{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T03:39:12Z","timestamp":1777520352523,"version":"3.51.4"},"reference-count":190,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Operational Research"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ejor.2025.08.029","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T16:26:09Z","timestamp":1757607969000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence for optimization: Unleashing the potential of parameter generation, model formulation, and solution methods"],"prefix":"10.1016","volume":"332","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5116-2956","authenticated-orcid":false,"given":"Zhenan","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4695-200X","authenticated-orcid":false,"given":"Bissan","family":"Ghaddar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2877-561X","authenticated-orcid":false,"given":"Xinglu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5565-3757","authenticated-orcid":false,"given":"Linzi","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zirui","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.orl.2004.04.002","article-title":"Branching rules revisited","volume":"33","author":"Achterberg","year":"2005","journal-title":"Operations Research Letters"},{"key":"10.1016\/j.ejor.2025.08.029_b2","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1613\/jair.1.13922","article-title":"Automated dynamic algorithm configuration","volume":"75","author":"Adriaensen","year":"2022","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1287\/ijoc.2016.0723","article-title":"A machine learning-based approximation of strong branching","volume":"29","author":"Alvarez","year":"2017","journal-title":"INFORMS Journal on Computing"},{"key":"10.1016\/j.ejor.2025.08.029_b4","unstructured":"Amos, Brandon, & Kolter, J. Zico (2017). OptNet: Differentiable optimization as a layer in neural networks. In Proceedings of the 34th international conference on machine learning (pp. 136\u2013145)."},{"key":"10.1016\/j.ejor.2025.08.029_b5","series-title":"Genetic and evolutionary computation conference companion","article-title":"Combining sequential model-based algorithm configuration with default-guided probabilistic sampling","author":"Anastacio","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b6","series-title":"Proceedings of the 16th international conference on parallel problem solving from nature","article-title":"Model-based algorithm configuration with default-guided probabilistic sampling","author":"Anastacio","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b7","unstructured":"Andrychowicz, Marcin, Denil, Misha, Colmenarejo, Sergio Gomez, Hoffman, Matthew W., Pfau, David, Schaul, Tom, & de Freitas, Nando (2016). Learning to learn by gradient descent by gradient descent. In Proceedings of the advances in neural information processing systems (pp. 3981\u20133989)."},{"key":"10.1016\/j.ejor.2025.08.029_b8","series-title":"International conference on principles and practice of constraint programming","first-page":"142","article-title":"A gender-based genetic algorithm for the automatic configuration of algorithms","author":"Ans\u00f3tegui","year":"2009"},{"key":"10.1016\/j.ejor.2025.08.029_b9","series-title":"Finding cuts in the TSP (A preliminary report)","author":"Applegate","year":"1995"},{"key":"10.1016\/j.ejor.2025.08.029_b10","series-title":"Autoformulation of mathematical optimization models using LLMs","author":"Astorga","year":"2024"},{"key":"10.1016\/j.ejor.2025.08.029_b11","doi-asserted-by":"crossref","unstructured":"Azizi, Mohammad Javad, Vayanos, Phebe, Wilder, Bryan, Rice, Eric, & Tambe, Milind (2018). Designing Fair, Efficient, and Interpretable Policies for Prioritizing Homeless Youth for Housing Resources. In Integration of constraint programming, artificial intelligence, and operations research - 15th international conference (pp. 35\u201351).","DOI":"10.1007\/978-3-319-93031-2_3"},{"key":"10.1016\/j.ejor.2025.08.029_b12","unstructured":"Babaeizadeh, Mohammad, Frosio, Iuri, Tyree, Stephen, Clemons, Jason, & Kautz, Jan (2017). Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU. In Proceedings of the international conference on learning representations."},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b13","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1287\/mnsc.49.3.312.12739","article-title":"Using neural network rule extraction and decision tables for credit-risk evaluation","volume":"49","author":"Baesens","year":"2003","journal-title":"Management Science"},{"key":"10.1016\/j.ejor.2025.08.029_b14","series-title":"Hybrid metaheuristics","first-page":"108","article-title":"Improvement strategies for the F-race algorithm: Sampling design and iterative refinement","author":"Balaprakash","year":"2007"},{"key":"10.1016\/j.ejor.2025.08.029_b15","unstructured":"Balcan, Maria-Florina, Dick, Travis, Sandholm, Tuomas, & Vitercik, Ellen (2018). Learning to branch. In Proceedings of the international conference on machine learning (pp. 344\u2013353)."},{"key":"10.1016\/j.ejor.2025.08.029_b16","article-title":"Scoring positive semidefinite cutting planes for quadratic optimization via trained neural networks","author":"Baltean-Lugojan","year":"2018","journal-title":"Optimization Online PReprint"},{"key":"10.1016\/j.ejor.2025.08.029_b17","doi-asserted-by":"crossref","DOI":"10.1287\/trsc.2023.0107","article-title":"Combinatorial optimization-enriched machine learning to solve the dynamic vehicle routing problem with time windows","author":"Baty","year":"2024","journal-title":"Transportation Science"},{"key":"10.1016\/j.ejor.2025.08.029_b18","series-title":"First-order methods in optimization","author":"Beck","year":"2017"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b19","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ejor.2020.07.063","article-title":"Machine learning for combinatorial optimization: A methodological tour d\u2019horizon","volume":"290","author":"Bengio","year":"2021","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b20","doi-asserted-by":"crossref","unstructured":"Bengio, Yoshua, Louradour, J\u00e9r\u00f4me, Collobert, Ronan, & Weston, Jason (2009). Curriculum learning. In Proceedings of the international conference on machine learning (pp. 41\u201348).","DOI":"10.1145\/1553374.1553380"},{"key":"10.1016\/j.ejor.2025.08.029_b21","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/BF01584074","article-title":"Experiments in mixed-integer linear programming","volume":"1","author":"B\u00e9nichou","year":"1971","journal-title":"Mathematical Programming"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b22","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1287\/ijoc.2020.1023","article-title":"JANOS: An integrated predictive and prescriptive modeling framework","volume":"34","author":"Bergman","year":"2021","journal-title":"INFORMS Journal on Computing"},{"key":"10.1016\/j.ejor.2025.08.029_b23","series-title":"Package \u2018lpsolve\u2019","author":"Berkelaar","year":"2015"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13675-018-0109-7","article-title":"Ten years of feasibility pump, and counting","volume":"7","author":"Berthold","year":"2019","journal-title":"EURO Journal on Computational Optimization"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b25","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1287\/mnsc.2018.3253","article-title":"From predictive to prescriptive analytics","volume":"66","author":"Bertsimas","year":"2020","journal-title":"Management Science"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1287\/msom.2015.0548","article-title":"Optimization in online content recommendation services: Beyond click-through rates","volume":"18","author":"Besbes","year":"2015","journal-title":"Manufacturing & Service Operations Management"},{"key":"10.1016\/j.ejor.2025.08.029_b27","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.artint.2016.04.003","article-title":"ASlib: A benchmark library for algorithm selection","volume":"237","author":"Bischl","year":"2016","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.ejor.2025.08.029_b28","series-title":"Proceedings of the international conference on the integration of constraint programming, artificial intelligence, and operations research","first-page":"595","article-title":"Learning a classification of mixed-integer quadratic programming problems","author":"Bonami","year":"2018"},{"issue":"6","key":"10.1016\/j.ejor.2025.08.029_b29","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.1287\/opre.2022.2267","article-title":"A classifier to decide on the linearization of mixed-integer quadratic problems in CPLEX","volume":"70","author":"Bonami","year":"2022","journal-title":"Operations Research"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b30","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ejor.2021.07.016","article-title":"Deep reinforcement learning for inventory control: A roadmap","volume":"298","author":"Boute","year":"2022","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Foundations and Trends in Machine Learning"},{"key":"10.1016\/j.ejor.2025.08.029_b32","series-title":"Language models are few-shot learners","author":"Brown","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b33","series-title":"Sparks of artificial general intelligence: Early experiments with GPT-4","author":"Bubeck","year":"2023"},{"key":"10.1016\/j.ejor.2025.08.029_b34","series-title":"Proximal policy gradient: PPO with policy gradient","author":"Byun","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b35","first-page":"3708","article-title":"The perils of learning before optimizing","volume":"vol. 36","author":"Cameron","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b36","series-title":"Proceedings of the international joint conference on artificial intelligence","first-page":"4348","article-title":"Combinatorial optimization and reasoning with graph neural networks","author":"Cappart","year":"2021"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b37","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1287\/msom.1120.0412","article-title":"Prioritizing burn-injured patients during a disaster","volume":"15","author":"Chan","year":"2013","journal-title":"Manufacturing & Service Operations Management"},{"issue":"189","key":"10.1016\/j.ejor.2025.08.029_b38","first-page":"1","article-title":"Learning to optimize: A primer and a benchmark","volume":"23","author":"Chen","year":"2022","journal-title":"Journal of Machine Learning Researc"},{"key":"10.1016\/j.ejor.2025.08.029_b39","series-title":"Proceedings of the international conference on machine learning","first-page":"1520","article-title":"Learning to stop while learning to predict","author":"Chen","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b40","first-page":"7332","article-title":"Training stronger baselines for learning to optimize","volume":"vol. 33","author":"Chen","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b41","first-page":"9633","article-title":"A deep reinforcement learning framework for column generation","volume":"vol. 35","author":"Chi","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b42","doi-asserted-by":"crossref","unstructured":"Codevilla, Felipe, Santana, Eder, L\u00f3pez, Antonio M Gaidon, Adrien (2019). Exploring the limitations of behavior cloning for autonomous driving. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9329\u20139338).","DOI":"10.1109\/ICCV.2019.00942"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b43","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1287\/opre.2016.1573","article-title":"The impact of linear optimization on promotion planning","volume":"65","author":"Cohen","year":"2017","journal-title":"Operations Research"},{"issue":"53","key":"10.1016\/j.ejor.2025.08.029_b44","first-page":"157","article-title":"V12. 1: User\u2019s manual for CPLEX","volume":"46","author":"CPLEX","year":"2009","journal-title":"International Business Machines Corporation"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b45","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1093\/comjnl\/8.3.250","article-title":"A tree-search algorithm for mixed integer programming problems","volume":"8","author":"Dakin","year":"1965","journal-title":"The Computer Journal"},{"key":"10.1016\/j.ejor.2025.08.029_b46","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s10107-004-0518-7","article-title":"Exploring relaxation induced neighborhoods to improve MIP solutions","volume":"102","author":"Danna","year":"2005","journal-title":"Mathematical Programming"},{"key":"10.1016\/j.ejor.2025.08.029_b47","series-title":"Linear Programming 2: Theory and Extensions","author":"Dantzig","year":"2003"},{"key":"10.1016\/j.ejor.2025.08.029_b48","doi-asserted-by":"crossref","first-page":"110713","DOI":"10.1109\/ACCESS.2024.3441037","article-title":"Counterfactual explanations with multiple properties in credit scoring","volume":"12","author":"Dastile","year":"2024","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b49","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.ejor.2023.09.026","article-title":"Explainable AI for operational research: A defining framework, methods, applications, and a research agenda","volume":"317","author":"De Bock","year":"2024","journal-title":"European Journal of Operational Research"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b50","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.ejor.2024.04.015","article-title":"Explainable analytics for operational research","volume":"317","author":"De Bock","year":"2024","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b51","volume":"vol. 5","author":"Desaulniers","year":"2006"},{"key":"10.1016\/j.ejor.2025.08.029_b52","first-page":"815","article-title":"Machine-learning-based column selection for column generation","volume":"55","author":"Desaulniers","year":"2020","journal-title":"Transportation Science"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b53","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.ejor.2015.08.018","article-title":"DASH: Dynamic approach for switching heuristics","volume":"248","author":"Di Liberto","year":"2016","journal-title":"European Journal of Operational Research"},{"issue":"7","key":"10.1016\/j.ejor.2025.08.029_b54","article-title":"Adaptive subgradient methods for online learning and stochastic optimization.","volume":"12","author":"Duchi","year":"2011","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b55","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1287\/mnsc.2020.3922","article-title":"Smart \u201cpredict, then optimize\u201d","volume":"68","author":"Elmachtoub","year":"2022","journal-title":"Management Science"},{"key":"10.1016\/j.ejor.2025.08.029_b56","series-title":"Integration of constraint programming, artificial intelligence, and operations research","first-page":"176","article-title":"Reinforcement learning for variable selection in a branch and bound algorithm","author":"Etheve","year":"2020"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2023.04.041","article-title":"Optimization with constraint learning: A framework and survey","volume":"314","author":"Fajemisin","year":"2024","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b58","first-page":"9650","article-title":"Smart initial basis selection for linear programs","volume":"vol. 202","author":"Fan","year":"2023"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b59","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1287\/msom.2015.0561","article-title":"Analytics for an online retailer: Demand forecasting and price optimization","volume":"18","author":"Ferreira","year":"2015","journal-title":"Manufacturing & Service Operations Management"},{"key":"10.1016\/j.ejor.2025.08.029_b60","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10107-003-0395-5","article-title":"Local branching","volume":"98","author":"Fischetti","year":"2003","journal-title":"Mathematical Programming"},{"issue":"1\u20133","key":"10.1016\/j.ejor.2025.08.029_b61","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01581089","article-title":"Steepest-edge simplex algorithms for linear programming","volume":"57","author":"Forrest","year":"1992","journal-title":"Mathematical Programming"},{"key":"10.1016\/j.ejor.2025.08.029_b62","series-title":"Augmented Lagrangian methods: applications to the numerical solution of boundary-value problems","author":"Fortin","year":"2000"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b63","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/BF00247655","article-title":"Application of the alternating direction method of multipliers to separable convex programming problems","volume":"1","author":"Fukushima","year":"1992","journal-title":"Computational Optimization and Applications"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b64","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s00291-020-00615-8","article-title":"A machine learning-based branch and price algorithm for a sampled vehicle routing problem","volume":"43","author":"Furian","year":"2021","journal-title":"OR Spectrum"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b65","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.ejor.2020.08.045","article-title":"Optimization problems for machine learning: A survey","volume":"290","author":"Gambella","year":"2021","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b66","doi-asserted-by":"crossref","unstructured":"Gao, Mingqi, Wan, Xiaojun, Su, Jia, Wang, Zhefeng, & Huai, Baoxing (2023). Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework. In Proceedings of the association for computational linguistics (pp. 13932\u201313959).","DOI":"10.18653\/v1\/2023.acl-long.779"},{"key":"10.1016\/j.ejor.2025.08.029_b67","first-page":"15554","article-title":"Exact combinatorial optimization with graph convolutional neural networks","volume":"vol. 32","author":"Gasse","year":"2019"},{"issue":"5","key":"10.1016\/j.ejor.2025.08.029_b68","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1287\/ijoc.2022.0090","article-title":"Learning for spatial branching: An algorithm selection approach","volume":"35","author":"Ghaddar","year":"2023","journal-title":"INFORMS Journal on Computing"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b69","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.ejor.2023.04.013","article-title":"A framework for inherently interpretable optimization models","volume":"310","author":"Goerigk","year":"2023","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b70","series-title":"An algorithm for the mixed integer problem","author":"Gomory","year":"1960"},{"key":"10.1016\/j.ejor.2025.08.029_b71","series-title":"Polynomial optimization: Enhancing RLT relaxations with conic constraints","author":"Gonz\u00e1lez-Rodr\u00edguez","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b72","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-16194-1_9","article-title":"Solving combinatorial problems with machine learning methods","author":"Guo","year":"2019","journal-title":"Nonlinear Combinatorial Optimization"},{"key":"10.1016\/j.ejor.2025.08.029_b73","first-page":"18087","article-title":"Hybrid models for learning to branch","volume":"vol. 33","author":"Gupta","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b74","article-title":"Lookback for learning to branch","author":"Gupta","year":"2022","journal-title":"Transactions on Machine Learning Research"},{"key":"10.1016\/j.ejor.2025.08.029_b75","series-title":"Gurobi Optimizer Reference Manual","author":"Gurobi","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b76","unstructured":"Haarnoja, Tuomas, Zhou, Aurick, Abbeel, Pieter, & Levine, Sergey (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the international conference on machine learning."},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b77","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","article-title":"A formal basis for the heuristic determination of minimum cost paths","volume":"4","author":"Hart","year":"1968","journal-title":"IEEE Transactions on Systems Science and Cybernetics"},{"key":"10.1016\/j.ejor.2025.08.029_b78","first-page":"3293","article-title":"Learning to search in branch and bound algorithms","volume":"vol. 27","author":"He","year":"2014"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b79","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1023\/A:1004603514434","article-title":"Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities","volume":"106","author":"He","year":"2000","journal-title":"Journal of Optimization Theory and Applications"},{"key":"10.1016\/j.ejor.2025.08.029_b80","series-title":"Handbook of satisfiability","first-page":"481","article-title":"Automated configuration and selection of SAT solvers","author":"Hoos","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b81","series-title":"Branch and bound in mixed integer linear programming problems: A survey of techniques and trends","author":"Huang","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b82","unstructured":"Huang, Wenlong, Mordatch, Igor, & Pathak, Deepak (2020). One policy to control them all: Shared modular policies for agent-agnostic control. In Proceedings of the international conference on machine learning (pp. 4455\u20134464)."},{"key":"10.1016\/j.ejor.2025.08.029_b83","series-title":"ORLM: A customizable framework in training large models for automated optimization modeling","author":"Huang","year":"2025"},{"key":"10.1016\/j.ejor.2025.08.029_b84","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108353","article-title":"Learning to select cuts for efficient mixed-integer programming","volume":"123","author":"Huang","year":"2022","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ejor.2025.08.029_b85","series-title":"Optverse solver","author":"Huawei","year":"2021"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b86","doi-asserted-by":"crossref","DOI":"10.1145\/3054912","article-title":"Imitation learning: A survey of learning methods","volume":"50","author":"Hussein","year":"2017","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.ejor.2025.08.029_b87","series-title":"Integration of AI and OR techniques in constraint programming for combinatorial optimization problems","first-page":"186","article-title":"Automated configuration of mixed integer programming solvers","author":"Hutter","year":"2010"},{"key":"10.1016\/j.ejor.2025.08.029_b88","series-title":"International conference on learning and intelligent optimization","first-page":"507","article-title":"Sequential model-based optimization for general algorithm configuration","volume":"vol. 6683","author":"Hutter","year":"2011"},{"key":"10.1016\/j.ejor.2025.08.029_b89","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1613\/jair.2861","article-title":"ParamILS: An automatic algorithm configuration framework","volume":"36","author":"Hutter","year":"2009","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10.1016\/j.ejor.2025.08.029_b90","first-page":"21043","article-title":"Accelerating quadratic optimization with reinforcement learning","volume":"vol. 34","author":"Ichnowski","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b91","unstructured":"Jiang, Caigao, Shu, Xiang, Qian, Hong, Lu, Xingyu, Zhou, Jun, Zhou, Aimin, & Yu, Yang (2025). LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch. In The thirteenth international conference on learning representations."},{"key":"10.1016\/j.ejor.2025.08.029_b92","doi-asserted-by":"crossref","unstructured":"Jo, Nathanael, Aghaei, Sina, Benson, Jack, G\u2019omez, Andr\u2019es, & Vayanos, Phebe (2023). Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy. In Proceedings of the 2023 AAAI\/ACM conference on AI, ethics, and society (pp. 181\u2013192).","DOI":"10.1145\/3600211.3604664"},{"key":"10.1016\/j.ejor.2025.08.029_b93","series-title":"Learning context-aware adaptive solvers to accelerate quadratic programming","author":"Jung","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b94","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/s43069-024-00327-7","article-title":"Configuring mixed-integer programming solvers for large-scale instances","volume":"5","author":"Kemminer","year":"2024","journal-title":"Operations Research Forum"},{"key":"10.1016\/j.ejor.2025.08.029_b95","unstructured":"Khalil, Elias, Dai, Hanjun, Zhang, Yuyu, Dilkina, Bistra, & Song, Le (2017). Learning combinatorial optimization algorithms over graphs. In Proceedings of the advances in neural information processing systems (pp. 6348\u20136358)."},{"key":"10.1016\/j.ejor.2025.08.029_b96","article-title":"Learning to branch in mixed integer programming","volume":"vol. 30","author":"Khalil","year":"2016"},{"key":"10.1016\/j.ejor.2025.08.029_b97","unstructured":"Kingma, Diederik P., & Ba, Jimmy (2015). Adam: A Method for Stochastic Optimization. In Proceedings of the international conference on learning representations."},{"key":"10.1016\/j.ejor.2025.08.029_b98","series-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b99","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/BF02680549","article-title":"A variable-penalty alternating directions method for convex optimization","volume":"83","author":"Kontogiorgis","year":"1998","journal-title":"Mathematical Programming"},{"key":"10.1016\/j.ejor.2025.08.029_b100","doi-asserted-by":"crossref","unstructured":"Kotary, James, Fioretto, Ferdinando, Van Hentenryck, Pascal, & Wilder, Bryan (2021). End-to-End Constrained Optimization Learning: A Survey. In Proceedings of the international joint conference on artificial intelligence (pp. 4475\u20134482).","DOI":"10.24963\/ijcai.2021\/610"},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b101","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.ejor.2019.09.018","article-title":"Deep learning in business analytics and operations research: Models, applications and managerial implications","volume":"281","author":"Kraus","year":"2020","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b102","first-page":"32000","article-title":"Learning to compare nodes in branch and bound with graph neural networks","volume":"vol. 35","author":"Labassi","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b103","series-title":"An automatic method for solving discrete programming problems","author":"Land","year":"2010"},{"key":"10.1016\/j.ejor.2025.08.029_b104","doi-asserted-by":"crossref","unstructured":"Lewis, Mike, Liu, Yinhan, Goyal, Naman, Ghazvininejad, Marjan, Mohamed, Abdelrahman, Levy, Omer, Stoyanov, Veselin, & Zettlemoyer, Luke (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 7871\u20137880).","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"10.1016\/j.ejor.2025.08.029_b105","series-title":"Rethinking optimal pivoting paths of simplex method","author":"Li","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b106","unstructured":"Li, Ke, & Malik, Jitendra (2016). Learning to optimize. In Proceedings of the international conference on learning representations."},{"key":"10.1016\/j.ejor.2025.08.029_b107","series-title":"Learning to optimize neural nets","author":"Li","year":"2017"},{"issue":"54","key":"10.1016\/j.ejor.2025.08.029_b108","first-page":"1","article-title":"SMAC3: A versatile Bayesian optimization package for hyperparameter optimization","volume":"23","author":"Lindauer","year":"2022","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.ejor.2025.08.029_b109","doi-asserted-by":"crossref","unstructured":"Lindauer, Marius, & Hutter, Frank (2018). Warmstarting of Model-Based Algorithm Configuration. In Proceedings of the AAAI conference on artificial intelligence (pp. 1355\u20131362).","DOI":"10.1609\/aaai.v32i1.11532"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b110","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1287\/ijoc.11.2.173","article-title":"A computational study of search strategies for mixed integer programming","volume":"11","author":"Linderoth","year":"1999","journal-title":"INFORMS Journal on Computing"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b111","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/00207543.2020.1841318","article-title":"A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect","volume":"59","author":"Liu","year":"2021","journal-title":"International Journal of Production Research"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b112","first-page":"207","article-title":"On learning and branching: a survey","volume":"25","author":"Lodi","year":"2017","journal-title":"Journal of the Spanish Society of Statistics and Operations Research"},{"key":"10.1016\/j.ejor.2025.08.029_b113","doi-asserted-by":"crossref","unstructured":"Lombardi, Michele, & Milano, Michela (2018). Boosting Combinatorial Problem Modeling with Machine Learning. In Proceedings of the international joint conference on artificial intelligence (pp. 5472\u20135478).","DOI":"10.24963\/ijcai.2018\/772"},{"key":"10.1016\/j.ejor.2025.08.029_b114","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","article-title":"The irace package: Iterated racing for automatic algorithm configuration","volume":"3","author":"L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","journal-title":"Operations Research Perspectives"},{"key":"10.1016\/j.ejor.2025.08.029_b115","series-title":"Advances in neural information processing systems","first-page":"4766","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.ejor.2025.08.029_b116","unstructured":"Lv, Kaifeng, Jiang, Shunhua, & Li, Jian (2017). Learning gradient descent: Better generalization and longer horizons. In Proceedings of the international conference on machine learning (pp. 2247\u20132255)."},{"key":"10.1016\/j.ejor.2025.08.029_b117","first-page":"1603","article-title":"Smart predict-and-optimize for hard combinatorial optimization problems","volume":"vol. 34","author":"Mandi","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b118","series-title":"Proceedings of the matheuristics","first-page":"133","article-title":"Diving heuristics","author":"Maniezzo","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b119","article-title":"Mixed-integer optimization with constraint learning","author":"Maragno","year":"2023","journal-title":"Operations Research"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b120","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TKDE.2008.131","article-title":"Decompositional rule extraction from support vector machines by active learning","volume":"21","author":"Martens","year":"2009","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b121","doi-asserted-by":"crossref","first-page":"73","DOI":"10.25300\/MISQ\/2014\/38.1.04","article-title":"Explaining data-driven document classifications","volume":"38","author":"Martens","year":"2014","journal-title":"MIS Quarterly: Management Information Systems"},{"key":"10.1016\/j.ejor.2025.08.029_b122","first-page":"64","article-title":"Recurrent neural networks","volume":"5","author":"Medsker","year":"2001","journal-title":"Design and Applications"},{"key":"10.1016\/j.ejor.2025.08.029_b123","unstructured":"Metz, Luke, Maheswaranathan, Niru, Nixon, Jeremy, Freeman, Daniel, & Sohl-Dickstein, Jascha (2019). Understanding and correcting pathologies in the training of learned optimizers. In Proceedings of the international conference on machine learning (pp. 4556\u20134565)."},{"issue":"3","key":"10.1016\/j.ejor.2025.08.029_b124","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TPWRS.2018.2886344","article-title":"Adaptive ADMM for distributed AC optimal power flow","volume":"34","author":"Mhanna","year":"2018","journal-title":"IEEE Transactions on Power Systems"},{"key":"10.1016\/j.ejor.2025.08.029_b125","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","volume":"vol. 48","author":"Mnih","year":"2016"},{"key":"10.1016\/j.ejor.2025.08.029_b126","doi-asserted-by":"crossref","unstructured":"Mostajabdaveh, Mahdi, Yu, Timothy T., Dash, Samarendra Chandan Bindu, Ramamonjison, Rindranirina, Byusa, Jabo Serge, Carenini, Giuseppe, Zhou, Zirui, & Zhang, Yong (2025). Evaluating LLM Reasoning in the Operations Research Domain with ORQA. In Proceedings of the AAAI conference on artificial intelligence.","DOI":"10.1609\/aaai.v39i23.34673"},{"key":"10.1016\/j.ejor.2025.08.029_b127","first-page":"1","article-title":"Optimization modeling and verification from problem specifications using a multi-agent multi-stage LLM framework","author":"Mostajabdaveh","year":"2024","journal-title":"INFOR. Information Systems and Operational Research"},{"key":"10.1016\/j.ejor.2025.08.029_b128","series-title":"Solving mixed integer programs using neural networks","author":"Nair","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b129","series-title":"Integration of AI and OR techniques in constraint programming for combinatorial optimization problems","first-page":"154","article-title":"A probing algorithm for MINLP with failure prediction by SVM","author":"Nannicini","year":"2011"},{"key":"10.1016\/j.ejor.2025.08.029_b130","first-page":"543","article-title":"A method for solving the convex programming problem with convergence rate O(1\/k2)","volume":"vol. 269","author":"Nesterov","year":"1983"},{"key":"10.1016\/j.ejor.2025.08.029_b131","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"vol. 35","author":"Ouyang","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b132","unstructured":"Pascanu, Razvan, Mikolov, Tomas, & Bengio, Yoshua (2013). On the difficulty of training recurrent neural networks. In Proceedings of the international conference on machine learning (pp. 1310\u20131318)."},{"key":"10.1016\/j.ejor.2025.08.029_b133","unstructured":"Paulus, Max B, Zarpellon, Giulia, Krause, Andreas, Charlin, Laurent, & Maddison, Chris (2022). Learning to cut by looking ahead: Cutting plane selection via imitation learning. In Proceedings of the international conference on machine learning (pp. 17584\u201317600)."},{"key":"10.1016\/j.ejor.2025.08.029_b134","series-title":"Handbook of Metaheuristics","article-title":"Large neighborhood search","author":"Pisinger","year":"2018"},{"key":"10.1016\/j.ejor.2025.08.029_b135","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s12532-020-00188-1","article-title":"A triangulation and fill-reducing initialization procedure for the simplex algorithm","volume":"13","author":"Ploskas","year":"2021","journal-title":"Mathematical Programming Computation"},{"key":"10.1016\/j.ejor.2025.08.029_b136","unstructured":"Pogancic, Marin Vlastelica, Paulus, Anselm, Musil, V\u00edt, Martius, Georg, & Rol\u00ednek, Michal (2020). Differentiation of Blackbox Combinatorial Solvers. In Proceedings of the international conference on learning representations."},{"key":"10.1016\/j.ejor.2025.08.029_b137","series-title":"Smart feasibility pump: Reinforcement learning for (mixed) integer programming","author":"Qi","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b138","series-title":"An improved reinforcement learning algorithm for learning to branch","author":"Qu","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b139","series-title":"Maynard\u2019s Industrial Engineering Handbook","first-page":"11","article-title":"Principles and applications of operations research","author":"Rajgopal","year":"2004"},{"key":"10.1016\/j.ejor.2025.08.029_b140","series-title":"neurIPS 2022 competition track","first-page":"189","article-title":"NL4Opt competition: Formulating optimization problems based on their natural language descriptions","author":"Ramamonjison","year":"2023"},{"issue":"4","key":"10.1016\/j.ejor.2025.08.029_b141","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s11634-020-00418-3","article-title":"A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C","volume":"14","author":"Ramon","year":"2020","journal-title":"Advances in Data Analysis and Classification"},{"key":"10.1016\/j.ejor.2025.08.029_b142","series-title":"Findings of the association for computational linguistics","first-page":"3822","article-title":"Learning to model editing processes","author":"Reid","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b143","first-page":"1135","article-title":"\u201cWhy should I trust you?\u201d explaining the predictions of any classifier","volume":"vol. 13\u201317","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.ejor.2025.08.029_b144","series-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics, AISTATS 2010","first-page":"661","article-title":"Efficient reductions for imitation learning","volume":"vol. 9","author":"Ross","year":"2010"},{"key":"10.1016\/j.ejor.2025.08.029_b145","series-title":"Code llama: Open foundation models for code","author":"Rozi\u00e8re","year":"2023"},{"key":"10.1016\/j.ejor.2025.08.029_b146","series-title":"An overview of gradient descent optimization algorithms","author":"Ruder","year":"2016"},{"key":"10.1016\/j.ejor.2025.08.029_b147","series-title":"Proceedings of the integration of AI and OR techniques in contraint programming for combinatorial optimzation problems","first-page":"356","article-title":"Guiding combinatorial optimization with UCT","author":"Sabharwal","year":"2012"},{"key":"10.1016\/j.ejor.2025.08.029_b148","first-page":"1","article-title":"Machine learning augmented branch and bound for mixed integer linear programming","author":"Scavuzzo","year":"2024","journal-title":"Mathematical Programming"},{"key":"10.1016\/j.ejor.2025.08.029_b149","first-page":"13676","article-title":"A survey of methods for automated algorithm configuration","volume":"1","author":"Schede","year":"2022","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10.1016\/j.ejor.2025.08.029_b150","first-page":"1889","article-title":"Trust region policy optimization","volume":"vol. 37","author":"Schulman","year":"2015"},{"issue":"8","key":"10.1016\/j.ejor.2025.08.029_b151","doi-asserted-by":"crossref","first-page":"5704","DOI":"10.1287\/mnsc.2021.4190","article-title":"Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing","volume":"68","author":"Senoner","year":"2022","journal-title":"Management Science"},{"key":"10.1016\/j.ejor.2025.08.029_b152","doi-asserted-by":"crossref","unstructured":"Shaw, Paul (1998). Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. In Proceedings of the international conference on principles and practice of constraint programming.","DOI":"10.1007\/3-540-49481-2_30"},{"key":"10.1016\/j.ejor.2025.08.029_b153","first-page":"9926","article-title":"Enhancing column generation by a machine-learning-based pricing heuristic for graph coloring","volume":"vol. 36","author":"Shen","year":"2022"},{"key":"10.1016\/j.ejor.2025.08.029_b154","series-title":"Learning to search via retrospective imitation","author":"Song","year":"2018"},{"key":"10.1016\/j.ejor.2025.08.029_b155","first-page":"20012","article-title":"A general large neighborhood search framework for solving integer linear programs","volume":"vol. 33","author":"Song","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b156","series-title":"Learning a large neighborhood search algorithm for mixed integer programs","author":"Sonnerat","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b157","series-title":"Understanding LSTM \u2013 a tutorial into long short-term memory recurrent neural networks","author":"Staudemeyer","year":"2019"},{"issue":"4","key":"10.1016\/j.ejor.2025.08.029_b158","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s12532-020-00179-2","article-title":"OSQP: An operator splitting solver for quadratic programs","volume":"12","author":"Stellato","year":"2020","journal-title":"Mathematical Programming Computation"},{"key":"10.1016\/j.ejor.2025.08.029_b159","series-title":"NeurIPS 2020 workshop on learning meets combinatorial algorithms","article-title":"Improving learning to branch via reinforcement learning","author":"Sun","year":"2020"},{"key":"10.1016\/j.ejor.2025.08.029_b160","first-page":"1","article-title":"Reinforcement learning of simplex pivot rules: a proof of concept","author":"Suriyanarayana","year":"2022","journal-title":"Optimization Letters"},{"key":"10.1016\/j.ejor.2025.08.029_b161","series-title":"Metaheuristics: from design to implementation","author":"Talbi","year":"2009"},{"key":"10.1016\/j.ejor.2025.08.029_b162","unstructured":"Tang, Yunhao, Agrawal, Shipra Faenza, Yuri (2020). Reinforcement learning for integer programming: Learning to cut. In Proceedings of the international conference on machine learning (pp. 9367\u20139376)."},{"key":"10.1016\/j.ejor.2025.08.029_b163","series-title":"Stanford alpaca: An instruction-following LLaMA model","author":"Taori","year":"2023"},{"key":"10.1016\/j.ejor.2025.08.029_b164","doi-asserted-by":"crossref","unstructured":"Torabi, Faraz, Warnell, Garrett, & Stone, Peter (2018). Behavioral Cloning from Observation. In Proceedings of the international joint conference on artificial intelligence (pp. 4950\u20134957).","DOI":"10.24963\/ijcai.2018\/687"},{"key":"10.1016\/j.ejor.2025.08.029_b165","series-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b166","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1137\/S1052623495294797","article-title":"An incremental gradient (-projection) method with momentum term and adaptive stepsize rule","volume":"8","author":"Tseng","year":"1998","journal-title":"SIAM Journal on Optimization"},{"key":"10.1016\/j.ejor.2025.08.029_b167","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2023.102538","article-title":"Everything is connected: Graph neural networks","volume":"79","author":"Veli\u010dkovi\u0107","year":"2023","journal-title":"Current Opinion in Structural Biology"},{"key":"10.1016\/j.ejor.2025.08.029_b168","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.asoc.2017.01.042","article-title":"RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints","volume":"60","author":"Verbeke","year":"2017","journal-title":"Applied Soft Computing Journal"},{"key":"10.1016\/j.ejor.2025.08.029_b169","first-page":"6545","article-title":"Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver","volume":"vol. 97","author":"Wang","year":"2019"},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b170","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1023\/A:1017522623963","article-title":"Decomposition method with a variable parameter for a class of monotone variational inequality problems","volume":"109","author":"Wang","year":"2001","journal-title":"Journal of Optimization Theory and Applications"},{"key":"10.1016\/j.ejor.2025.08.029_b171","doi-asserted-by":"crossref","unstructured":"Wen, Tsung-Hsien, Vandyke, David, Mrk\u0161i\u0107, Nikola, Gasic, Milica, Barahona, Lina M Rojas, Su, Pei-Hao, Ultes, Stefan, & Young, Steve (2017). A Network-based End-to-End Trainable Task-oriented Dialogue System. In Proceedings of the European chapter of the association for computational linguistics (pp. 438\u2013449).","DOI":"10.18653\/v1\/E17-1042"},{"key":"10.1016\/j.ejor.2025.08.029_b172","unstructured":"Wichrowska, Olga, Maheswaranathan, Niru, Hoffman, Matthew W, Colmenarejo, Sergio Gomez, Denil, Misha, Freitas, Nando, & Sohl-Dickstein, Jascha (2017). Learned optimizers that scale and generalize. In Proceedings of the international conference on machine learning (pp. 3751\u20133760)."},{"key":"10.1016\/j.ejor.2025.08.029_b173","series-title":"Reinforcement learning","first-page":"5","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","author":"Williams","year":"1992"},{"key":"10.1016\/j.ejor.2025.08.029_b174","series-title":"Operations research: applications and algorithms","author":"Winston","year":"2004"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b175","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"10.1016\/j.ejor.2025.08.029_b176","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.ejor.2025.08.029_b177","series-title":"Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation","first-page":"16550","article-title":"Towards human-aligned evaluation for linear programming word problems","author":"Xing","year":"2024"},{"key":"10.1016\/j.ejor.2025.08.029_b178","unstructured":"Xu, Keyulu, Hu, Weihua, Leskovec, Jure, & Jegelka, Stefanie (2019). How powerful are graph neural networks?. In Proceedings of the international conference on learning representations."},{"key":"10.1016\/j.ejor.2025.08.029_b179","first-page":"21520","article-title":"A surrogate objective framework for prediction+ programming with soft constraints","volume":"vol. 34","author":"Yan","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b180","doi-asserted-by":"crossref","unstructured":"Yan, Junchi, Yang, Shuang, & Hancock, Edwin R. (2020). Learning for Graph Matching and Related Combinatorial Optimization Problems. In Proceedings of the international joint conference on artificial intelligence (pp. 4988\u20134996).","DOI":"10.24963\/ijcai.2020\/694"},{"key":"10.1016\/j.ejor.2025.08.029_b181","unstructured":"Yang, Zhicheng, Wang, Yiwei, Huang, Yinya, Guo, Zhijiang, Shi, Wei, Han, Xiongwei, Feng, Liang, Song, Linqi, Liang, Xiaodan, & Tang, Jing (2025). OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling. In The thirteenth international conference on learning representations."},{"issue":"2","key":"10.1016\/j.ejor.2025.08.029_b182","first-page":"150","article-title":"A study of learning search approximation in mixed integer branch and bound: Node selection in SCIP","volume":"2","author":"Yilmaz","year":"2021","journal-title":"Artificial Intelligence"},{"issue":"7","key":"10.1016\/j.ejor.2025.08.029_b183","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Computation"},{"key":"10.1016\/j.ejor.2025.08.029_b184","first-page":"3931","article-title":"Parameterizing branch-and-bound search trees to learn branching policies","volume":"vol. 35","author":"Zarpellon","year":"2021"},{"key":"10.1016\/j.ejor.2025.08.029_b185","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2022.108546","article-title":"A reinforcement learning approach to parameter selection for distributed optimal power flow","volume":"212","author":"Zeng","year":"2022","journal-title":"Electric Power Systems Research"},{"key":"10.1016\/j.ejor.2025.08.029_b186","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.neucom.2022.11.024","article-title":"A survey for solving mixed integer programming via machine learning","volume":"519","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ejor.2025.08.029_b187","doi-asserted-by":"crossref","DOI":"10.1049\/cim2.12072","article-title":"A review on learning to solve combinatorial optimisation problems in manufacturing","author":"Zhang","year":"2023","journal-title":"IET Collaborative Intelligent Manufacturing"},{"key":"10.1016\/j.ejor.2025.08.029_b188","unstructured":"Zheng, Wenqing, Chen, Tianlong, Hu, Ting-Kuei, & Wang, Zhangyang (2022). Symbolic Learning to Optimize: Towards Interpretability and Scalability. In International conference on learning representations."},{"key":"10.1016\/j.ejor.2025.08.029_b189","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.ejor.2025.08.029_b190","series-title":"Lima: Less is more for alignment","author":"Zhou","year":"2023"}],"container-title":["European Journal of Operational Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0377221725006666?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0377221725006666?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T05:30:07Z","timestamp":1773293407000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0377221725006666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":190,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S0377221725006666"],"URL":"https:\/\/doi.org\/10.1016\/j.ejor.2025.08.029","relation":{},"ISSN":["0377-2217"],"issn-type":[{"value":"0377-2217","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence for optimization: Unleashing the potential of parameter generation, model formulation, and solution methods","name":"articletitle","label":"Article Title"},{"value":"European Journal of Operational Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejor.2025.08.029","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}