Estimating Primary Demand for Substitutable Products from Sales Transaction Data

Vulcano, Gustavo; Ryzin, Garrett van; Ratliff, Richard

doi:10.1287/opre.1110.1012

Cited by 234 publications

(111 citation statements)

References 34 publications

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…They also reported a case study that tested their method . Vulcano et al (2012) proposed a method for estimating substitute and lost demand in a general sales context (not just airline RM) when only sales and product availability are observable and assuming the seller knows its aggregate market share. They used a multinomial logit choice model with a non-homogeneous Poisson model of arrivals over multiple periods.…”

Section: General Surveys and Simulationsmentioning

confidence: 99%

The history of unconstraining models in revenue management

Weatherford¹

2016

J Revenue Pricing Manag

View full text Add to dashboard Cite

Larry Weatherford is the Scarlett Endowed Chair at the University of Wyoming. He holds a PhD from the University of Virginia. He has received several Outstanding Teaching Awards. He has published 35 scholarly articles and presented over 100 papers on six different continents. He has consulted with such major corporations as ABSTRACT Unconstraining has been used in revenue management (RM) for nearly the last 40 years.This brief, historical article surveys over 40 articles from the recent period and traces the evolution of RM unconstraining models. The two main areas that are covered are general articles comparing the performance of different unconstraining methods under a variety of circumstances and more recently, customer choice models related to unconstraining censored data.

show abstract

Section: General Surveys and Simulationsmentioning

confidence: 99%

The history of unconstraining models in revenue management

Weatherford¹

2016

J Revenue Pricing Manag

View full text Add to dashboard Cite

show abstract

“…This problem has been discussed in detail in Cooper et al (2006). Spiral-down effect problem has been addressed by considering the upsell rate in forecasting and optimization models (see for example Brumelle et al, 1990, Belobaba and Weatherford, 1996, Fiig et al, 2010, Vulcano et al, 2012. However, estimation of the sell-up value involves many assumptions and approximations.…”

Section: Modeling Sell-up Potentialmentioning

confidence: 99%

“…These data, along with business inputs from the airline, serve in coming up with the untruncated demand for each service-class-POS combination, the competition and business rules. The multi-flight expectation maximization (MFEM) algorithm proposed in Ratliff et al (2008) and Vulcano et al (2012) is used for demand unconstraining. MFEM jointly estimates spill and recapture along with demand from the bookings and availability information.…”

Section: Apos Frameworkmentioning

confidence: 99%

Simulating the flavors of revenue management for airlines

Doreswamy¹,

Kothari²,

Tirumalachetty³

2015

J Revenue Pricing Manag

View full text Add to dashboard Cite

“…Q-forecast Hopperstad & Belobaba (2004) Hybrid forecast Boyd & Kallesen (2004) Choice-based, Vulcano (2012) n/a Direct Observation, Inverse Cumulative, Forecast Prediction Hopperstad et al (2006Hopperstad et al ( , 2007 Choice-based, Vulcano (2012) Unconstrained demand Lee (1990), Oancea & Bala (2013) ??? Belobaba (1987), EMSR Smith & Penn (1992) Demand forecasting and measuring forecast accuracy For this step, similar time-series methods were applied.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An iterative approach for solving choice parameters is described in Vulcano et al (2010Vulcano et al ( , 2012. This approach calculates the expected value of underlying demand (E-step) conditioned on current estimates of the β parameters, followed by the maximization of the log-likelihood (M-step) to obtain next-iteration parameter estimates.…”

Section: Complexities Of Dependent Demand Forecastingmentioning

confidence: 99%

Demand forecasting and measuring forecast accuracy in general fare structures

Fiig¹,

Härdling²,

Pölt

et al. 2014

J Revenue Pricing Manag

View full text Add to dashboard Cite

We present a forecasting methodology that enables forecasting in general fare structures (or, equivalently, demand models), including restricted, semi-restricted, unrestricted and fare families. Our contributions are twofold. First, we re-establish the measurement of forecast accuracy to be applicable to dependent demand models, by introducing a constrained forecast accuracy measure. Second, we provide a parametric forecast model for a general demand model, and we apply the forecast accuracy measure to compute optimal forecast parameters by minimizing the retrospective forecasting error on historical observations. Most of the parameters of our forecast model can be determined analytically in closed form based on historical observations. Appropriate aggregation levels and parsimonious use of parameters ensure robustness and resolve data-sparseness problems. We prove the performance of our approach using real airline data for a fenceless fare structure. Further, we use the Passenger Origin-Destination Simulator to investigate the impact of errors in the forecast parameters on revenue, load factor and forecast accuracy.

show abstract

Estimating Primary Demand for Substitutable Products from Sales Transaction Data

Cited by 234 publications

References 34 publications

The history of unconstraining models in revenue management

The history of unconstraining models in revenue management

Simulating the flavors of revenue management for airlines

Demand forecasting and measuring forecast accuracy in general fare structures

Contact Info

Product

Resources

About