Improved genetic algorithms by means of fuzzy crossover operators for revenue management in airlines

Khanmohammadi, S. and Mousavi, Seyed Ali. and Sadeghi, R. and Sadeghi, Mohsen. (2012) Improved genetic algorithms by means of fuzzy crossover operators for revenue management in airlines. World Applied Sciences Journal, 2 (6).

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Abstract

Abstract: Revenue Management is an economic policy that increases the earned profit by adjusting the service demand and inventory. Revenue Management in airlines correlates with inventory control and price levels in different fare classes. We focus on pricing and seat allocation problems in airlines by introducing a constrained optimization problem in Binary Integer Programming (BIP) formulation. Two BIP problems are represented. Moreover, some improved Genetic Algorithms (GAs) approaches are used to solve these problems. We introduce new crossover operators that assign a Fuzzy Membership Function to each parent in GAs. We achieve better outputs with new methods that take lower calculation times and earn higher profits. Three different test problems in different scales are selected to evaluate the effectiveness of each algorithm. This paper defines new crossover operators that help to reach better solutions that take lower calculation times and more earned profits.

Item Type: Article
Uncontrolled Keywords: Key words: Genetic Algorithms (Gas) Operational Research (OR) Binary Integer Programming Revenue Management Fuzzy Logic
Subjects: QU Biochemistry > Cell biology and genetics
Divisions: Faculty of Health
Depositing User: zahra bagheri .
Date Deposited: 17 Oct 2017 06:18
Last Modified: 20 May 2018 07:53
URI: http://eprints.skums.ac.ir/id/eprint/5996

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