IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v310y2023i3p1312-1324.html
   My bibliography  Save this article

A framework for inherently interpretable optimization models

Author

Listed:
  • Goerigk, Marc
  • Hartisch, Michael

Abstract

With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving optimization problems often turns out to be one of the smaller difficulties when putting solutions into practice. One major barrier is that the optimization software can be perceived as a black box, which may produce solutions of high quality, but can create completely different solutions when circumstances change leading to low acceptance of optimized solutions. Such issues of interpretability and explainability have seen significant attention in other areas, such as machine learning, but less so in optimization. In this paper we propose an optimization framework that inherently comes with an easily interpretable optimization rule, that explains under which circumstances certain solutions are chosen. Focusing on univariate decision trees to represent interpretable optimization rules, we propose integer programming formulations as well as a heuristic method that ensure applicability of our approach even for large-scale problems. By presenting several extensions to the univariate decision tree approach, we showcase the generality of the proposed framework. Computational experiments using random and real-world data of a road network indicate that the costs of inherent interpretability can be very small.

Suggested Citation

  • Goerigk, Marc & Hartisch, Michael, 2023. "A framework for inherently interpretable optimization models," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1312-1324.
  • Handle: RePEc:eee:ejores:v:310:y:2023:i:3:p:1312-1324
    DOI: 10.1016/j.ejor.2023.04.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221723002953
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2023.04.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andrea Prat, 2005. "The Wrong Kind of Transparency," American Economic Review, American Economic Association, vol. 95(3), pages 862-877, June.
    2. Parker, Andrew M. & Srinivasan, Sinduja V. & Lempert, Robert J. & Berry, Sandra H., 2015. "Evaluating simulation-derived scenarios for effective decision support," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 64-77.
    3. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    4. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    5. Buchheim, Christoph & Pruente, Jonas, 2019. "K-adaptability in stochastic combinatorial optimization under objective uncertainty," European Journal of Operational Research, Elsevier, vol. 277(3), pages 953-963.
    6. Tsai, Jung-Fa & Lin, Ming-Hua & Hu, Yi-Chung, 2008. "Finding multiple solutions to general integer linear programs," European Journal of Operational Research, Elsevier, vol. 184(2), pages 802-809, January.
    7. Dragos Florin Ciocan & Velibor V. Mišić, 2022. "Interpretable Optimal Stopping," Management Science, INFORMS, vol. 68(3), pages 1616-1638, March.
    8. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    9. Grani A. Hanasusanto & Daniel Kuhn & Wolfram Wiesemann, 2015. "K -Adaptability in Two-Stage Robust Binary Programming," Operations Research, INFORMS, vol. 63(4), pages 877-891, August.
    10. Zhang, Yuchang & Bai, Ruibin & Qu, Rong & Tu, Chaofan & Jin, Jiahuan, 2022. "A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties," European Journal of Operational Research, Elsevier, vol. 300(2), pages 418-427.
    11. Angelos Georghiou & Daniel Kuhn & Wolfram Wiesemann, 2019. "The decision rule approach to optimization under uncertainty: methodology and applications," Computational Management Science, Springer, vol. 16(4), pages 545-576, October.
    12. Weiquan Wang & Jingjun (David) Xu & May Wang, 2018. "Effects of Recommendation Neutrality and Sponsorship Disclosure on Trust vs. Distrust in Online Recommendation Agents: Moderating Role of Explanations for Organic Recommendations," Management Science, INFORMS, vol. 64(11), pages 5198-5219, November.
    13. Chassein, André & Dokka, Trivikram & Goerigk, Marc, 2019. "Algorithms and uncertainty sets for data-driven robust shortest path problems," European Journal of Operational Research, Elsevier, vol. 274(2), pages 671-686.
    14. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.
    15. Drake, John H. & Kheiri, Ahmed & Özcan, Ender & Burke, Edmund K., 2020. "Recent advances in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 405-428.
    16. Edmund K. Burke & Matthew R. Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2019. "A Classification of Hyper-Heuristic Approaches: Revisited," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 453-477, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    3. Corrente, Salvatore & Greco, Salvatore & Matarazzo, Benedetto & Słowiński, Roman, 2024. "Explainable interactive evolutionary multiobjective optimization," Omega, Elsevier, vol. 122(C).

    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. Cohen, Izack & Postek, Krzysztof & Shtern, Shimrit, 2023. "An adaptive robust optimization model for parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 306(1), pages 83-104.
    2. Zeren, Bahadır & Özcan, Ender & Deveci, Muhammet, 2024. "An adaptive greedy heuristic for large scale airline crew pairing problems," Journal of Air Transport Management, Elsevier, vol. 114(C).
    3. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    4. Dimitrios Letsios & Jeremy T. Bradley & Suraj G & Ruth Misener & Natasha Page, 2021. "Approximate and robust bounded job start scheduling for Royal Mail delivery offices," Journal of Scheduling, Springer, vol. 24(2), pages 237-258, April.
    5. Nicolas Kämmerling & Jannis Kurtz, 2020. "Oracle-based algorithms for binary two-stage robust optimization," Computational Optimization and Applications, Springer, vol. 77(2), pages 539-569, November.
    6. Rettinger, Moritz & Mandl, Christian & Minner, Stefan, 2024. "A data-driven approach for optimal operational and financial commodity hedging," European Journal of Operational Research, Elsevier, vol. 316(1), pages 341-360.
    7. Notz, Pascal M. & Pibernik, Richard, 2024. "Explainable subgradient tree boosting for prescriptive analytics in operations management," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1119-1133.
    8. Jacques Crémer, 2010. "Arm's-Length Relationships without Moral Hazard," Journal of the European Economic Association, MIT Press, vol. 8(2-3), pages 377-387, 04-05.
    9. V. Bhaskar & Caroline Thomas, 2019. "The Culture of Overconfidence," American Economic Review: Insights, American Economic Association, vol. 1(1), pages 95-110, June.
    10. Aleksei Smirnov & Egor Starkov, 2019. "Timing of predictions in dynamic cheap talk: experts vs. quacks," ECON - Working Papers 334, Department of Economics - University of Zurich.
    11. Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
    12. Serrano, Breno & Minner, Stefan & Schiffer, Maximilian & Vidal, Thibaut, 2024. "Bilevel optimization for feature selection in the data-driven newsvendor problem," European Journal of Operational Research, Elsevier, vol. 315(2), pages 703-714.
    13. Gahm, Christian & Uzunoglu, Aykut & Wahl, Stefan & Ganschinietz, Chantal & Tuma, Axel, 2022. "Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning," European Journal of Operational Research, Elsevier, vol. 296(3), pages 819-836.
    14. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    15. Derhami, Shahab & Smith, Alice E., 2017. "An integer programming approach for fuzzy rule-based classification systems," European Journal of Operational Research, Elsevier, vol. 256(3), pages 924-934.
    16. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    17. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    18. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    19. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    20. Derya Deliktaş, 2022. "Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 748-784, September.

    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:eee:ejores:v:310:y:2023:i:3:p:1312-1324. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.