Dynamic assortment optimization based on customers’ behavior using transaction data

Yousef Maknoon; Shadi Sharif Azadeh; Michel Bierlaire

Dynamic assortment optimization based on customers’ behavior using transaction data

Yousef Maknoon, Shadi Sharif Azadeh, Michel Bierlaire

Transport and Logistics

Research output: Contribution to conference › Abstract › Scientific

Abstract

In this paper, we aim at investigating the impact of demand modelling on revenue performance for a shared mobility system. We consider the case where the information associated to the systematic part of the utility function is not available. We use dynamic assortment optimization to estimate utilities. We compare this assumption with the case where the choice probabilities are assumed to be static and homogenously estimated. The results indicate that this additional information helps to improve the service level as well as the revenue performance.

Original language	English
Number of pages	1
Publication status	Published - 2018
Event	IATBR 2018: 15th International Conference on Travel Behaviour Research - Santa Barbara, United States Duration: 15 Jul 2018 → 20 Jul 2018 Conference number: 15 http://www.iatbr2018.org/

Conference

Conference	IATBR 2018: 15th International Conference on Travel Behaviour Research
Abbreviated title	IATBR 2018
Country/Territory	United States
City	Santa Barbara
Period	15/07/18 → 20/07/18
Internet address	http://www.iatbr2018.org/

Cite this

@conference{d769f00e49ba42faa2b7a3cc965d40e6,

title = "Dynamic assortment optimization based on customers{\textquoteright} behavior using transaction data",

abstract = "In this paper, we aim at investigating the impact of demand modelling on revenue performance for a shared mobility system. We consider the case where the information associated to the systematic part of the utility function is not available. We use dynamic assortment optimization to estimate utilities. We compare this assumption with the case where the choice probabilities are assumed to be static and homogenously estimated. The results indicate that this additional information helps to improve the service level as well as the revenue performance.",

author = "Yousef Maknoon and {Sharif Azadeh}, Shadi and Michel Bierlaire",

year = "2018",

language = "English",

note = "IATBR 2018: 15th International Conference on Travel Behaviour Research, IATBR 2018 ; Conference date: 15-07-2018 Through 20-07-2018",

url = "http://www.iatbr2018.org/",

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TY - CONF

T1 - Dynamic assortment optimization based on customers’ behavior using transaction data

AU - Maknoon, Yousef

AU - Sharif Azadeh, Shadi

AU - Bierlaire, Michel

N1 - Conference code: 15

PY - 2018

Y1 - 2018

N2 - In this paper, we aim at investigating the impact of demand modelling on revenue performance for a shared mobility system. We consider the case where the information associated to the systematic part of the utility function is not available. We use dynamic assortment optimization to estimate utilities. We compare this assumption with the case where the choice probabilities are assumed to be static and homogenously estimated. The results indicate that this additional information helps to improve the service level as well as the revenue performance.

AB - In this paper, we aim at investigating the impact of demand modelling on revenue performance for a shared mobility system. We consider the case where the information associated to the systematic part of the utility function is not available. We use dynamic assortment optimization to estimate utilities. We compare this assumption with the case where the choice probabilities are assumed to be static and homogenously estimated. The results indicate that this additional information helps to improve the service level as well as the revenue performance.

UR - https://easychair.org/smart-program/SantaBarbara2018-IATBR2018/2018-07-18.html

M3 - Abstract

T2 - IATBR 2018: 15th International Conference on Travel Behaviour Research

Y2 - 15 July 2018 through 20 July 2018

ER -

Dynamic assortment optimization based on customers’ behavior using transaction data

Abstract

Conference

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