Solving Bi-objective Model of Hotel Revenue Management Considering Customer Choice Behavior Using Meta-heuristic Algorithms
محورهای موضوعی : StrategySurur Yaghobi Harzandi 1 , Amir Abbas Najafi 2
1 - Islamic Azad University
2 - K.N. Toosi University of Technology
کلید واژه: Meta-Heuristic Algorithms, Bi-objective model, Hotel Revenue management, Customer choices,
چکیده مقاله :
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.
Adelman, D. (2007). Dynamic bid-prices in revenue management, Operations Research, 55(1), 647-661.
Amiri, M., Najafi, A.A., Gheshlaghi, K. (2008). Response surface methodology and genetic algorithm in optimization of cement clinkering process, Journal of Applied Sciences, 8(15), 2732-2738.
Arjmand, M., Najafi, A.A., Ebrahimzadeh, M. (2020). Evolutionary algorithms for multi-objective stochastic resource availability cost problem, OPSEARCH, 57(3), 935-985
Bront, J.J.M., Diaz, I.M., Bront, J .M., Vulcano,G. (2009). A column generation algorithm for the choice-based linear programming model for network revenue management .Operations researches, 57(3), 769-784.
Coello Coello, C.A., Lechuga, M.S. (2002). MOPSO:A propsal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, IEEE Press, 1051-1056.
Etebari, F., Aaghaie, A., Khoshalhan, F. (2011). A genetic algorithm for choice –based network revenue management, Iranian Journal of Operations Research,3(1), 89-103.
Gallego, G., Iyengar,G., Phillips, F., Dubey, A. (2004). Managing flexible products on a network, Technical report CORC TR-2004-01, Department of industrial Engineering and operations research, Columbia University.
Goldman, G., Iyengar, G., Phillips, F., Dubey, A. (2002). Models and techniques for hotel revenue management using a rolling horizon, Journal of Revenue and Pricing Management, 1(3), 207-219.
Gosavi, A. (2002). The effect of noise on artificial intelligence and meta-heuristic Techniques, Intelligence Engineering systems through artificial neural network, 12, 981-988.
Kunnumkal, S., Topaloglu, H. (2010). Computing time –dependent bid prices in network revenue management problems, Transportation Science, 44(1), 38-62.
Lai, K.K., Ng, W. (2005). A Stochastic approach to hotel revenue optimization, Computers & Operations Research, 32, 1059-1072.
Liu, Q., Van Ryzin, G. (2008). On the choice-based linear programming model for network revenue management, Manufacturing and Service Operation Management, 10(2), 288-310.
Mostaghim, S., Teich, J. (2003). Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). Proceedings of the 2003 IEEE Swarm Intelligence Symposium. IEEE Press, 26-33.
Phillips. R. (2005). Pricing and revenue optimization, Stanford University Press.
Ray, W.S. (1960). An introduction to experimental design. Macmillan, New York.
Rezaei, F., Najafi, A.A., Ramezanian, R. (2020). Mean-conditional value at risk model for the stochastic project scheduling problem, Computers and Industrial Engineering, 142(2020), 106356.
Salimi, M., Najafi, A.A. (2018). Modeling and solution procedure for a preemptive multi-objective multi-mode project scheduling model in resource investment problems, Journal of Optimization in Industrial Engineering, 11(1), 169-183
Schott, J.R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization, Master’s Thesis, Department of Aeronautics and Astronautics, Institute of Technology, Massachusetts.
Shahsavar M., Najafi A.A. and Niaki, S. T. A. (2011). Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems", Mathematical Problems in Engineering, 2011, 2-17.
Srinivas, N., Deb, K. (1994). Multi-objective optimization using nondominated sorting in genetic algorithm, Evolutionary computation, 2(3), 221-248.
Talluri, K.Y., Van Ryzin, G.J. (2004). Revenue management under a general discrete choice model of consumer behavior, Management Science, 50, 15-33.
Tong, C., Topalogu, H., (2012). On the approximate linear programming approach for network revenue management problem. INFORMS Journal on Computing, 26(1), 1-18.
Van Ryzin .J., Vulcano, G. (2008). Computing virtual nesting controls for network revenue management under customer choice behavior. Manufacturing Service Operations Management, 10(3), 448-467.
Zitzler, E., Deb, K., Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computation, 8(2), 173-195.
Zitzler, E., Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms - A comparative case study, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 292-301.
Zitzler, E., (1999). Evoutionary algorithms for multiobjective optimization: Methods and applications, A dissertation submitted to the Swiss Federal Institute of Technology, Zurich, Swiss.
