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Estimation and pricing for substitutable products in choice-based revenue management

Estimation and pricing for substitutable products in choice-based revenue management
Estimation and pricing for substitutable products in choice-based revenue management
-It has been proved that choice-based Revenue Management can result in significant increases in revenue in situations where a seller is pricing a set of substitutable products. This is particularly applicable to the transport industry and we present an example of train ticket sales.

Estimating customer choice models is difficult, particularly in situations where the data file is incomplete. We use the Multinomial Logit (MNL) model to describe customer preferences, and a two-step algorithm to jointly estimate the parameters of this model and the customer arrival rate. A simple Markov Chain Monte Carlo (MCMC) method is also applied to update our belief of arrival rate and customer choice model.

The dynamic programming model for the choice-based pricing problem suffers from the “curse of dimensionality ”. The computational time increases dramatically and makes it impossible to solve the problem with exact solutions. Approximate dynamic programming methods can be used to solve the problem. We propose a new approximation method that reduces the running time. The thesis will describe the complete methodology that we have implemented and provide some numerical results.

As these are live sales systems, it is important that the system continues to earn revenues while the parameters are being estimated. A decision-making problem is needed to maintain a balance between the learning of customer preference (exploration) and earning (exploitation) in choice-based Revenue Management. In order to maximise the total revenue, the seller must decide whether to choose the current optimal price (exploitation) or to set prices that help to better estimate customer choice behaviour (exploration). We propose two pulling policies in a Multi-armed Bandit (MAB) experiment to balance the trade-off between exploration and exploitation.
University of Southampton
Bi, Yalin
cad2ef80-3eb9-4168-9f9e-292e45532c46
Bi, Yalin
cad2ef80-3eb9-4168-9f9e-292e45532c46
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Fliege, Joerg
54978787-a271-4f70-8494-3c701c893d98

Bi, Yalin (2018) Estimation and pricing for substitutable products in choice-based revenue management. University of Southampton, Doctoral Thesis, 120pp.

Record type: Thesis (Doctoral)

Abstract

-It has been proved that choice-based Revenue Management can result in significant increases in revenue in situations where a seller is pricing a set of substitutable products. This is particularly applicable to the transport industry and we present an example of train ticket sales.

Estimating customer choice models is difficult, particularly in situations where the data file is incomplete. We use the Multinomial Logit (MNL) model to describe customer preferences, and a two-step algorithm to jointly estimate the parameters of this model and the customer arrival rate. A simple Markov Chain Monte Carlo (MCMC) method is also applied to update our belief of arrival rate and customer choice model.

The dynamic programming model for the choice-based pricing problem suffers from the “curse of dimensionality ”. The computational time increases dramatically and makes it impossible to solve the problem with exact solutions. Approximate dynamic programming methods can be used to solve the problem. We propose a new approximation method that reduces the running time. The thesis will describe the complete methodology that we have implemented and provide some numerical results.

As these are live sales systems, it is important that the system continues to earn revenues while the parameters are being estimated. A decision-making problem is needed to maintain a balance between the learning of customer preference (exploration) and earning (exploitation) in choice-based Revenue Management. In order to maximise the total revenue, the seller must decide whether to choose the current optimal price (exploitation) or to set prices that help to better estimate customer choice behaviour (exploration). We propose two pulling policies in a Multi-armed Bandit (MAB) experiment to balance the trade-off between exploration and exploitation.

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Published date: October 2018

Identifiers

Local EPrints ID: 420666
URI: https://eprints.soton.ac.uk/id/eprint/420666
PURE UUID: a8d6cb6e-d627-4e57-b259-adc4d8402b37
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Joerg Fliege: ORCID iD orcid.org/0000-0002-4459-5419

Catalogue record

Date deposited: 11 May 2018 16:30
Last modified: 14 Mar 2019 01:44

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