Using Neural Networks to Guide Data-Driven Operational Decisions

Published Online:https://doi.org/10.1287/mnsc.2023.04141

References

  • Amazon (2023) Amazon selling stats. Accessed October 16, 2023, https://sell.amazon.com/blog/amazon-stats.Google Scholar
  • Amos B, Xu L, Kolter JZ (2017) Input convex neural networks. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 70 (PMLR, New York), 146–155.Google Scholar
  • Anderson R, Huchette J, Ma W, Tjandraatmadja C, Vielma JP (2020) Strong mixed-integer programming formulations for trained neural networks. Math. Programming 183(1):3–39.CrossrefGoogle Scholar
  • Aouad A, Desir A (2025) Representing random utility choice models with neural networks. Management Sci., ePub ahead of print November 17, https://doi.org/10.1287/mnsc.2023.02189.Google Scholar
  • Athey S, Wager S (2021) Policy learning with observational data. Econometrica 89(1):133–161.CrossrefGoogle Scholar
  • Babier A, Chan TCY, Diamant A, Mahmood R (2024) Learning to optimize contextually constrained problems for real-time decision generation. Management Sci. 71(2):1165–1186. LinkGoogle Scholar
  • Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Bartlett PL, Foster DJ, Telgarsky MJ (2017) Spectrally-normalized margin bounds for neural networks. Guyon I, von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan SVN, Garnett R, eds. Proc. 31st Annual Conf. Neural Inform. Processing Systems (NIPS 2017) (Curran Associates, Inc., Red Hook, NY), 6240–6249.Google Scholar
  • Bartlett PL, Montanari A, Rakhlin A (2021) Deep learning: A statistical viewpoint. Acta Numerica 30:87–201.CrossrefGoogle Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Bertsimas D, Koduri N (2022) Data-driven optimization: A reproducing kernel Hilbert space approach. Oper. Res. 70(1):454–471.LinkGoogle Scholar
  • Bertsimas D, Van Parys B (2022) Bootstrap robust prescriptive analytics. Math. Programming 195(1–2):39–78.Google Scholar
  • Bibaut A, Kallus N, Dimakopoulou M, Chambaz A, van Der Laan M (2021) Risk minimization from adaptively collected data: Guarantees for supervised and policy learning. Ranzato M, Beygelzimer A, Dauphin YN, Liang P, Wortman Vaughan J, eds. Adv. Neural Inform. Processing Systems, vol. 34 (Curran Associates, Inc., Red Hook, NY), 19261–19273.Google Scholar
  • Bierlaire M (2003) Biogeme: A free package for the estimation of discrete choice models. Proc. 3rd Swiss Transport Res. Conf. (Monte Verità, Ascona, Switzerland).Google Scholar
  • Biggs M (2026) Convex surrogate loss functions for contextual pricing with transaction data. Management Sci. Forthcoming.Google Scholar
  • Biggs M, Gao R, Sun W (2023) Loss functions for discrete contextual pricing with observational data. Preprint, submitted November 18, https://arxiv.org/abs/2111.09933.Google Scholar
  • Botoeva E, Kouvaros P, Kronqvist J, Lomuscio A, Misener R (2020) Efficient verification of ReLU-based neural networks via dependency analysis. Proc. Thirty-Fourth AAAI Conf. Artificial Intelligence (AAAI-20) (AAAI Press, Palo Alto, CA), 3291–3299.Google Scholar
  • Byrd RH, Hribar ME, Nocedal J (1999) An interior point algorithm for large-scale nonlinear programming. SIAM J. Optim. 9(4):877–900.CrossrefGoogle Scholar
  • Cai Z, Wang H, Talluri K, Li X (2022) Deep learning for choice modeling. Preprint, submitted August 19, https://arxiv.org/abs/2208.09325.Google Scholar
  • Cao J (2025) Collaborative learning and decision-making on pricing and recommendation: A simple framework for planning. Management Sci., ePub ahead of print November 11, https://doi.org/10.1287/mnsc.2023.00320.LinkGoogle Scholar
  • Chen J, Jiang N (2022) Offline reinforcement learning under value and density-ratio realizability: The power of gaps. Cussens J, Zhang K, eds. Proc. Thirty-Eighth Conf. Uncertainty Artificial Intelligence, Proceedings of Machine Learning Research, vol. 180 (PMLR, New York), 378–388.Google Scholar
  • Chen N, Cire AA, Hu M, Lagzi S (2023) Model-free assortment pricing with transaction data. Management Sci. 69(10):5830–5847.LinkGoogle Scholar
  • Chen X, Owen Z, Pixton C, Simchi-Levi D (2022) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.LinkGoogle Scholar
  • Collier M, Llorens HU (2018) Deep contextual multi-armed bandits. Preprint, submitted July 25, https://arxiv.org/abs/1807.09809.Google Scholar
  • Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems 2(4):303–314.CrossrefGoogle Scholar
  • Dutta S, Jha S, Sankaranarayanan S, Tiwari A (2018) Output range analysis for deep feedforward neural networks. Dutle A, Muñoz CA, Narkawicz A, eds. NASA Formal Methods: 10th Internat. Sympos., Proc., Lecture Notes in Computer Science, vol. 10811 (Springer, Cham, Switzerland), 121–138.CrossrefGoogle Scholar
  • Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize”. Management Sci. 68(1):9–26.LinkGoogle Scholar
  • Fan J, Ma C, Zhong Y (2021) A selective overview of deep learning. Statist. Sci. 36(2):264–290.CrossrefGoogle Scholar
  • Farrell MH, Liang T, Misra S (2021) Deep neural networks for estimation and inference. Econometrica 89(1):181–213.CrossrefGoogle Scholar
  • Foster DJ, Gentile C, Mohri M, Zimmert J (2020) Adapting to misspecification in contextual bandits. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Adv. Neural Inform. Processing Systems, vol. 33 (Curran Associates, Inc., Red Hook, NY), 11478–11489.Google Scholar
  • Gabel S, Timoshenko A (2022) Product choice with large assortments: A scalable deep-learning model. Management Sci. 68(3):1808–1827.LinkGoogle Scholar
  • Gijsbrechts J, Boute RN, Van Mieghem JA, Zhang DJ (2022) Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems. Manufacturing Service Oper. Management 24(3):1349–1368.LinkGoogle Scholar
  • Goodfellow I, Bengio Y, Courville A (2016) Deep Learning (The MIT Press, Cambridge, MA).Google Scholar
  • Grimstad B, Andersson H (2019) ReLU networks as surrogate models in mixed-integer linear programs. Computers Chemical Engrg. 131:106580.CrossrefGoogle Scholar
  • Gühring I, Kutyniok G, Petersen P (2020) Error bounds for approximations with deep ReLU neural networks in w s, p norms. Anal. Appl. 18(5):803–859.CrossrefGoogle Scholar
  • Hardt M, Recht B, Singer Y (2016) Train faster, generalize better: Stability of stochastic gradient descent. Balcan MF, Weinberger KQ, eds. Proc. 33rd Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 48 (PMLR, New York), 1225–1234.Google Scholar
  • Hornik K, Stinchcombe M, White H (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks 3(5):551–560.CrossrefGoogle Scholar
  • Hornik K, Stinchcombe M, White H, Auer P (1994) Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives. Neural Comput. 6(6):1262–1275.CrossrefGoogle Scholar
  • Hu Y, Kallus N, Mao X (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):4236–4245.LinkGoogle Scholar
  • Hu Y, Kallus N, Uehara M (2025) Fast rates for the regret of offline reinforcement learning. Math. Oper. Res. 50(1):633–655.LinkGoogle Scholar
  • Huber J, Müller S, Fleischmann M, Stuckenschmidt H (2019) A data-driven newsvendor problem: From data to decision. Eur. J. Oper. Res. 278(3):904–915.CrossrefGoogle Scholar
  • Jiang Z, Li J (2025) Instrumenting while experimenting: An empirical method for competitive pricing at scale. Oper. Res. 73(5):2477–2495.LinkGoogle Scholar
  • Jiang Y, Neyshabur B, Mobahi H, Krishnan D, Bengio S (2019) Fantastic generalization measures and where to find them. Proc. 8th Internat. Conf. Learn. Representations (ICLR 2020) (Curran Associates, Inc., Red Hook, NY), 2821–2853.Google Scholar
  • Kallus N (2018) Balanced policy evaluation and learning. Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 31 (Curran Associates, Inc., Red Hook, NY), 8895–8906.Google Scholar
  • Kallus N, Mao X (2022) Stochastic optimization forests. Management Sci. 69(4):1975–1994.LinkGoogle Scholar
  • Kallus N, Zhou A (2018) Policy evaluation and optimization with continuous treatments. Storkey A, Perez-Cruz F, eds. Proc. Twenty-First Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 84 (PMLR, New York), 1243–1251.Google Scholar
  • Keskar NS, Socher R (2017) Improving generalization performance by switching from Adam to SGD. Preprint, submitted December 20, https://arxiv.org/abs/1712.07628.Google Scholar
  • Krishnamurthy A, Langford J, Slivkins A, Zhang C (2020) Contextual bandits with continuous actions: Smoothing, zooming, and adapting. J. Machine Learn. Res. 21(137):1–45.Google Scholar
  • Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks 6(6):861–867.CrossrefGoogle Scholar
  • Li Y (2017) Deep reinforcement learning: An overview. Preprint, submitted January 25, https://arxiv.org/abs/1701.07274.Google Scholar
  • Liu M, Qi M, Shen ZJM (2024) End-to-end deep learning for inventory management with fixed ordering cost and its theoretical analysis. Working paper, University of California, Berkeley, Berkeley.Google Scholar
  • Lu H, Pang G, Zhou Y (2016) G/GI/N (+ GI)) queues with service interruptions in the Halfin–Whitt regime. Math. Methods Oper. Res. 83:127–160.CrossrefGoogle Scholar
  • Mandelbaum A, Momčilović P (2012) Queues with many servers and impatient customers. Math. Oper. Res. 37(1):41–65.LinkGoogle Scholar
  • Mandelbaum A, Zeltyn S (2009) Staffing many-server queues with impatient customers: Constraint satisfaction in call centers. Oper. Res. 57(5):1189–1205.LinkGoogle Scholar
  • Mao Y, Wang Q, Qu Y, Jiang Y, Ji X (2024) Doubly mild generalization for offline reinforcement learning. Globerson A, Mackey L, Belgrave D, Fan A, Paquet U, Tomczak J, Zhang C, eds. Adv. Neural Inform. Processing Systems, vol. 37 (Curran Associates, Inc., Red Hook, NY), 51436–51473.Google Scholar
  • Marković D, Stojić H, Schwöbel S, Kiebel SJ (2021) An empirical evaluation of active inference in multi-armed bandits. Neural Networks 144:229–246.CrossrefGoogle Scholar
  • Neghab DP, Khayyati S, Karaesmen F (2022) An integrated data-driven method using deep learning for a newsvendor problem with unobservable features. Eur. J. Oper. Res. 302(2):482–496.CrossrefGoogle Scholar
  • Neyshabur B, Bhojanapalli S, Srebro N (2018) A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks. Internat. Conf. Learn. Representations (ICLR 2018) (OpenReview).Google Scholar
  • Ohn I, Kim Y (2019) Smooth function approximation by deep neural networks with general activation functions. Entropy 21(7):627.CrossrefGoogle Scholar
  • Oroojlooyjadid A, Snyder LV, Takáč M (2020) Applying deep learning to the newsvendor problem. IISE Trans. 52(4):444–463.CrossrefGoogle Scholar
  • Oroojlooyjadid A, Nazari M, Snyder LV, Takáč M (2022) A deep q-network for the beer game: Deep reinforcement learning for inventory optimization. Manufacturing Service Oper. Management 24(1):285–304.LinkGoogle Scholar
  • Perakis G, Tsiourvas A (2022) Optimizing objective functions from ReLU neural networks in revenue management applications. Presented at the 21st INFORMS Revenue Management and Pricing Conference, virtual, June 20–22, 2022.Google Scholar
  • PYMNTS (2023) Instacart partners with Sharebite to target corporate food spend. Accessed August 1, 2024, https://www.pymnts.com/news/delivery/2023/instacart-partners-with-sharebite-to-target-corporate-food-spend/.Google Scholar
  • Qi M, Shi Y, Qi Y, Ma C, Yuan R, Wu D, Shen ZJM (2023) A practical end-to-end inventory management model with deep learning. Management Sci. 69(2):759–773.Google Scholar
  • Sarraf A (2020) A tight upper bound on the generalization error of feedforward neural networks. Neural Networks 127:1–6.CrossrefGoogle Scholar
  • Seubert F, Stein N, Taigel F, Winkelmann A (2020) Making the newsvendor smart–order quantity optimization with ANNs for a bakery chain. Anderson BB, Thatcher J, Meservy RD, Chudoba K, Fadel KJ, Brown SA, eds. Proc. 26th Americas Conf. Inform. Systems (AMCIS 2020), Virtual Conf. (Association for Information Systems, Atlanta), 1–10.Google Scholar
  • Shen M, Tang CS, Wu D, Yuan R, Zhou W (2020) JD.com: Transaction-level data for the 2020 MSOM Data Driven Research Challenge. Manufacturing Service Oper. Management 26(1):2–10.LinkGoogle Scholar
  • Shi C, Zhang S, Lu W, Song R (2022) Statistical inference of the value function for reinforcement learning in infinite-horizon settings. J. Roy. Statist. Soc. Ser. B Statist. Methodology 84(3):765–793.CrossrefGoogle Scholar
  • Simchi-Levi D, Xu Y (2022) Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability. Math. Oper. Res. 47(3):1904–1931.LinkGoogle Scholar
  • Sivaprasad S, Singh A, Manwani N, Gandhi V (2021) The curious case of convex neural networks. Oliver N, Pérez-Cruz F, Kramer S, Read J, Lozano JA, eds. Machine Learn. Knowledge Discovery Databases. Research Track: Eur. Conf., ECML PKDD 2021, Proc., Part I (Springer, Cham, Switzerland), 738–754.Google Scholar
  • Sondhi A, Arbour D, Dimmery D (2020) Balanced off-policy evaluation in general action spaces. Chiappa S, Calandra R, eds. Proc. Twenty Third Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 108 (PMLR, New York), 2413–2423.Google Scholar
  • Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. Dasgupta S, McAllester D, eds. Proc. 30th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 28 (PMLR, New York), 1139–1147.Google Scholar
  • Swaminathan A, Krishnamurthy A, Agarwal A, Dudik M, Langford J, Jose D, Zitouni I (2017) Off-policy evaluation for slate recommendation. Guyon I, von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan SVN, Garnett R, eds. 31st Annual Conf. Neural Inform. Processing Systems (NIPS 2017) (Curran Associates, Inc., Red Hook, NY), 3632–3642.Google Scholar
  • Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. Bengio Y, LeCun Y, eds. 2nd Internat. Conf. Learn. Representations (ICLR 2014) (Banff, Canada), Conf. Track Proc. Google Scholar
  • Tsay C, Kronqvist J, Thebelt A, Misener R (2021) Partition-based formulations for mixed-integer optimization of trained ReLU neural networks. Ranzato MA, Beygelzimer A, Dauphin YN, Liang P, Wortman Vaughan J, eds. Adv. Neural Inform. Processing Systems (NeurIPS 2021), vol. 34 (Curran Associates, Inc., Red Hook, NY), 3068–3080.Google Scholar
  • U.S. Census Bureau (2021) Income and poverty in the United States: 2020. Accessed August 1, 2022, https://www.census.gov/library/publications/2021/demo/p60-273.html.Google Scholar
  • Uehara M, Kiyohara H, Bennett A, Chernozhukov V, Jiang N, Kallus N, Shi C, Sun W (2023) Future-dependent value-based off-policy evaluation in POMDPs. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. Adv. Neural Inform. Processing Systems (NeurIPS 2023), vol. 36 (Curran Associates, Inc., Red Hook, NY), 15991–16008.Google Scholar
  • Valle-Pérez G, Louis AA (2020) Generalization bounds for deep learning. Preprint, submitted December 7, https://arxiv.org/abs/2012.04115.Google Scholar
  • Wang X, Kadıoğlu S (2023) Modeling uncertainty to improve personalized recommendations via bayesian deep learning. Internat. J. Data Sci. Anal. 16(2):191–201.CrossrefGoogle Scholar
  • Whitt W (2004) A diffusion approximation for the G/GI/n/m queue. Oper. Res. 52(6):922–941.LinkGoogle Scholar
  • Wilson AC, Roelofs R, Stern M, Srebro N, Recht B (2017) The marginal value of adaptive gradient methods in machine learning. Guyon I, von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan SVN, Garnett R, eds. Adv. Neural Inform. Processing Systems (NIPS 2017), vol. 30 (Curran Associates, Inc., Red Hook, NY), 4148–4158.Google Scholar
  • Wu G, Say B, Sanner S (2020) Scalable planning with deep neural network learned transition models. J. Artificial Intelligence Res. 68:571–606.CrossrefGoogle Scholar
  • Xu Y, Zeevi A (2020) Upper counterfactual confidence bounds: A new optimism principle for contextual bandits. Preprint, submitted July 15, https://arxiv.org/abs/2007.07876.Google Scholar
  • Xu P, Wen Z, Zhao H, Gu Q (2022) Neural contextual bandits with deep representation and shallow exploration. Tenth Internat. Conf. Learn. Representations (ICLR 2022), Virtual Event (OpenReview.net).Google Scholar
  • Yarotsky D (2017) Error bounds for approximations with deep ReLU networks. Neural Networks 94:103–114.CrossrefGoogle Scholar
  • Zeltyn S, Mandelbaum A (2005) Call centers with impatient customers: Many-server asymptotics of the M/M/n+ G queue. Queueing Systems 51:361–402.CrossrefGoogle Scholar
  • Zhan R, Ren Z, Athey S, Zhou Z (2024) Policy learning with adaptively collected data. Management Sci. 70(8):5270–5297.LinkGoogle Scholar
  • Zhu Y, Mineiro P (2022) Contextual bandits with smooth regret: Efficient learning in continuous action spaces. Chaudhuri K, Jegelka S, Song L, Szepesvári C, Niu G, Sabato S, eds. Proc. 39th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 162 (PMLR, New York), 27574–27590.Google Scholar
  • Zhu Y, Foster DJ, Langford J, Mineiro P (2022) Contextual bandits with large action spaces: Made practical. Chaudhuri K, Jegelka S, Song L, Szepesvári C, Niu G, Sabato S, eds. Proc. 39th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 162 (PMLR, New York), 27428–27453.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.