Optimising flight connection times in airline bank structure through Simulated Annealing and Tabu Search algorithms |
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| Affiliation: | 1. Department of Industrial Engineering, Bilkent University, 06800 Ankara, Turkey;2. Industrial Engineering & Operations Research, University of California, Berkeley, CA 94720, USA;1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;2. University of Chinese Academy of Sciences, 100049, China;1. Department of Information Systems & Business Administration, Johannes-Gutenberg University Mainz, Jakob-Welder-Weg 9, 55128 Mainz, Germany;2. airconomy aviation intelligence GmbH, Amadeus IT Group, Hugo-Eckener-Ring FAC 1, 60549 Frankfurt, Germany;1. Institute of Air Transportation Systems, German Aerospace Center, Blohmstrasse 18, 21079 Hamburg, Germany;2. Department of Computer Science, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany;1. Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran;2. Department of Industrial Engineering, University of Bojnord, Bojnord, Iran;3. Department of Civil Engineering, Imam Khomeini International University, Qazvin, Iran |
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| Abstract: | In hub and spoke airline networks, flight arrivals and departures generally have a bank structure to increase connections among spoke cities through a hub airport in order to provide cheaper service for higher volumes of air traffic. In this study, we introduce the airline bank optimisation problem with a novel mathematical model for improving flight connection times. The mathematical model aims to minimise the total connection times for transfer passengers and generates flight schedules regarding slot capacities in the hub airports. Since the problem is a combinatorial optimisation problem NP-hard and computational complexity increases rapidly for real-world problems, we employ the simulated annealing and the tabu search algorithms to achieve better solutions in a reasonable time. We generate sub-problems using real-world data and investigate the effectiveness of the algorithms. Finally, we present the results of a real case study of a Turkish airline company which has a hub airport connecting the flights between Middle Eastern and European cities. |
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| Keywords: | Hub and spoke systems Flight rescheduling Tabu search Simulated annealing Airline bank structure |
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