IDEAS home Printed from https://ideas.repec.org/r/inm/ormnsc/v66y2020i3p1025-1044.html
   My bibliography  Save this item

From Predictive to Prescriptive Analytics

Citations

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


Cited by:

  1. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
  2. Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2022. "A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization," DES - Working Papers. Statistics and Econometrics. WS 36072, Universidad Carlos III de Madrid. Departamento de Estadística.
  3. Bertsimas, Dimitris & McCord, Christopher & Sturt, Bradley, 2023. "Dynamic optimization with side information," European Journal of Operational Research, Elsevier, vol. 304(2), pages 634-651.
  4. 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.
  5. Shaochong Lin & Youhua (Frank) Chen & Yanzhi Li & Zuo‐Jun Max Shen, 2022. "Data‐Driven Newsvendor Problems Regularized by a Profit Risk Constraint," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1630-1644, April.
  6. Apostolos Burnetas, 2022. "Learning and data-driven optimization in queues with strategic customers," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 517-519, April.
  7. 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.
  8. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
  9. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
  10. Andrew Butler & Roy Kwon, 2021. "Efficient differentiable quadratic programming layers: an ADMM approach," Papers 2112.07464, arXiv.org.
  11. Bernardo K. Pagnoncelli & Domingo Ramírez & Hamed Rahimian & Arturo Cifuentes, 2023. "A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 187-204, June.
  12. Nam Ho-Nguyen & Fatma Kılınç-Karzan, 2022. "Risk Guarantees for End-to-End Prediction and Optimization Processes," Management Science, INFORMS, vol. 68(12), pages 8680-8698, December.
  13. Liu, Congzheng & Letchford, Adam N. & Svetunkov, Ivan, 2022. "Newsvendor problems: An integrated method for estimation and optimisation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 590-601.
  14. Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2022. "On data-driven chance constraint learning for mixed-integer optimization problems," DES - Working Papers. Statistics and Econometrics. WS 35425, Universidad Carlos III de Madrid. Departamento de Estadística.
  15. Yichun Hu & Nathan Kallus & Xiaojie Mao, 2022. "Fast Rates for Contextual Linear Optimization," Management Science, INFORMS, vol. 68(6), pages 4236-4245, June.
  16. Keliang Wang & Leonardo Lozano & Carlos Cardonha & David Bergman, 2023. "Optimizing over an Ensemble of Trained Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 652-674, May.
  17. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
  18. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
  19. Meng Qi & Ying Cao & Zuo-Jun (Max) Shen, 2022. "Distributionally Robust Conditional Quantile Prediction with Fixed Design," Management Science, INFORMS, vol. 68(3), pages 1639-1658, March.
  20. van Eekelen, Wouter, 2023. "Distributionally robust views on queues and related stochastic models," Other publications TiSEM 9b99fc05-9d68-48eb-ae8c-9, Tilburg University, School of Economics and Management.
  21. Adriana Tiron-Tudor & Delia Deliu, 2021. "Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study," JRFM, MDPI, vol. 14(8), pages 1-26, August.
  22. Pirayesh Neghab, Davood & Khayyati, Siamak & Karaesmen, Fikri, 2022. "An integrated data-driven method using deep learning for a newsvendor problem with unobservable features," European Journal of Operational Research, Elsevier, vol. 302(2), pages 482-496.
  23. Muzaffer Buyruk & Ertan Güner, 2022. "Personalization in airline revenue management: an overview and future outlook," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 129-139, April.
  24. Jos'e-Manuel Pe~na & Fernando Su'arez & Omar Larr'e & Domingo Ram'irez & Arturo Cifuentes, 2023. "A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation," Papers 2302.02269, arXiv.org, revised Feb 2023.
  25. Hauser, Matthias & Flath, Christoph M. & Thiesse, Frédéric, 2021. "Catch me if you scan: Data-driven prescriptive modeling for smart store environments," European Journal of Operational Research, Elsevier, vol. 294(3), pages 860-873.
  26. Corredera, Alberto & Ruiz, Carlos, 2023. "Prescriptive selection of machine learning hyperparameters with applications in power markets: Retailer’s optimal trading," European Journal of Operational Research, Elsevier, vol. 306(1), pages 370-388.
  27. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
  28. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
  29. Dmitry B. Rokhlin, 2020. "Relative utility bounds for empirically optimal portfolios," Papers 2006.05204, arXiv.org.
  30. Max Biggs & Rim Hariss & Georgia Perakis, 2023. "Constrained optimization of objective functions determined from random forests," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 397-415, February.
  31. Shunichi Ohmori, 2021. "A Predictive Prescription Using Minimum Volume k -Nearest Neighbor Enclosing Ellipsoid and Robust Optimization," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
  32. 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).
  33. Shuaian Wang & Xuecheng Tian, 2023. "A Deficiency of the Predict-Then-Optimize Framework: Decreased Decision Quality with Increased Data Size," Mathematics, MDPI, vol. 11(15), pages 1-9, July.
  34. Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.
  35. Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2023. "Inventory control with modulated demand and a partially observed modulation process," Annals of Operations Research, Springer, vol. 321(1), pages 343-369, February.
  36. Wei Zhang & Kai Wang & Alexandre Jacquillat & Shuaian Wang, 2023. "Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 886-908, July.
  37. Wang, Shuaian & Yan, Ran, 2023. "Fundamental challenge and solution methods in prescriptive analytics for freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
  38. Notz, Pascal M. & Wolf, Peter K. & Pibernik, Richard, 2023. "Prescriptive analytics for a multi-shift staffing problem," European Journal of Operational Research, Elsevier, vol. 305(2), pages 887-901.
  39. Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2022. "Optimal day-ahead offering strategy for large producers based on market price response learning," DES - Working Papers. Statistics and Econometrics. WS 34605, Universidad Carlos III de Madrid. Departamento de Estadística.
  40. Yunxiao Deng & Suvrajeet Sen, 2022. "Predictive stochastic programming," Computational Management Science, Springer, vol. 19(1), pages 65-98, January.
  41. Chuah, Stephanie Hui-Wen & Yu, Joanne, 2021. "The future of service: The power of emotion in human-robot interaction," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
  42. Toon Vanderschueren & Robert Boute & Tim Verdonck & Bart Baesens & Wouter Verbeke, 2022. "Prescriptive maintenance with causal machine learning," Papers 2206.01562, arXiv.org.
  43. Pascal M. Notz & Richard Pibernik, 2022. "Prescriptive Analytics for Flexible Capacity Management," Management Science, INFORMS, vol. 68(3), pages 1756-1775, March.
  44. Talia Gillis & Bryce McLaughlin & Jann Spiess, 2021. "On the Fairness of Machine-Assisted Human Decisions," Papers 2110.15310, arXiv.org, revised Sep 2023.
  45. 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.
  46. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2023. "Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 158-177, January.
  47. Muñoz, M.A. & Pineda, S. & Morales, J.M., 2022. "A bilevel framework for decision-making under uncertainty with contextual information," Omega, Elsevier, vol. 108(C).
  48. Corredera, Alberto & Ruiz Mora, Carlos, 2022. "Prescriptive selection of machine learning hyperparameters with applications in power markets: retailer's optimal trading," DES - Working Papers. Statistics and Econometrics. WS 33693, Universidad Carlos III de Madrid. Departamento de Estadística.
  49. van der Laan, Niels & Teunter, Ruud H. & Romeijnders, Ward & Kilic, Onur A., 2022. "The data-driven newsvendor problem: Achieving on-target service-levels using distributionally robust chance-constrained optimization," International Journal of Production Economics, Elsevier, vol. 249(C).
  50. Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
  51. Yinchu Zhu & Ilya O. Ryzhov, 2022. "Optimal data-driven hiring with equity for underrepresented groups," Papers 2206.09300, arXiv.org.
  52. Wang, Shixuan & Syntetos, Aris A. & Liu, Ying & Di Cairano-Gilfedder, Carla & Naim, Mohamed M., 2023. "Improving automotive garage operations by categorical forecasts using a large number of variables," European Journal of Operational Research, Elsevier, vol. 306(2), pages 893-908.
  53. Jun Li & Yizhe Huang & Yan‐Fu Li & Shuming Wang, 2023. "Redundancy allocation under state‐dependent distributional uncertainty of component lifetimes," Production and Operations Management, Production and Operations Management Society, vol. 32(3), pages 930-950, March.
  54. Dmitry B. Rokhlin, 2021. "Relative utility bounds for empirically optimal portfolios," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(3), pages 437-462, June.
  55. Micus, Christian & Schramm, Simon & Boehm, Markus & Krcmar, Helmut, 2023. "Methods to analyze customer usage data in a product decision process:A systematic literature review," Operations Research Perspectives, Elsevier, vol. 10(C).
  56. Sebastian Kraul & Markus Seizinger & Jens O. Brunner, 2023. "Machine Learning–Supported Prediction of Dual Variables for the Cutting Stock Problem with an Application in Stabilized Column Generation," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 692-709, May.
  57. Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
  58. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
  59. Angel A. Juan & Peter Keenan & Rafael Martí & Seán McGarraghy & Javier Panadero & Paula Carroll & Diego Oliva, 2023. "A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 831-861, January.
  60. Kobayashi, Ken & Takano, Yuichi & Nakata, Kazuhide, 2023. "Cardinality-constrained distributionally robust portfolio optimization," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1173-1182.
  61. Soeffker, Ninja & Ulmer, Marlin W. & Mattfeld, Dirk C., 2022. "Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review," European Journal of Operational Research, Elsevier, vol. 298(3), pages 801-820.
  62. Meng Qi & Yuanyuan Shi & Yongzhi Qi & Chenxin Ma & Rong Yuan & Di Wu & Zuo-Jun (Max) Shen, 2023. "A Practical End-to-End Inventory Management Model with Deep Learning," Management Science, INFORMS, vol. 69(2), pages 759-773, February.
  63. Robert Engel & Pablo Fernandez & Antonio Ruiz-Cortes & Aly Megahed & Juan Ojeda-Perez, 2022. "SLA-aware operational efficiency in AI-enabled service chains: challenges ahead," Information Systems and e-Business Management, Springer, vol. 20(1), pages 199-221, March.
  64. Andrew Butler & Roy H. Kwon, 2021. "Data-driven integration of norm-penalized mean-variance portfolios," Papers 2112.07016, arXiv.org, revised Nov 2022.
  65. Adam N. Elmachtoub & Paul Grigas, 2022. "Smart “Predict, then Optimize”," Management Science, INFORMS, vol. 68(1), pages 9-26, January.
  66. Nathan Kallus & Xiaojie Mao, 2023. "Stochastic Optimization Forests," Management Science, INFORMS, vol. 69(4), pages 1975-1994, April.
  67. 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.
  68. Christian Mandl & Selvaprabu Nadarajah & Stefan Minner & Srinagesh Gavirneni, 2022. "Data‐driven storage operations: Cross‐commodity backtest and structured policies," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2438-2456, June.
  69. Yan Gu & Jianlin Jiang & Shun Zhang, 2023. "Distributionally robust Weber problem with uncertain demand," Computational Optimization and Applications, Springer, vol. 85(3), pages 705-752, July.
  70. Qi Feng & J. George Shanthikumar, 2022. "Developing operations management data analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4544-4557, December.
  71. Goerigk, Marc & Hartisch, Michael, 2023. "A framework for inherently interpretable optimization models," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1312-1324.
  72. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
  73. Nathan Kallus & Madeleine Udell, 2020. "Dynamic Assortment Personalization in High Dimensions," Operations Research, INFORMS, vol. 68(4), pages 1020-1037, July.
  74. Andrew Butler & Roy H. Kwon, 2021. "Integrating prediction in mean-variance portfolio optimization," Papers 2102.09287, arXiv.org, revised Nov 2022.
  75. David Bergman & Teng Huang & Philip Brooks & Andrea Lodi & Arvind U. Raghunathan, 2022. "JANOS: An Integrated Predictive and Prescriptive Modeling Framework," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 807-816, March.
  76. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.