  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 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.
