Computing bid prices for revenue management under customer choice behavior

We consider a choice-based, network revenue management (RM) problem in a setting where heterogeneous customers consider an assortment of products offered by a firm (e.g., different flight times, fare classes, and/or routes). Individual choice decisions are modeled through an ordered list of preferen...

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Detalles Bibliográficos
Autores principales: Chaneton, J.M., Vulcano, G.
Formato: JOUR
Materias:
Choice behavior
Network capacity control
Simulation-based optimization
Stochastic gradient methods
Capacity models
Choice behaviors
Continuous demand
Control parameters
Control strategies
Customer choice
Expected revenue
Fare class
Flight time
Individual choice
Real-world application
Revenue function
Revenue management
Sample path
Simulation-based optimizations
Stationary points
Statistical properties
Steepest ascent
Algorithms
Gradient methods
Management
Network management
Optimization
Stochastic systems
Commerce
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_15234614_v13_n4_p452_Chaneton
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_15234614_v13_n4_p452_Chaneton
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spelling todo:paper_15234614_v13_n4_p452_Chaneton2023-10-03T16:20:52Z Computing bid prices for revenue management under customer choice behavior Chaneton, J.M. Vulcano, G. Choice behavior Network capacity control Simulation-based optimization Stochastic gradient methods Capacity models Choice behaviors Continuous demand Control parameters Control strategies Customer choice Expected revenue Fare class Flight time Individual choice Network capacity control Real-world application Revenue function Revenue management Sample path Simulation-based optimizations Stationary points Statistical properties Steepest ascent Stochastic gradient methods Algorithms Gradient methods Management Network management Optimization Stochastic systems Commerce We consider a choice-based, network revenue management (RM) problem in a setting where heterogeneous customers consider an assortment of products offered by a firm (e.g., different flight times, fare classes, and/or routes). Individual choice decisions are modeled through an ordered list of preferences, and minimal assumptions are made about the statistical properties of this demand sequence. The firm manages the availability of products using a bid-price control strategy, and would like to optimize the control parameters. We formulate a continuous demand and capacity model for this problem that allows for the partial acceptance of requests. The model admits a simple calculation of the sample path gradient of the revenue function. This gradient is then used to construct a stochastic steepest ascent algorithm. We show that the algorithm converges (w.p.1) to a stationary point of the expected revenue function under mild conditions. The procedure is relatively efficient from a computational standpoint, and in our synthetic and real-data experiments performs comparably to or even better than other choice-based methods that are incompatible with the current infrastructure of RM systems. These features make it an interesting candidate to be pursued for real-world applications. © 2011 INFORMS. Fil:Chaneton, J.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Vulcano, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_15234614_v13_n4_p452_Chaneton
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Choice behavior
Network capacity control
Simulation-based optimization
Stochastic gradient methods
Capacity models
Choice behaviors
Continuous demand
Control parameters
Control strategies
Customer choice
Expected revenue
Fare class
Flight time
Individual choice
Network capacity control
Real-world application
Revenue function
Revenue management
Sample path
Simulation-based optimizations
Stationary points
Statistical properties
Steepest ascent
Stochastic gradient methods
Algorithms
Gradient methods
Management
Network management
Optimization
Stochastic systems
Commerce
spellingShingle Choice behavior
Network capacity control
Simulation-based optimization
Stochastic gradient methods
Capacity models
Choice behaviors
Continuous demand
Control parameters
Control strategies
Customer choice
Expected revenue
Fare class
Flight time
Individual choice
Network capacity control
Real-world application
Revenue function
Revenue management
Sample path
Simulation-based optimizations
Stationary points
Statistical properties
Steepest ascent
Stochastic gradient methods
Algorithms
Gradient methods
Management
Network management
Optimization
Stochastic systems
Commerce
Chaneton, J.M.
Vulcano, G.
Computing bid prices for revenue management under customer choice behavior
topic_facet Choice behavior
Network capacity control
Simulation-based optimization
Stochastic gradient methods
Capacity models
Choice behaviors
Continuous demand
Control parameters
Control strategies
Customer choice
Expected revenue
Fare class
Flight time
Individual choice
Network capacity control
Real-world application
Revenue function
Revenue management
Sample path
Simulation-based optimizations
Stationary points
Statistical properties
Steepest ascent
Stochastic gradient methods
Algorithms
Gradient methods
Management
Network management
Optimization
Stochastic systems
Commerce
description We consider a choice-based, network revenue management (RM) problem in a setting where heterogeneous customers consider an assortment of products offered by a firm (e.g., different flight times, fare classes, and/or routes). Individual choice decisions are modeled through an ordered list of preferences, and minimal assumptions are made about the statistical properties of this demand sequence. The firm manages the availability of products using a bid-price control strategy, and would like to optimize the control parameters. We formulate a continuous demand and capacity model for this problem that allows for the partial acceptance of requests. The model admits a simple calculation of the sample path gradient of the revenue function. This gradient is then used to construct a stochastic steepest ascent algorithm. We show that the algorithm converges (w.p.1) to a stationary point of the expected revenue function under mild conditions. The procedure is relatively efficient from a computational standpoint, and in our synthetic and real-data experiments performs comparably to or even better than other choice-based methods that are incompatible with the current infrastructure of RM systems. These features make it an interesting candidate to be pursued for real-world applications. © 2011 INFORMS.
format JOUR
author Chaneton, J.M.
Vulcano, G.
author_facet Chaneton, J.M.
Vulcano, G.
author_sort Chaneton, J.M.
title Computing bid prices for revenue management under customer choice behavior
title_short Computing bid prices for revenue management under customer choice behavior
title_full Computing bid prices for revenue management under customer choice behavior
title_fullStr Computing bid prices for revenue management under customer choice behavior
title_full_unstemmed Computing bid prices for revenue management under customer choice behavior
title_sort computing bid prices for revenue management under customer choice behavior
url http://hdl.handle.net/20.500.12110/paper_15234614_v13_n4_p452_Chaneton
work_keys_str_mv AT chanetonjm computingbidpricesforrevenuemanagementundercustomerchoicebehavior
AT vulcanog computingbidpricesforrevenuemanagementundercustomerchoicebehavior
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