From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems," Omega, Elsevier, vol. 131(C).

    Cited by:

    1. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting," European Journal of Operational Research, Elsevier, vol. 323(2), pages 553-570.
    2. Grillo, Marco & Kotłowski, Wojciech & Kadziński, Miłosz, 2025. "Ordinal regression meets online learning: Interactive preference learning for multiple criteria choice and ranking with provable guarantees," European Journal of Operational Research, Elsevier, vol. 327(3), pages 1003-1022.
    3. Li, Yijun & Guo, Mengzhuo & Kadziński, Miłosz & Zhang, Qingpeng & Xu, Chenxi, 2025. "Data-driven preference learning methods for sorting problems with multiple temporal criteria," European Journal of Operational Research, Elsevier, vol. 323(3), pages 918-937.

  2. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.

    Cited by:

    1. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    2. Huberts, Nick F.D. & Thijssen, Jacco J.J., 2023. "Optimal timing of non-pharmaceutical interventions during an epidemic," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1366-1389.

  3. Bagherzadeh, Mehdi & Ghaderi, Mohammad & Fernandez, Anne-Sophie, 2022. "Coopetition for innovation - the more, the better? An empirical study based on preference disaggregation analysis," European Journal of Operational Research, Elsevier, vol. 297(2), pages 695-708.

    Cited by:

    1. Liu, Lili & Ang, Sheng & Yang, Feng & Zhang, Xiaoqi, 2025. "Partner selection for supply chain collaboration: New data envelopment analysis models," Omega, Elsevier, vol. 132(C).
    2. Rouyre, Audrey & Fernandez, Anne-Sophie & Bruyaka, Olga, 2024. "Big problems require large collective actions: Managing multilateral coopetition in strategic innovation networks," Technovation, Elsevier, vol. 132(C).
    3. Wioletta Mierzejewska & Maria Aluchna & Emilia Tomczyk, 2023. "Do coopetition and cohesion of business groups stimulate their innovation performance?," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(2), pages 153-168, June.
    4. Alshareef, Leena & Dietlmeier, Simon Frederic & Florian, Urmetzer, 2024. "Coopetition For The Greater Good. Exploratory study of coopetition management mechanisms in the pharmaceutical industry," MPRA Paper 120645, University Library of Munich, Germany.
    5. Kaifu Yuan & Chuanji Wang & Guangqiang Wu, 2025. "RETRACTED ARTICLE: Range Coopetition: NEV Automakers' Strategies Under Dual Credit Policy Influences," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 5056-5092, March.
    6. Anita Mustikasari & Wakhid Slamet Ciptono, 2025. "The Microfoundations of Managing Adaptive Tension as Dynamic Capability in Innovation Management," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 26(3), pages 649-679, September.
    7. Xie, Qiuhao & Gao, Ying & Xia, Nini & Zhang, Shuibo & Tao, Guowu, 2023. "Coopetition and organizational performance outcomes: A meta-analysis of the main and moderator effects," Journal of Business Research, Elsevier, vol. 154(C).
    8. Wu, Xingli & Liao, Huchang, 2023. "Value-driven preference disaggregation analysis for uncertain preference information," Omega, Elsevier, vol. 115(C).
    9. Kamala Kannan Dinesh & Abhilasha Meena, 2025. "Coopetition and Strategic Innovation: A Structured Literature Review Using Argumentation-Based TISM," International Journal of Global Business and Competitiveness, Springer, vol. 20(2), pages 80-94, December.
    10. Niu, Baozhuang & Chen, Yuyang & Zeng, Fanzhuo, 2023. "One step further for procurement cooperation: Will the industry leader benefit from its competitive manufacturer's joint determination of consumption quality?," European Journal of Operational Research, Elsevier, vol. 311(3), pages 989-1008.
    11. Frédéric Le Roy & Anne-Sophie Fernandez & Lin Po-Chang, 2024. "Managing coopetitive innovation: sharing while protecting knowledge in separated project teams," Post-Print hal-04791151, HAL.

  4. Ghaderi, Mohammad & Kadziński, Miłosz, 2021. "Incorporating uncovered structural patterns in value functions construction," Omega, Elsevier, vol. 99(C).

    Cited by:

    1. Ru, Zice & Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu, 2022. "Bayesian ordinal regression for multiple criteria choice and ranking," European Journal of Operational Research, Elsevier, vol. 299(2), pages 600-620.
    2. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting," European Journal of Operational Research, Elsevier, vol. 323(2), pages 553-570.
    3. Wen, Zhi & Liao, Huchang & Figueira, José Rui, 2024. "A preference disaggregation-driven multiple criteria sorting model based on regret theory," Omega, Elsevier, vol. 129(C).
    4. Liu, Jiapeng & Wang, Yan & Kadziński, Miłosz & Mao, Xiaoxin & Rao, Yuan, 2024. "A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences," Omega, Elsevier, vol. 128(C).
    5. Bas Donkers & Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński, 2025. "Random Preference Model," Working Papers 1502, Barcelona School of Economics.
    6. Kadziński, Miłosz & Wójcik, Michał & Ghaderi, Mohammad, 2025. "From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems," Omega, Elsevier, vol. 131(C).
    7. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao, 2023. "Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 764-785, July.
    8. Ru, Zice & Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu, 2023. "Probabilistic ordinal regression methods for multiple criteria sorting admitting certain and uncertain preferences," European Journal of Operational Research, Elsevier, vol. 311(2), pages 596-616.
    9. Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński & Bas Donkers, 2025. "Random preference model," Economics Working Papers 1913, Department of Economics and Business, Universitat Pompeu Fabra.

  5. Kadziński, Miłosz & Ghaderi, Mohammad & Dąbrowski, Maciej, 2020. "Contingent preference disaggregation model for multiple criteria sorting problem," European Journal of Operational Research, Elsevier, vol. 281(2), pages 369-387.

    Cited by:

    1. Ru, Zice & Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu, 2022. "Bayesian ordinal regression for multiple criteria choice and ranking," European Journal of Operational Research, Elsevier, vol. 299(2), pages 600-620.
    2. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods. Part II: theoretical results and general issues," 4OR, Springer, vol. 21(2), pages 181-204, June.
    3. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting," European Journal of Operational Research, Elsevier, vol. 323(2), pages 553-570.
    4. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples," Omega, Elsevier, vol. 131(C).
    5. Bagherzadeh, Mehdi & Ghaderi, Mohammad & Fernandez, Anne-Sophie, 2022. "Coopetition for innovation - the more, the better? An empirical study based on preference disaggregation analysis," European Journal of Operational Research, Elsevier, vol. 297(2), pages 695-708.
    6. Marco Cinelli & Matteo Spada & Wansub Kim & Yiwen Zhang & Peter Burgherr, 2021. "MCDA Index Tool: an interactive software to develop indices and rankings," Environment Systems and Decisions, Springer, vol. 41(1), pages 82-109, March.
    7. Wu, Xingli & Zhu, Ting, 2025. "From data to diagnosis: A logical learning method to enhance interpretability in bipolar and major depressive disorder identification," European Journal of Operational Research, Elsevier, vol. 325(2), pages 362-380.
    8. Wen, Zhi & Liao, Huchang & Figueira, José Rui, 2024. "A preference disaggregation-driven multiple criteria sorting model based on regret theory," Omega, Elsevier, vol. 129(C).
    9. Leyva López, Juan Carlos & Solano Noriega, Jesús Jaime & Figueira, José Rui & Liu, Jun & Gastélum Chavira, Diego Alonso, 2021. "Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation," European Journal of Operational Research, Elsevier, vol. 293(2), pages 615-631.
    10. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2024. "A guided tour of multiple criteria sorting models and methods," Annals of Operations Research, Springer, vol. 343(2), pages 785-845, December.
    11. Podinovski, Vladislav V., 2020. "Maximum likelihood solutions for multicriterial choice problems," European Journal of Operational Research, Elsevier, vol. 286(1), pages 299-308.
    12. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2021. "MURAME parameter setting for creditworthiness evaluation: data-driven optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 295-339, June.
    13. Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu & Mao, Xiaoxin & Wang, Yao, 2020. "A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples," European Journal of Operational Research, Elsevier, vol. 286(3), pages 963-985.
    14. Kadziński, Miłosz & Ciomek, Krzysztof, 2021. "Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 293(2), pages 658-680.
    15. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.
    16. Ghaderi, Mohammad & Kadziński, Miłosz, 2021. "Incorporating uncovered structural patterns in value functions construction," Omega, Elsevier, vol. 99(C).
    17. Tlili, Ali & Belahcène, Khaled & Khaled, Oumaima & Mousseau, Vincent & Ouerdane, Wassila, 2022. "Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations," European Journal of Operational Research, Elsevier, vol. 298(3), pages 979-1006.
    18. Bas Donkers & Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński, 2025. "Random Preference Model," Working Papers 1502, Barcelona School of Economics.
    19. Li, Yijun & Guo, Mengzhuo & Kadziński, Miłosz & Zhang, Qingpeng & Xu, Chenxi, 2025. "Data-driven preference learning methods for sorting problems with multiple temporal criteria," European Journal of Operational Research, Elsevier, vol. 323(3), pages 918-937.
    20. Kadziński, Miłosz & Wójcik, Michał & Ghaderi, Mohammad, 2025. "From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems," Omega, Elsevier, vol. 131(C).
    21. Amoussou, Manuel & Belahcene, Khaled & Maudet, Nicolas & Mousseau, Vincent & Ouerdane, Wassila, 2026. "Designing and computing explanations for comparisons inferred from an additive value model," European Journal of Operational Research, Elsevier, vol. 328(1), pages 232-245.
    22. Yu Yang & Yelin Fu & Siying Chen, 2026. "Group Preference Construction with Attitudinal Consensus Building Based on Uncertain Preference Disaggregation Analysis," Group Decision and Negotiation, Springer, vol. 35(2), pages 1-31, June.
    23. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao, 2023. "Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 764-785, July.
    24. Ru, Zice & Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu, 2023. "Probabilistic ordinal regression methods for multiple criteria sorting admitting certain and uncertain preferences," European Journal of Operational Research, Elsevier, vol. 311(2), pages 596-616.
    25. Cinelli, Marco & Kadziński, Miłosz & Miebs, Grzegorz & Gonzalez, Michael & Słowiński, Roman, 2022. "Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system," European Journal of Operational Research, Elsevier, vol. 302(2), pages 633-651.
    26. Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński & Bas Donkers, 2025. "Random preference model," Economics Working Papers 1913, Department of Economics and Business, Universitat Pompeu Fabra.
    27. Mei Cai & Xue Zhang, 2025. "Consumer preference analysis integrating online reviews: a multiple criteria group approach considering individual stochastic behavior," 4OR, Springer, vol. 23(1), pages 97-122, March.

  6. Ghaderi, Mohammad & Ruiz, Francisco & Agell, Núria, 2017. "A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1073-1084.

    Cited by:

    1. Gilbert, Hugo & Ouaguenouni, Mohamed & Öztürk, Meltem & Spanjaard, Olivier, 2025. "Robust ordinal regression for subsets comparisons with interactions," European Journal of Operational Research, Elsevier, vol. 320(1), pages 146-159.
    2. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting," European Journal of Operational Research, Elsevier, vol. 323(2), pages 553-570.
    3. Arribas, I. & Urbano, A., 2018. "Identification of efficient equilibria in multiproduct trading with indivisibilities and non-monotonicity," Journal of Mathematical Economics, Elsevier, vol. 79(C), pages 83-94.
    4. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples," Omega, Elsevier, vol. 131(C).
    5. Bagherzadeh, Mehdi & Ghaderi, Mohammad & Fernandez, Anne-Sophie, 2022. "Coopetition for innovation - the more, the better? An empirical study based on preference disaggregation analysis," European Journal of Operational Research, Elsevier, vol. 297(2), pages 695-708.
    6. Kadziński, Miłosz & Ghaderi, Mohammad & Dąbrowski, Maciej, 2020. "Contingent preference disaggregation model for multiple criteria sorting problem," European Journal of Operational Research, Elsevier, vol. 281(2), pages 369-387.
    7. Liu, Jiapeng & Liao, Xiuwu & Kadziński, Miłosz & Słowiński, Roman, 2019. "Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1071-1089.
    8. Podinovski, Vladislav V., 2020. "Maximum likelihood solutions for multicriterial choice problems," European Journal of Operational Research, Elsevier, vol. 286(1), pages 299-308.
    9. Guo, Mengzhuo & Zhang, Qingpeng & Liao, Xiuwu & Chen, Frank Youhua & Zeng, Daniel Dajun, 2021. "A hybrid machine learning framework for analyzing human decision-making through learning preferences," Omega, Elsevier, vol. 101(C).
    10. Xingli Wu & Huchang Liao, 2021. "Learning judgment benchmarks of customers from online reviews," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 1125-1157, December.
    11. Guo, Mengzhuo & Liao, Xiuwu & Liu, Jiapeng & Zhang, Qingpeng, 2020. "Consumer preference analysis: A data-driven multiple criteria approach integrating online information," Omega, Elsevier, vol. 96(C).
    12. Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu & Mao, Xiaoxin & Wang, Yao, 2020. "A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples," European Journal of Operational Research, Elsevier, vol. 286(3), pages 963-985.
    13. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.
    14. Ghaderi, Mohammad & Kadziński, Miłosz, 2021. "Incorporating uncovered structural patterns in value functions construction," Omega, Elsevier, vol. 99(C).
    15. Dias, Luis C. & Dias, Joana & Ventura, Tiago & Rocha, Humberto & Ferreira, Brígida & Khouri, Leila & Lopes, Maria do Carmo, 2022. "Learning target-based preferences through additive models: An application in radiotherapy treatment planning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 270-279.
    16. Kadziński, Miłosz & Wójcik, Michał & Ciomek, Krzysztof, 2022. "Review and experimental comparison of ranking and choice procedures for constructing a univocal recommendation in a preference disaggregation setting," Omega, Elsevier, vol. 113(C).
    17. Arwa Khannoussi & Antoine Rolland & Julien Velcin, 2025. "A multidimensional spatial model for preference representation in multi-criteria group decision aiding," 4OR, Springer, vol. 23(2), pages 163-191, June.
    18. Pegdwendé Minoungou & Vincent Mousseau & Wassila Ouerdane & Paolo Scotton, 2023. "A MIP-based approach to learn MR-Sort models with single-peaked preferences," Annals of Operations Research, Springer, vol. 325(2), pages 795-817, June.
    19. Bas Donkers & Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński, 2025. "Random Preference Model," Working Papers 1502, Barcelona School of Economics.
    20. Li, Yijun & Guo, Mengzhuo & Kadziński, Miłosz & Zhang, Qingpeng & Xu, Chenxi, 2025. "Data-driven preference learning methods for sorting problems with multiple temporal criteria," European Journal of Operational Research, Elsevier, vol. 323(3), pages 918-937.
    21. Luis C. Dias & Gabriela D. Oliveira & Paula Sarabando, 2021. "Choice-based preference disaggregation concerning vehicle technologies," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 177-200, March.
    22. Kadziński, Miłosz & Wójcik, Michał & Ghaderi, Mohammad, 2025. "From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems," Omega, Elsevier, vol. 131(C).
    23. Gehrlein, Jonas & Miebs, Grzegorz & Brunelli, Matteo & Kadziński, Miłosz, 2023. "An active preference learning approach to aid the selection of validators in blockchain environments," Omega, Elsevier, vol. 118(C).
    24. Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński & Bas Donkers, 2025. "Random preference model," Economics Working Papers 1913, Department of Economics and Business, Universitat Pompeu Fabra.
    25. Zhou, Kun & Gong, Zaiwu & Wei, Guo & Słowiński, Roman, 2025. "Preference disaggregation analysis with criteria selection in a regularization framework," Omega, Elsevier, vol. 133(C).
    26. Susmaga, Robert & Szczȩch, Izabela & Zielniewicz, Piotr & Brzezinski, Dariusz, 2023. "MSD-space: Visualizing the inner-workings of TOPSIS aggregations," European Journal of Operational Research, Elsevier, vol. 308(1), pages 229-242.
    27. Yue Qi & Ralph E. Steuer, 2020. "On the analytical derivation of efficient sets in quad-and-higher criterion portfolio selection," Annals of Operations Research, Springer, vol. 293(2), pages 521-538, October.
    28. Subrata Mitra & Balram Avittathur, 2018. "Application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired power-generating company," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(3), pages 207-224, September.
    29. Mohammad Ghaderi & Milosz Kadzinsky, 2019. "Accounting for structural patterns in construction of value functions: a convex optimization approach," Economics Working Papers 1634, Department of Economics and Business, Universitat Pompeu Fabra.
    30. Wachowicz, Tomasz & Roszkowska, Ewa, 2022. "Can holistic declaration of preferences improve a negotiation offer scoring system?," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1018-1032.
    31. Sarah Ben Amor & Fateh Belaid & Ramzi Benkraiem & Boumediene Ramdani & Khaled Guesmi, 2023. "Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda," Annals of Operations Research, Springer, vol. 325(2), pages 771-793, June.

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