New neighborhood approaches in memetic algorithm for customer type discovery

Subject Areas : Computer Engineering and IT

Hamed Sherafat Moula ¹ , S.Hadi Yaghoubyan ² , razieh malekhoseini ³ , Karamolah BagheriFard ⁴

1 - Department of Computer Engineering, Yasuj Branch, Islamic Azad University, Yasuj, Iran
2 - Department of Computer Engineering, Yasuj Branch, Islamic Azad University, Yasuj, Iran
3 - Department of Computer Engineering, Yasuj Branch, Islamic Azad University, Yasuj, Iran
4 - Department of Computer Engineering, Yasuj Branch, Islamic Azad University, Yasuj, Iran

Received: 2024-02-02 Accepted : 2024-03-11 Published : 2024-07-31

Keywords: revenue management, meta-heuristic algorithms, genetic algorithm, memetic algorithm,

Abstract :

"Revenue management" systems are extensively utilized across various industries today. One of the primary pillars of revenue management lies in demand estimation, which predicts the demand for products and services. Understanding customers and their preferences forms the cornerstone of demand estimation, and this understanding is acquired through solving the "customer type discovery" problem. Recently, this problem has been addressed using the "genetic" meta-heuristic method. In this research, we propose solving this problem utilizing the "memetic" meta-heuristic method, employing alternative approaches to identify the neighborhood. By evaluating real data from five hotels, we demonstrate that our method offers the first viable solution to the problem, resulting in a total of 10.5% fewer iterations compared to the "genetic" method.

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