Dynamic Procurement of New Products with Covariate Information: The Residual Tree Method

References

• Agrawal N, Smith SA, Tsay AA (2002) Multi-vendor sourcing in a retail supply chain. Production Oper. Management 11(2):157–182.Crossref, Google Scholar
• Allon G, Van Mieghem JA (2010) Global dual sourcing: Tailored base-surge allocation to near- and offshore production. Management Sci. 56(1):110–124.Link, Google Scholar
• Baardman L, Levin I, Perakis G, Singhvi D (2017) Leveraging comparables for new product sales forecasting. Working paper, Massachusetts Institute of Technology, SSRN: http://dx.doi.org/10.213.Crossref, Google Scholar
• Ban GY (2018) Confidence intervals for data-driven inventory policies with demand censoring. Working paper, London Business School, SSRN: http://dx.doi.org/10.2139/ssrn.2654014.Google Scholar
• Ban GY, Keskin NB (2017) Personalized dynamic pricing with machine learning. Working paper, London Business School, SSRN: http://dx.doi.org/10.2139/ssrn.2972985.Google Scholar
• Ban GY, Rudin C (2018) The big data newsvendor: Practical insights from machine learning. Oper. Res. Working paper, London Business School, SSRN: http://dx.doi.org/10.2139/ssrn.2559116.Link, Google Scholar
• Bertsimas D, Weismantel R (2005) Optimization Over Integers (Dynamic Ideas, Belmont, MA).Google Scholar
• Burnetas A, Gilbert S (2001) Future capacity procurements under unknown demand and increasing costs. Management Sci. 47(7):979–992.Link, Google Scholar
• Burnetas AN, Smith CE (2000) Adaptive ordering and pricing for perishable products. Oper. Res. 48(3):436–443.Link, Google Scholar
• Casey MS, Sen S (2005) The scenario generation algorithm for multistage stochastic linear programming. Math. Oper. Res. 30(3):615–631.Link, Google Scholar
• Chen X, Owen Z, Pixton C, Simchi-Levi D (2015) A statistical learning approach to personalization in revenue management. Working paper, New York University, SSRN: http://dx.doi.org/10.2139/ssrn.2579462.Google Scholar
• Cohen MC, Lobel I, Paes Leme R (2016) Feature-based dynamic pricing. Working paper, New York University, http://dx.doi.org/10.2139/ssrn.2737045.Google Scholar
• Dupačová J, Consigli G, Wallace SW (2000) Scenarios for multistage stochastic programs. Ann. Oper. Res. 100(1–4):25–53.Crossref, Google Scholar
• Dupačová J, Gröwe-Kuska N, Römisch W (2003) Scenario reduction in stochastic programming. Math. Programming, Ser. A 95(3):493–511.Crossref, Google Scholar
• Eppen GD, Iyer AV (1997) Improved fashion buying with Bayesian updates. Oper. Res. 45(6):805–819.Link, Google Scholar
• Escudero LF, Kamesam PV, King AJ, Wets RJ-B (1993) Production planning via scenario modelling. Ann. Oper. Res. 43(6):309–335.Crossref, Google Scholar
• Ferreira KJ, Alex Lee BH, Simchi-Levi D (2015) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
• Fisher M, Raman A (1996) Reducing the cost of demand uncertainty through accurate response to early sales. Oper. Res. 44(1):87–99.Link, Google Scholar
• Fisher M, Rajaram K, Raman A (2001) Optimizing inventory replenishment of retail fashion products. Manufacturing Service Oper. Management 3(3):230–241.Link, Google Scholar
• Godfrey GA, Powell WB (2001) An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution. Management Sci. 47(8):1101–1112.Link, Google Scholar
• Graves SC, Meal HC, Dasu S, Qui Y (1986) Two-stage production planning in a dynamic environment. Multi-Stage Production Planning and Inventory Control (Springer, Berlin, Heidelberg), 9–43.Crossref, Google Scholar
• Heath DC, Jackson PL (1994) Modeling the evolution of demand forecasts ith application to safety stock analysis in production/distribution systems. IIE Trans. 26(3):17–30.Crossref, Google Scholar
• Heitsch H, Römisch W (2003) Scenario reduction algorithms in stochastic programming. Comput. Optim. Appl. 24(2–3):187–206.Crossref, Google Scholar
• Higle JH, Kempf KG (2010) Production planning under supply and demand uncertainty: A stochastic programming approach. Stochastic Programming (Springer, New York), 297-315.Crossref, Google Scholar
• Hu K, Acimovic J, Erize F, Thomas DJ, Van Mieghem JA (2016) Forecasting product life cycle curves: Practical approach and empirical analysis. Working paper, Northwestern University, SSRN: http://dx.doi.org/10.2139/ssrn.2867528.Google Scholar
• Huh WT, Rusmevichientong P (2009) A nonparametric asymptotic analysis of inventory planning with censored demand. Math. Oper. Res. 34(1):103–123.Link, Google Scholar
• Iida T, Zipkin PH (2006) Approximate solutions of a dynamic forecast-inventory model. Manufacturing Service Oper. Management 8(4):407–425.Link, Google Scholar
• Jones PC, Lowe TJ, Traub RD, Kegler G (2001) Matching supply and demand: The value of a second chance in producing hybrid seed corn. Manufacturing Service Oper. Management 3(2):122–137.Link, Google Scholar
• Kaminsky P, Swaminathan JM (2001) Utilizing forecast band refinement for capacitated production planning. Manufacturing Service Oper. Management 3(1):68–81.Link, Google Scholar
• Kaminsky P, Swaminathan JM (2004) Effective heuristics for capacitated production planning with multiperiod production and demand with forecast band refinement. Manufacturing Service Oper. Management 6(2):184–194.Link, Google Scholar
• Kunnumkal S, Topaloglu H (2008) Using stochastic approximation methods to compute optimal base-stock levels in inventory control problems. Oper. Res. 56(3):646–664.Link, Google Scholar
• Lau HS, Lau AHL (1996) The newsstand problem: A capacitated multiple-product single-period inventory problem. Eur. J. Oper. Res. 94(1):29–42.Crossref, Google Scholar
• Levi R, Perakis G, Uichanco J (2015) The data-driven newsvendor problem: New bounds and insights. Oper. Res. 63(6):1294–1306.Link, Google Scholar
• Levi R, Roundy RO, Shmoys DB (2007) Provably near-optimal sampling-based policies for stochastic inventory control models. Math. Oper. Res. 32(4):821–839.Link, Google Scholar
• Li J, Chand S, Dada M, Mehta S (2009) Managing inventory over a short season: Models with two procurement opportunities. Manufacturing Service Oper. Management 11(1):174–184.Link, Google Scholar
• Lu X, Song JS, Regan A (2006) Inventory planning with forecast updates: Approximate solutions and cost error bounds. Oper. Res. 54(6):1079–1097.Link, Google Scholar
• Milner JM, Kouvelis P (2002) On the complementary value of accurate demand information and production and supplier flexibility. Manufacturing Service Oper. Management 4(2):99–113.Link, Google Scholar
• Milner JM, Kouvelis P (2005) Order quantity and timing flexibility in supply chains: The role of demand characteristics. Management Sci. 51(6):970–985.Link, Google Scholar
• Patel J (2012) Optimization-based decision support system for retail sourcing. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
• Peng C, Erhun F, Hertzler EF, Kempf KG (2012) Capacity planning in the semiconductor industry: Dual-mode procurement with options. Manufacturing Service Oper. Management 14(2):170–185.Link, Google Scholar
• Pennanen T (2005) Epi-convergent discretizations of multistage stochastic programs. Math. Oper. Res. 30(1):245–256.Link, Google Scholar
• Pennanen T (2009) Epi-convergent discretizations of multistage stochastic programs via integration quadratures. Math. Programming 116(1–2):461–479.Crossref, Google Scholar
• Pennanen T, Koivu M (2002) Integration quadratures in discretization of stochastic programs. Higle JL, Römisch W, Sen S, eds. Stochastic Programming E-Print Series (Humboldt University of Berlin, Berlin).Google Scholar
• Pflug G, Hochreiter R (2003) Scenario generation for stochastic multi-stage decision processes as facility location problems. Technical report, University of Vienna, Austria.Google Scholar
• Qiang S, Bayati M (2016) Dynamic pricing with demand covariates. Working paper, Stanford University, SSRN: http://dx.doi.org/10.2139/ssrn.2765257.Google Scholar
• Rios I, Wets RJB, Woodruff DL (2015) Multi-period forecasting and scenario generation with limited data. Comput. Management Sci. 12(2):267–295.Crossref, Google Scholar
• Serel DA (2016) Intelligent procurement systems to support fast fashion supply chains in the apparel industry. Choi TM, ed. Information Systems for the Fashion and Apparel Industry (Elsevier, Amsterdam), 121–144.Crossref, Google Scholar
• Shapiro A, Dentcheva D, Ruszczyński A (2009) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).Crossref, Google Scholar
• Wang T, Atasu A, Kurtuluş M (2012) A multiordering newsvendor model with dynamic forecast evolution. Manufacturing Service Oper. Management 14(3):472–484.Link, Google Scholar
• Zipkin P (2008) On the structure of lost-sales inventory models. Oper. Res. 56(4):937–944.Link, Google Scholar