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Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

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Abstract

This paper explores the critical domain of revenue management (RM) within operations research (OR), focusing on intricate pricing dynamics. Utilizing mixed integer linear programming (MILP) models, the study enhances revenue optimization by considering product prices as decision variables and emphasizing the interplay between demand and supply. Recent advancements in discrete choice models (DCMs), particularly those rooted in random regret minimization (RRM) theory, are investigated as potent alternatives to established random utility maximization (RUM)-based DCMs. Despite the widespread use of DCMs in RM, a significant gap exists between cutting-edge RRM-based models and their practical integration into RM strategies. The study addresses this gap by incorporating an advanced RRM-based DCM into MILP models, addressing pricing challenges in both capacitated and uncapacitated supply scenarios. The developed models demonstrate the feasibility and offer diverse interpretations of consumer choice behavior, drawing inspiration from established RUM-based frameworks. This research contributes to bridging the existing gap in the application of advanced DCMs within practical RM implementations.

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Data Availability

Data and code availability: Data and code are available upon request.

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Acknowledgements

We acknowledge Grenoble Alpes university IDEX scholarship. Dr. Pierre Lemaire and Dr. Iragael Joly provided important support.

Funding

The authors declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA

    Amirreza Talebi

  2. School of Computing, Clemson University, Clemson, SC, 29632, USA

    Sayed Pedram Haeri Boroujeni & Abolfazl Razi

  3. ENSIMAG, Grenoble Alpes University, Grenoble, France

    Amirreza Talebi

Authors
  1. Amirreza Talebi
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  2. Sayed Pedram Haeri Boroujeni
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  3. Abolfazl Razi
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Contributions

Amirreza Talebi: Conceptualization, Investigation, Methodology, Formal Analysis, Software, Validation, Writing—original draft. Sayed Pedram Haeri Boroujeni: Methodology, Software, Formal Analysis, Data curation, Writing—original draft. Abolfazl Razi: Conceptualization, Supervision.

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Correspondence to Amirreza Talebi.

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Talebi, A., Haeri Boroujeni, S.P. & Razi, A. Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization. Iran J Comput Sci (2024). https://doi.org/10.1007/s42044-024-00193-w

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