Solving the gate assignment problem through the Fuzzy Bee Colony Optimization. (July 2017)

• Record Type: Journal Article
• Authors: Dell'Orco, Mauro; Marinelli, Mario; Altieri, Maria Giovanna
• Is Part Of: Transportation research. Volume 80(2017)
• Page Start: 424
• Page End: 438
• Publication Date: 2017-07
• Subjects: Bee Colony Optimization -- Metaheuristics -- Multicriteria analysis -- Air transport -- Flight gate assignment problem; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.03.019
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A new heuristic algorithm based on the Fuzzy Bee Colony Optimization (FBCO). Bee Colony Optimization solves combinatorial problems with a fast convergence. FBCO handles uncertainty or approximation in data, often present in FGAP. Effectiveness of the proposed method from comparison with two real airport schedules. Results of the proposed method outperform the Ant Colony Optimization. Abstract: In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers' total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.