Data Analytics in Operations Management: A Review

Published Online:https://doi.org/10.1287/msom.2019.0805

References

  • Acimovic J, Graves SC (2014) Making better fulfillment decisions on the fly in an online retail environment. Manufacturing Service Oper. Management 17(1):34–51.LinkGoogle Scholar
  • Ahuja RK, Orlin JB (2001) Inverse optimization. Oper. Res. 49(5):771–783.LinkGoogle Scholar
  • Alley M, Biggs M, Hariss R, Herman C, Li M, Perakis G (2019) Pricing for heterogeneous products: Analytics for ticket reselling. Working paper, Massachusetts Institute of Technology, Cambridge. Google Scholar
  • Ang E, Kwasnick S, Bayati M, Plambeck EL, Aratow M (2015) Accurate emergency department wait time prediction. Manufacturing Service Oper. Management 18(1):141–156.LinkGoogle Scholar
  • Angrist JD, Pischke J-S (2008) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Aouad A, Farias VF, Levi R (2015) Assortment optimization under consider-then-choose choice models. Working paper, London Business School, London. Google Scholar
  • Aouad A, Farias V, Levi R, Segev D (2018) The approximability of assortment optimization under ranking preferences. Oper. Res. 66(6):1661–1669.LinkGoogle Scholar
  • Aswani A, Shen Z-JM, Siddiq A (2019) Data-driven incentive design in the Medicare shared savings program. Oper. Res. 67(4):1002–1026.Google Scholar
  • Avrahami A, Herer YT, Levi R (2014) Matching supply and demand: Delayed two-phase distribution at Yedioth Group—models, algorithms, and information technology. Interfaces 44(5):445–460.LinkGoogle Scholar
  • Baardman L, Levin I, Perakis G, Singhvi D (2018a) Leveraging comparables for new product sales forecasting. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Baardman L, Borjian Boroujeni S, Cohen-Hillel T, Panchamgam K, Perakis G (2018b) Detecting customer trends for optimal promotion targeting. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Baardman L, Cohen M, Panchamgam K, Perakis G, Segev D (2018c) Scheduling promotion vehicles to boost profits. Management Sci. 65(1):50–70.LinkGoogle Scholar
  • Ban G-Y, Keskin NB (2017) Personalized dynamic pricing with machine learning. Working paper, London Business School, London.Google Scholar
  • Ban G-Y, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Ban G-Y, Gallien J, Mersereau AJ (2018) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management, ePub ahead of print December 10, https://doi.org/10.1287/msom.2018.0725.LinkGoogle Scholar
  • Bandi C, Moreno A, Ngwe D, Xu Z (2018) Opportunistic returns and dynamic pricing: Empirical evidence from online retailing in emerging markets. Harvard Business School Working Paper 19-030, Harvard University, Boston.Google Scholar
  • Bastani H, Bayati M (2015) Online decision-making with high-dimensional covariates. Working paper, University of Pennsylvania, Philadelphia.Google Scholar
  • Bernstein F, Modaresi S, Sauré D (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.AbstractGoogle Scholar
  • Bertsimas D, Kallus N (2016) The power and limits of predictive approaches to observational-data-driven optimization. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Bertsimas D, Kallus N (2019) From predictive to prescriptive analytics. Management Sci., ePub ahead of print August 23, https://doi.org/10.1287/mnsc.2018.3253. Google Scholar
  • Bertsimas D, Mišić VV (2019) Exact first-choice product line optimization. Oper. Res. 67(3):651–670.Google Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.CrossrefGoogle Scholar
  • Bertsimas D, Farias VF, Trichakis N (2013) Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Oper. Res. 61(1):73–87.LinkGoogle Scholar
  • Bertsimas D, O’Hair A, Relyea S, Silberholz J (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.LinkGoogle Scholar
  • Bertsimas D, Kallus N, Weinstein AM, Zhuo YD (2017) Personalized diabetes management using electronic medical records. Diabetes Care 40(2):210–217.CrossrefGoogle Scholar
  • Blanchet J, Gallego G, Goyal V (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.LinkGoogle Scholar
  • Bravo F, Shaposhnik Y (2018) Mining optimal policies: A pattern recognition approach to model analysis. Working paper, University of California, Los Angeles, Los Angeles. Google Scholar
  • Bravo F, Braun M, Farias V, Levi R, Lynch C, Tumolo J, Whyte R (2019) Optimization-driven framework to understand healthcare network costs and resource allocation. Working paper, University of California, Los Angeles, Los Angeles.Google Scholar
  • Breiman L (1996) Bagging predictors. Machine Learn. 24(2):123–140.CrossrefGoogle Scholar
  • Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.CrossrefGoogle Scholar
  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees (CRC Press, Boca Raton, FL).Google Scholar
  • Chaurasia M, Pandey S, Perakis G, Rathore HS, Singhvi D, Spanditakis Y (2019) First delivery gaps: A supply chain level to reduce product returns in online retail. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Chen X, Owen Z, Pixton C, Simchi-Levi D (2015) A statistical learning approach to personalization in revenue management. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Ciocan DF, Mišić VV (2018) Interpretable optimal stopping. Working paper, INSEAD, Fontainebleau, France. Google Scholar
  • Cohen M, Lobel I, Paes Leme R (2016) Feature-based dynamic pricing. Working paper, New York University, New York.Google Scholar
  • Cohen MC, Kalas J, Perakis G (2017a) Optimizing promotions for multiple items in supermarkets. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Cohen MC, Leung N-HZ, Panchamgam K, Perakis G, Smith A (2017b) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.LinkGoogle Scholar
  • Cohen-Hillel T, Panchamgam K, Perakis G (2019a) High-low promotion policies for peak-end demand models. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Cohen-Hillel T, Panchamgam K, Perakis G (2019b) Bounded memory peak end models can be surprisingly good. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.LinkGoogle Scholar
  • Doshi-Velez F, Kim B (2017) Toward a rigorous science of interpretable machine learning. Working paper, Harvard University, Cambridge.Google Scholar
  • Elmachtoub AN, Grigas P (2017) Smart “predict, then optimize.” Working paper, Columbia University, New York.Google Scholar
  • Ettl M, Harsha P, Papush A, Perakis G (2019) A data-driven approach to personalized bundle pricing and recommendation. Manufacturing Service Oper. Management, ePub ahead of print July 19, https://doi.org/10.1287/msom.2018.0756.Google Scholar
  • Farias VF, Li AA (2019) Learning preferences with side information. Management Sci. 65(7):3131–3149.LinkGoogle Scholar
  • Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.LinkGoogle Scholar
  • Feldman J, Paul A, Topaloglu H (2019) Assortment optimization with small consideration sets. Oper. Res. Forthcoming.Google Scholar
  • Ferreira KJ, Lee BHA, Simchi-Levi D (2015) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Fisher M, Gallino S, Xu J (2016) The value of rapid delivery in online retailing. Working paper, University of Pennsylvania, Philadelphia.Google Scholar
  • Freitas AA (2014) Comprehensible classification models: A position paper. ACM SIGKDD Explorations Newslett. 15(1):1–10.CrossrefGoogle Scholar
  • Glaeser CK, Fisher M, Su X (2018) Optimal retail location: Empirical methodology and application to practice. Manufacturing Service Oper. Management 21(1):86–102.Google Scholar
  • Golrezaei N, Nazerzadeh H, Rusmevichientong P (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.LinkGoogle Scholar
  • Gupta V, Rusmevichientong P (2017) Small-data, large-scale linear optimization with uncertain objectives. Working paper, University of Southern California, Los Angeles.Google Scholar
  • Gupta V, Han BR, Kim S-H, Paek H (2017) Maximizing intervention effectiveness. Working paper, University of Southern California, Los Angeles.Google Scholar
  • Harsha P, Subramanian S, Uichanco J (2019) Dynamic pricing of omnichannel inventories. Manufacturing Service Oper. Management 21(1):47–65.LinkGoogle Scholar
  • Hawkins JT (2003) A Lagrangian decomposition approach to weakly coupled dynamic optimization problems and its applications. Unpublished PhD thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • He L, Mak H-Y, Rong Y, Shen Z-JM (2017) Service region design for urban electric vehicle sharing systems. Manufacturing Service Oper. Management 19(2):309–327.LinkGoogle Scholar
  • Ho T-H, Lim N, Reza S, Xia X (2017) OM Forum: Causal inference models in operations management. Manufacturing Service Oper. Management 19(4):509–525.LinkGoogle Scholar
  • Javanmard A, Nazerzadeh H (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(9):1–49.Google Scholar
  • Mahmoudi M, Caracciolo G, Safavi-Sohi R, Poustchi H, Li AA, Vasighi M, Chiozzi RZ, et al.. (2017) Multi-nanoparticle protein corona characterization of human plasma and machine learning enable accurate identification and discrimination of cancers at early stages. Working paper, Harvard Medical School, Cambridge, MA.Google Scholar
  • Rath S, Rajaram K (2018) Staff planning for hospitals with cost estimation and optimization. Working paper, University of North Carolina at Chapel Hill, Chapel Hill.Google Scholar
  • Rath S, Rajaram K, Mahajan A (2017) Integrated anesthesiologist and room scheduling for surgeries: Methodology and application. Oper. Res. 65(6):1460–1478.LinkGoogle Scholar
  • Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B. 58(1):267–288.CrossrefGoogle Scholar
  • Vielma JP (2015) Mixed integer linear programming formulation techniques. SIAM Rev. 57(1):3–57.CrossrefGoogle Scholar
  • Zenteno AC, Carnes T, Levi R, Daily BJ, Dunn PF, Systematic OR (2016) Block allocation at a large academic medical center. Ann. Surgery 264(6):973–981.CrossrefGoogle Scholar
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