IDEAS home Printed from https://ideas.repec.org/r/eee/ejores/v290y2021i2p405-421.html
   My bibliography  Save this item

Machine learning for combinatorial optimization: A methodological tour d’horizon

Citations

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


Cited by:

  1. Yang, Yu & Boland, Natashia & Dilkina, Bistra & Savelsbergh, Martin, 2022. "Learning generalized strong branching for set covering, set packing, and 0–1 knapsack problems," European Journal of Operational Research, Elsevier, vol. 301(3), pages 828-840.
  2. 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.
  3. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
  4. Korekane, Shinya & Nishi, Tatsushi & Tierney, Kevin & Liu, Ziang, 2024. "Neural network assisted branch and bound algorithm for dynamic berth allocation problems," European Journal of Operational Research, Elsevier, vol. 319(2), pages 531-542.
  5. Robert P. Rooderkerk & Nicole DeHoratius & Andrés Musalem, 2022. "The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3727-3748, October.
  6. Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
  7. Juho Lauri & Sourav Dutta & Marco Grassia & Deepak Ajwani, 2023. "Learning fine-grained search space pruning and heuristics for combinatorial optimization," Journal of Heuristics, Springer, vol. 29(2), pages 313-347, June.
  8. Ahmet Herekoğlu & Özgür Kabak, 2024. "Crew recovery optimization with deep learning and column generation for sustainable airline operation management," Annals of Operations Research, Springer, vol. 342(1), pages 399-427, November.
  9. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
  10. Sun, Yanshuo & Kirtonia, Sajeeb & Chen, Zhi-Long, 2021. "A survey of finished vehicle distribution and related problems from an optimization perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
  11. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
  12. Parmentier, Axel & T’Kindt, Vincent, 2023. "Structured learning based heuristics to solve the single machine scheduling problem with release times and sum of completion times," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1032-1041.
  13. Fajemisin, Adejuyigbe O. & Maragno, Donato & den Hertog, Dick, 2024. "Optimization with constraint learning: A framework and survey," European Journal of Operational Research, Elsevier, vol. 314(1), pages 1-14.
  14. Luis O. Lara-Cerecedo & Jesús F. Hinojosa & Nun Pitalúa-Díaz & Yasuhiro Matsumoto & Alvaro González-Angeles, 2023. "Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO," Energies, MDPI, vol. 16(16), pages 1-26, August.
  15. Adamo, Tommaso & Gendreau, Michel & Ghiani, Gianpaolo & Guerriero, Emanuela, 2024. "A review of recent advances in time-dependent vehicle routing," European Journal of Operational Research, Elsevier, vol. 319(1), pages 1-15.
  16. Fang, Chao & Han, Zonglei & Wang, Wei & Zio, Enrico, 2023. "Routing UAVs in landslides Monitoring: A neural network heuristic for team orienteering with mandatory visits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
  17. Shen, Yunzhuang & Sun, Yuan & Li, Xiaodong & Eberhard, Andrew & Ernst, Andreas, 2023. "Adaptive solution prediction for combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1392-1408.
  18. 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).
  19. Silva, Allyson & Roodbergen, Kees Jan & Coelho, Leandro C. & Darvish, Maryam, 2022. "Estimating optimal ABC zone sizes in manual warehouses," International Journal of Production Economics, Elsevier, vol. 252(C).
  20. Yaping Ren & Xinyu Lu & Hongfei Guo & Zhaokang Xie & Haoyang Zhang & Chaoyong Zhang, 2023. "A Review of Combinatorial Optimization Problems in Reverse Logistics and Remanufacturing for End-of-Life Products," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
  21. Lin, Yun Hui & Yin, Xiao Feng & Tian, Qingyun, 2024. "Unlocking efficiency: End-to-end optimization learning for recurrent facility operational planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
  22. Bouška, Michal & Šůcha, Přemysl & Novák, Antonín & Hanzálek, Zdeněk, 2023. "Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness," European Journal of Operational Research, Elsevier, vol. 308(3), pages 990-1006.
  23. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2024. "A flow based formulation and a reinforcement learning based strategic oscillation for cross-dock door assignment," European Journal of Operational Research, Elsevier, vol. 312(2), pages 473-492.
  24. Guo, Feng & Wei, Qu & Wang, Miao & Guo, Zhaoxia & Wallace, Stein W., 2023. "Deep attention models with dimension-reduction and gate mechanisms for solving practical time-dependent vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
  25. Bongiovanni, Claudia & Kaspi, Mor & Cordeau, Jean-François & Geroliminis, Nikolas, 2022. "A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
  26. Qiang Zhang & Shi Qiang Liu & Andrea D’Ariano, 2023. "Bi-objective bi-level optimization for integrating lane-level closure and reversal in redesigning transportation networks," Operational Research, Springer, vol. 23(2), pages 1-51, June.
  27. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
  28. Yagmur S. Gök & Silvia Padrón & Maurizio Tomasella & Daniel Guimarans & Cemalettin Ozturk, 2023. "Constraint-based robust planning and scheduling of airport apron operations through simheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 795-830, January.
  29. van der Hagen, L. & Agatz, N.A.H. & Spliet, R. & Visser, T.R. & Kok, A.L., 2022. "Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management," ERIM Report Series Research in Management ERS-2022-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  30. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
  31. Laporte, Gilbert, 2024. "Fifty years of operational research: 1972–2022," European Journal of Operational Research, Elsevier, vol. 319(2), pages 347-360.
  32. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.
  33. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pasdeloup, Bastien & Meyer, Patrick, 2023. "Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1296-1330.
  34. Bootaki, Behrang & Zhang, Guoqing, 2024. "A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology," International Journal of Production Economics, Elsevier, vol. 275(C).
  35. Andre A. Cire & Adam Diamant, 2022. "Dynamic scheduling of home care patients to medical providers," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4038-4056, November.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.