Data-Driven Decisions for Problems with an Unspecified Objective Function

Published Online:https://doi.org/10.1287/ijoc.2018.0818

References

  • Afèche P, Ata B (2013) Bayesian dynamic pricing in queueing systems with unknown delay cost characteristics. Manufacturing Service Oper. Management 15(2):292–304.LinkGoogle Scholar
  • ATKearney (2013) Big data and the creative destruction of today’s business models. Accessed January 11, 2016, http://www.atkearney.com/strategic-it/ideas-insights/article/-/asset_publisher/LCcgOeS4t85g/content/big-data-and-the-creative-destruction-of-today-s-business-models/10192.Google Scholar
  • Audibert JY, Bubeck S (2010) Regret bounds and minimax policies under partial monitoring. J. Machine Learn. Res. 11(October):2785–2810.Google Scholar
  • Audibert JY, Munos R, Szepesvári C (2009) Exploration-exploitation trade-off using variance estimates in multi-armed bandits. Theoret. Comput. Sci. 410(19):1876–1902.CrossrefGoogle Scholar
  • Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learn. 47(2):235–256.CrossrefGoogle Scholar
  • Ben-Tal A, Ghaoui LE, Nemirovski A (2009) Robust Optimization (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.CrossrefGoogle Scholar
  • Bertsimas D, Gupta V, Kallus N (2018) Data-driven robust optimization. Math. Programming 167(2):235–292.CrossrefGoogle Scholar
  • Chehrazi N, Weber TA (2010) Monotone approximation of decision problems. Oper. Res. 58(4):1158–1177.LinkGoogle Scholar
  • Chu LY, Shanthikumar JG, Shen ZJM (2008) Solving operational statistics via a bayesian analysis. Oper. Res. Lett. 36(1):110–116.CrossrefGoogle Scholar
  • Cybenko G (1989) Approximations by superpositions of sigmoidal functions. Math. Control, Signals, Systems 2(4):303–314.CrossrefGoogle Scholar
  • Dar Z (2014) The real promise of big data: It’s changing the whole way humans will solve problems. Accessed January 11, 2016, http://venturebeat.com/2014/02/09/the-real-promise-of-big-data-its-changing-the-whole-way-humans-will-solve-problems/.Google Scholar
  • Dupačová J (1966) On minimax solutions of stochastic linear programming problems. Časopis pro pěstováni matematiky 91(4):423–430.Google Scholar
  • Fast Company (2014) The world’s top 10 most innovative companies in big data. Accessed January 11, 2016, http://www.fastcompany.com/most-innovative-companies/2014/industry/big-data.Google Scholar
  • Flaxman AD, Kalai AT, McMahan HB (2005) Online convex optimization in the bandit setting: Gradient descent without a gradient. Proc. 16th Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 285–394.Google Scholar
  • Fu MC, ed. (2015) Handbook of Simulation Optimization (Springer, Secaucus, NJ).CrossrefGoogle Scholar
  • Fu MC, Glover FW, April J (2005) Simulation optimization: A review, new developments, and applications. Kuhl ME, Steiger NM, Armstrong FB, Joines JA, eds. Proc. 37th Winter Simulation Conf., Orlando, FL, 83–95.Google Scholar
  • Goh J, Sim M (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4):902–917.LinkGoogle Scholar
  • Hassoun MH (1995) Fundamentals of Artificial Neural Networks (MIT Press, Cambridge, MA).Google Scholar
  • Haykin S (1998) Neural Networks: A Comprehensive Foundation (Prentice Hall, Upper Saddle River, NJ).Google Scholar
  • Horst R, Pardalos PM, eds. (2002) Handbook of Global Optimization (Springer, Secaucus, NJ).Google Scholar
  • IBM (2013) The four V’s of big data. Accessed January 11, 2016, http://www.ibmbigdatahub.com/infographic/four-vs-big-data.Google Scholar
  • Kekre S, Secomandi N, Sönmez E, West K (2009) Balancing risk and efficiency at a major commercial bank. Manufacturing Service Oper. Management 11(1):160–173.LinkGoogle Scholar
  • Kuhn D, Wiesemann W, Georghiou A (2011) Primal and dual linear decision rules in stochastic and robust optimization. Math. Programming 130(1):177–209.CrossrefGoogle Scholar
  • Kvasov DE, Sergeyev YD (2013) Lipschitz global optimization methods in control problems. Automation Remote Control 74(9):1435–1448.CrossrefGoogle Scholar
  • Li X, Natarajan K, Teo CP, Zheng Z (2013) Distributionally robust mixed integer linear programs: Persistency models with applications. Eur. J. Oper. Res. 233(3):459–473.CrossrefGoogle Scholar
  • Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.CrossrefGoogle Scholar
  • Mak HY, Shen ZJM (2014) Pooling and dependence of demand and yield in multiple-location inventory systems. Manufacturing Service Oper. Management 16(2):263–269.LinkGoogle Scholar
  • McAfee A, Brynjolfsson E (2012) Big data: The management revolution. Harvard Bus. Rev. 90(October):60–68.Google Scholar
  • Mersereau AJ (2015) Demand estimation from censored observations with inventory record inaccuracy. Manufacturing Service Oper. Management 17(3):335–349.LinkGoogle Scholar
  • Mockus J (2012) Bayesian Approach to Global Optimization: Theory and Application (Springer, Secaucus, NJ).Google Scholar
  • Scarf H (1958) A min-max solution of an inventory problem. Studies in the Mathematical Theory of Inventory and Production (Stanford University Press, Palo Alto, CA), 201–209.Google Scholar
  • Srinivas N, Krause A, Kakade SM, Seeger M (2010) Gaussian process optimization in the bandit setting: No regret and experimental design. Rand Corporation Paper Series, Rand Corporation, Santa Monica, CA.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.