Solving Bi-objective Model of Hotel Revenue Management Considering Customer Choice Behavior Using Meta-heuristic Algorithms

Document Type: Original Manuscript


1 Islamic Azad University

2 K.N. Toosi University of Technology



The problem of maximizing the benefit from a specified number of a particular product with respect to the behavior of customer choices is regarded as revenue management. This managerial technique was first adopted by the airline industries before being widely used by many others such as hotel industries. The scope of this research is mainly focused on hotel revenue management, regarding which a bi-objective model is proposed. The suggested method aims at increasing the revenue of hotels by assigning the same rooms to different customers. Maximization of hotel revenue is a network management problem aiming to manage several resources simultaneously. Accordingly, a model is proposed in this paper based on the customer choice behavior in which the customers are divided into two groups of business and leisure. Customers of the business group prefer products with full price, whereas products with discounts are most desirable for leisure customers. The model consists of two objectives, the first one of which maximizes the means of revenue, and the second one minimizes the dispersion of revenue. Since the problem under consideration is Non-deterministic Polynomial-time hard (NP-hard), two meta-heuristic algorithms of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) are proposed to solve the problem. Moreover, the tuned algorithms are compared via the statistical analysis method. The results show that the NSGA-II is more efficient in comparison with MOPSO.

Graphical Abstract

Solving Bi-objective Model of Hotel Revenue Management Considering Customer Choice Behavior Using Meta-heuristic Algorithms


  • The problem is investigated in this research considering customer choice behavior.
  • This model has two objectives, maximization of expected revenue and minimization of dispersion of revenue
  • The customers were divided into two groups of business and leisure customers.
  • Customer preferences are chosen from different price levels.
  • The model is solved via two meta-heuristic algorithms.


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