The gate assignment problem (GAP) is one of the most important problems that operations managers face daily. The GAP aims at determining an assignment of flights to terminal and ramp positions (gates), and an assignment of starting and ending times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of passengers’ total walking distance. The main aim of this research is to find a novel methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained by properly combining two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee’s search behavior. The obtained results show the better performances of the proposed approach in solving FGAP when compared to BCO.
Optimizing Airport Gate Assignments Through a Hybrid Metaheuristic Approach / Marinelli, Mario; Palmisano, Gianvito; Dell'Orco, Mauro; Ottomanelli, Michele (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING). - In: Advanced Concepts, Methodologies and Technologies for Transportation and Logistics / [a cura di] Jacek Żak, Yuval Hadas, Riccardo Rossi. - Cham, CH : Springer, 2018. - ISBN 978-3-319-57104-1. - pp. 389-404 [10.1007/978-3-319-57105-8_19]
Optimizing Airport Gate Assignments Through a Hybrid Metaheuristic Approach
MARINELLI, Mario
;PALMISANO, Gianvito;DELL'ORCO, Mauro;OTTOMANELLI, Michele
2018-01-01
Abstract
The gate assignment problem (GAP) is one of the most important problems that operations managers face daily. The GAP aims at determining an assignment of flights to terminal and ramp positions (gates), and an assignment of starting and ending times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of passengers’ total walking distance. The main aim of this research is to find a novel methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained by properly combining two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee’s search behavior. The obtained results show the better performances of the proposed approach in solving FGAP when compared to BCO.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.