Dynamic Assortment with Demand Learning for Seasonal Consumer Goods

References

• Adelman D., Mersereau A. J. Relaxations of weakly coupled stochastic dynamic programs. (2004) . Working paper, Graduate School of Business, The University of Chicago, Chicago, ILGoogle Scholar
• Agrawal R., Hedge M. V., Teneketzis D. Asymptotically efficient adaptive allocation rules for the multiarmed bandit problem with switching cost. IEEE Trans. Automatic Control (1988) 33(10):899–906Crossref, Google Scholar
• Anantharam V., Varaiya P., Walrand J. Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays—Part I: I.i.d. rewards. IEEE Trans. Automatic Control (1987) 32(11):968–976Crossref, Google Scholar
• Aviv Y., Pazgal A. Pricing of short life-cycle products through active learning. (2002) . Working paper, Washington University, St. Louis, MOGoogle Scholar
• Ben-Akiva M., Lerman S. R.Discrete Choice Analysis (1985) (MIT Press, Cambridge, MA) Google Scholar
• Berry D. A., Fristedt B.Bandit Problems, Sequential Allocation of Experiments (1985) (Chapman and Hall, New York) Crossref, Google Scholar
• Bertsekas D.Nonlinear Programming (1999) (Athena Scientific, Belmont, MA) Google Scholar
• Bertsekas D.Dynamic Programming and Optimal Control (2001) I and II(Athena Scientific, Belmont, MA) Google Scholar
• Bertsimas D., Mersereau A. A learning approach to customized marketing. (2004) . Working paper, The University of Chicago, Chicago, ILGoogle Scholar
• Brezzi M., Lai T. L. Optimal learning and experimentation in bandit problems. J. Econom. Dynam. Control (2002) 27:87–108Crossref, Google Scholar
• Bultez A., Naert P. SHARP: Shelf allocation for retailers profit. Marketing Sci. (1988) 7:211–231Link, Google Scholar
• Caro F. Dynamic retail assortment models with demand learning for seasonal consumer goods. (2005) . Ph.D. thesis, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
• Castañon D. A. Approximate dynamic programming for sensor management. Proc. 36th IEEE Conf. Decision and Control (1997) San Diego, CA:1202–1207Crossref, Google Scholar
• DeGroot M. H.Optimal Statistical Decisions (1970) (McGraw-Hill, New York) Google Scholar
• Fisher M. L., Raman A. Reducing the cost of demand uncertainty through accurate response to early sales. Oper. Res. (1996) 44(1):87–99Link, Google Scholar
• Fisher M. L., Raman A., McClelland A. S. Rocket science retailing is almost here—Are you ready. Harvard Bus. Rev. (2000) July–August):115–124Google Scholar
• Ghemawat P., Nueno J. L. ZARA: Fast fashion. (2003) . Harvard Business School Multimedia Case 9-703-416, Harvard University, Boston, MAGoogle Scholar
• Ginebra J., Clayton M. K. Response surface bandits. J. Roy. Statist. Soc. Series B (1995) 57:771–784Google Scholar
• Gittins J. C. Bandit processes and dynamic allocation indices. J. Roy. Statist. Soc. Series B (1979) 14:148–167Google Scholar
• Hardwick J., Oehmke R., Stout Q. F. New adaptive designs for delayed response models. J. Sequential Planning Inference (2006) 136:1940–1955Crossref, Google Scholar
• Hawkins J. T. A Lagrangian decomposition approach to weakly coupled dynamic optimization problems and its applications. (2003) . Ph.D. thesis, Operations Research Center, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
• Karmarkar U. S. The multilocation multiperiod inventory problem: Bounds and approximations. Management Sci. (1987) 33(1):86–94Link, Google Scholar
• Kök A. G., Fisher M. L. Demand estimation and assortment optimization under substitution: Methodology and application. (2004) . Working paper, Duke University, Durham, NCGoogle Scholar
• Lagarias J. C., Reeds J. A., Wright M. H., Wright P. E. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optim. (1998) 9(1):112–147Crossref, Google Scholar
• Mahajan S., van Ryzin G. Stocking retail assortments under dynamic consumer substitution. Oper. Res. (2001) 49:334–351Link, Google Scholar
• Presman E. L., Sonin I. N.Sequential Control with Incomplete Information: The Bayesian Approach to Multi-Armed Bandit Problems (1990) (Academic Press, San Diego, CA) Google Scholar
• Smith S. A., Agrawal N. Management of multi-item retail inventory systems with demand substitution. Oper. Res. (2000) 48:50–64Link, Google Scholar
• van Ryzin G., Mahajan S. On the relationship between inventory costs and variety benefits in retail assortments. Management Sci. (1999) 45:1496–1509Link, Google Scholar
• Weber R. R., Weiss G. On an index policy for restless bandits. J. Appl. Probab. (1990) 27:637–648Crossref, Google Scholar
• Whittle P., Gani J. Restless bandits: Activity allocation in a changing world. A Celebration of Applied Probability. J. Appl. Probab. (1988) 25A:287–298Crossref, Google Scholar