Smart “Predict, then Optimize”

Published Online:https://doi.org/10.1287/mnsc.2020.3922

References

  • Ahuja RK, Magnanti TL, Orlin JB (1993) Network Flows: Theory, Algorithms, and Applications (Pearson, Upper Saddle River, NJ).Google Scholar
  • Angalakudati M, Balwani S, Calzada J, Chatterjee B, Perakis G, Raad N, Uichanco J (2014) Business analytics for flexible resource allocation under random emergencies. Management Sci. 60(6):1552–1573.LinkGoogle Scholar
  • Aswani A, Shen Z-J, Siddiq A (2018) Inverse optimization with noisy data. Oper. Res. 66(3):870–892.LinkGoogle Scholar
  • Balkanski E, Rubinstein A, Singer Y (2016) The power of optimization from samples. Adv. Neural Inform. Processing Systems 29:4017–4025.Google Scholar
  • Balkanski E, Rubinstein A, Singer Y (2017) The limitations of optimization from samples. Proc. 49th Annu. ACM SIGACT Sympos. Theory Comput. (Association for Computing Machinery, New York), 1016–1027.CrossrefGoogle 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, El Karoui N, Lim AEB (2018) Machine learning and portfolio optimization. Management Sci. 64(3):1136–1154.LinkGoogle Scholar
  • Bartlett PL, Jordan MI, McAuliffe JD (2006) Convexity, classification, and risk bounds. J. Amer. Statist. Assoc. 101(473):138–156.CrossrefGoogle Scholar
  • Ben-David S, Eiron N, Long PM (2003) On the difficulty of approximately maximizing agreements. J. Comput. System Sci. 66(3):496–514.CrossrefGoogle Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Bertsimas D, Thiele A (2006) Robust and data-driven optimization: modern decision making under uncertainty. Johnson MP, Norman B, Secomandi N, eds. Models, Methods, and Applications for Innovative Decision Making, INFORMS TutORials in Operations Research (INFORMS, Catonsville, MD), 95–122.Google Scholar
  • Bertsimas D, Gupta V, Kallus N (2018a) Data-driven robust optimization. Math. Programming 167(2):235–292.CrossrefGoogle Scholar
  • Bertsimas D, Gupta V, Kallus N (2018b) Robust sample average approximation. Math. Programming 171(1-2):217–282.CrossrefGoogle Scholar
  • Bertsimas D, Gupta V, Paschalidis IC (2015) Data-driven estimation in equilibrium using inverse optimization. Math. Programming 153(2):595–633.CrossrefGoogle Scholar
  • Besbes O, Gur Y, Zeevi A (2015) Optimization in online content recommendation services: Beyond click-through rates. Manufacturing Service Oper. Management 18(1):15–33.LinkGoogle Scholar
  • Besbes O, Phillips R, Zeevi A (2010) Testing the validity of a demand model: An operations perspective. Manufacturing Service Oper. Management 12(1):162–183.LinkGoogle Scholar
  • Chan CW, Farias VF, Bambos N, Escobar GJ (2012) Optimizing intensive care unit discharge decisions with patient readmissions. Oper. Res. 60(6):1323–1341.LinkGoogle Scholar
  • Chan CW, Green LV, Lu Y, Leahy N, Yurt R (2013) Prioritizing burn-injured patients during a disaster. Manufacturing Service Oper. Management 15(2):170–190.LinkGoogle Scholar
  • Chan TCY, Craig T, Lee T, Sharpe MB (2014) Generalized inverse multiobjective optimization with application to cancer therapy. Oper. Res. 62(3):680–695.LinkGoogle Scholar
  • Cheung M, Elmachtoub AN, Levi R, Shmoys DB (2016) The submodular joint replenishment problem. Math. Programming 158(1-2):207–233.CrossrefGoogle Scholar
  • Chu LY, Shanthikumar JG, Shen Z-JM (2008) Solving operational statistics via a Bayesian analysis. Oper. Res. Lett. 36(1):110–116.CrossrefGoogle Scholar
  • Cohen MC, Leung N-HZ, Panchamgam K, Perakis G, Smith A (2017) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.LinkGoogle Scholar
  • Den Boer AV, Sierag DD (2020) Decision-based model selection. Eur. J. Oper. Res. 290(2):671–686.Google Scholar
  • den Hertog D, Postek K (2016) Bridging the gap between predictive and prescriptive analytics-new optimization methodology needed. Preprint, submitted December 9, http://www.optimization-online.org/DB_HTML/2016/12/5779.html.Google Scholar
  • Deng Y, Liu J, Sen S (2018) Coalescing data and decision sciences for analytics. Gel E, Ntaimo L, eds. Recent Advances in Optimization and Modeling of Contemporary Problems, INFORMS TutORials in Operations Research (INFORMS, Catonsville, MD), 20–49.Google Scholar
  • Deo S, Rajaram K, Rath S, Karmarkar US, Goetz MB (2015) Planning for HIV screening, testing, and care at the Veterans Health Administration. Oper. Res. 63(2):287–304.LinkGoogle Scholar
  • Donti P, Amos B, Kolter JZ (2017) Task-based end-to-end model learning in stochastic optimization. Adv. Neural Inform. Processing Systems. 30:5484–5494.Google Scholar
  • Dunning I, Huchette J, Lubin M (2017) Jump: A modeling language for mathematical optimization. SIAM Rev. 59(2):295–320.CrossrefGoogle Scholar
  • El Balghiti O, Elmachtoub AN, Grigas P, Tewari A (2019) Generalization bounds in the predict-then-optimize framework. Adv. Neural Inform. Processing Systems 32:14412–14421.Google Scholar
  • Elmachtoub AN, Jason CNL, McNellis R (2020) Decision trees for decision-making under the predict-then-optimize framework. Proc. 37th Internat. Conf. Machine Learn. (PMLR), 2858–2867.Google Scholar
  • Esfahani PM, Shafieezadeh-Abadeh S, Grani A, Hanasusanto DK (2018) Data-driven inverse optimization with imperfect information. Math. Programming 167(1):191–234.CrossrefGoogle Scholar
  • Farias V (2007) Revenue management beyond estimate, then optimize. Unpublished doctoral thesis, Stanford University, Stanford, CA.Google Scholar
  • Ferreira KJ, Bin HAL, Simchi-Levi D (2015) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Gallien J, Mersereau AJ, Garro A, Mora AD, Vidal MN (2015) Initial shipment decisions for new products at Zara. Oper. Res. 63(2):269–286.LinkGoogle Scholar
  • Gupta V, Rusmevichientong P (2017) Small-data, large-scale linear optimization with uncertain objectives. Preprint, submitted October 31, https://dx.doi.org/10.2139/ssrn.3065655.Google Scholar
  • Hofmann T, Schölkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann. Statist. 36(3):1171–1220.CrossrefGoogle Scholar
  • Jaggi M (2011) Convex optimization without projection steps. Preprint, submitted August 4, https://arxiv.org/abs/1108.1170.Google Scholar
  • Kao Y-h, Roy BV, Yan X (2009) Directed regression. Adv. Neural Inform. Processing Systems 22:889–897.Google Scholar
  • Keshavarz A, Wang Y, Boyd S (2011) Imputing a convex objective function. 2011 IEEE Internat. Sympos. Intelligent Control (ISIC) (IEEE, Piscataway, NJ), 613–619.CrossrefGoogle Scholar
  • Kleywegt AJ, Shapiro A, Homem-de Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.CrossrefGoogle Scholar
  • Levi R, Roundy RO, Shmoys DB (2006) Primal-dual algorithms for deterministic inventory problems. Math. Oper. Res. 31(2):267–284.LinkGoogle Scholar
  • Lim AEB, Shanthikumar JG, Vahn G-Y (2012) Robust portfolio choice with learning in the framework of regret: Single-period case. Management Sci. 58(9):1732–1746.LinkGoogle Scholar
  • Lin Y (2004) A note on margin-based loss functions in classification. Statist. Probab. Lett. 68(1):73–82.CrossrefGoogle Scholar
  • Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.CrossrefGoogle Scholar
  • Markowitz H (1952) Portfolio selection. J. Finance 7(1):77–91.Google Scholar
  • Mehrotra M, Dawande M, Gavirneni S, Demirci M, Tayur S (2011) OR practice—production planning with patterns: A problem from processed food manufacturing. Oper. Res. 59(2):267–282.LinkGoogle Scholar
  • Mišić VV, Perakis G (2020) Data analytics in operations management: A review. Manufacturing Service Oper. Management 22(1):158–169.LinkGoogle Scholar
  • Nowozin S, Lampert CH (2011) Structured learning and prediction in computer vision. Foundations Trends Comput. Graphics Vision. 6(3–4):185–365.CrossrefGoogle Scholar
  • Osokin A, Bach F, Lacoste-Julien S (2017) On structured prediction theory with calibrated convex surrogate losses. Von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, eds. NIPS’17 Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 302–313.Google Scholar
  • Schütz P, Tomasgard A, Ahmed S (2009) Supply chain design under uncertainty using sample average approximation and dual decomposition. Eur. J. Oper. Res. 199(2):409–419.CrossrefGoogle Scholar
  • Sen S, Deng Y (2017) Learning enabled optimization: Toward a fusion of statistical learning and stochastic optimization. Preprint, submitted March 14, http://www.optimization-online.org/DB_HTML/2017/03/5904.html.Google Scholar
  • Simchi-Levi D (2013) OM forum—OM research: From problem-driven to data-driven research. Manufacturing Service Oper. Management 16(1):2–10.LinkGoogle Scholar
  • Steinwart I (2002) Support vector machines are universally consistent. J. Complexity 18(3):768–791.CrossrefGoogle Scholar
  • Taskar B, Guestrin C, Koller D (2004) Max-margin Markov networks. Adv. Neural Inform. Processing Systems 16:25–32.Google Scholar
  • Taskar B, Chatalbashev V, Koller D, Guestrin C (2005) Learning structured prediction models: A large margin approach. Proc. 22nd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 896–903.Google Scholar
  • Tewari A, Bartlett PL (2007) On the consistency of multiclass classification methods. J. Machine Learn. Res. 8(May):1007–1025.Google Scholar
  • Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J. Machine Learn. Res. 6(Sep):1453–1484.Google Scholar
  • Tulabandhula T, Rudin C (2013) Machine learning with operational costs. J. Machine Learn. Res. 14(1):1989–2028.Google Scholar
  • Wagner HM, Whitin TM (1958) Dynamic version of the economic lot size model. Management Sci. 5(1):89–96.LinkGoogle Scholar
  • Wang Z, Glynn PW, Ye Y (2016) Likelihood robust optimization for data-driven problems. Comput. Management Sci. 13(2):241–261.CrossrefGoogle Scholar
  • Zhang T (2004) Statistical analysis of some multi-category large margin classification methods. J. Machine Learn. Res. 5(Oct):1225–1251.Google Scholar
  • Zou H, Zhu J, Hastie T (2008) New multicategory boosting algorithms based on multicategory Fisher-consistent losses. Ann. Appl. Statist. 2(4):1290–1306.CrossrefGoogle 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.