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

Journal of Optimization in Industrial Engineering

مقاله 2، دوره 14، شماره 1 - شماره پیاپی 30، شهریور 2021، صفحه 10-18   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2020.511.21

نویسندگان

Surur Yaghobi Harzandi¹؛ Amir Abbas Najafi^{* 2}

¹Islamic Azad University

²K.N. Toosi University of Technology

چکیده

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.

کلیدواژه‌ها

Hotel Revenue management؛ Bi-objective model؛ Meta-heuristic algorithms؛ Customer choices

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