A stochastic neighborhood search approach for airport gate assignment problem Hakkı Murat Genç a,b,⇑ , Osman Kaan Erol c , _ Ibrahim Eksin b , Mehmet Fatih Berber d , Binnur Onaran Güleryüz d a The National Research Institute of Electronics and Cryptology, Information Technologies Institute, TR-41470 Gebze, Kocaeli, Turkey b _ Istanbul Technical University, Faculty of Electrical and Electronics Engineering, Control Engineering Department, Maslak, TR-34469 _ Istanbul, Turkey c _ Istanbul Technical University, Faculty of Electrical and Electronics Engineering, Computer Engineering Department, Maslak, TR-34469 _ Istanbul, Turkey d TAV Information Technologies Corporation, _ Istanbul Atatürk International Airport, Yes ßilköy, TR-34149 _ Istanbul, Turkey article info Keywords: Gate assignment problem Big Bang–Big Crunch optimization algorithm abstract An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own phys- ical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great mod- ularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for _ Istanbul Atatürk Airport. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction Gate assignment problem (GAP) has increasing importance due to the increasing passengers in airports. Though it is easy to under- stand the problem definition and meaning of the various objective functions derived in the literature, many studies covered the same problem with different approaches. The total number of solution candidates is number of planes over number of gates, and for a practical airport, this product yields impractical amounts of candi- date solutions to be tried. So, the grandeur of the solution space and the existence of various and problem dependent constraints make the problem still difficult to solve for the optimum solution and therefore still up to date. The lack of a precise ordering among the solutions such as in the case of a numerical parameter optimization problem makes the problem almost impossible or – at least – difficult to solve using any gradient search methodology. Since one cannot guarantee the order of a flight to gate assignment list to another one, it is also impossible to obtain an ‘obvious’ parameter-objective function graphics. Possible objective functions can be defined in terms of the staying time of the planes in the gates, number of passengers in aircrafts, the total walking distances belonging to the passengers of all scheduled flights within a specified and closed time interval. Therefore, the problem formulation can vary quite a lot due to this large span of objectives. Moreover, basic gate assignment problem is NP-hard (non-deterministic polynomial-time hard) (Obata, 1979) quadratic assignment problem. Because of these, there are various approaches to this problem in the literature with respect to requirements imposed. The gate assignment problems can be categorized with respect to objective functions, mathematical for- mulations, time slot models and constraint satisfaction strategies. Besides, the solution approaches have two heavily interacting main branches: rule based expert systems and mathematical models. In our implementation, the GAP objective is to maximize total gate time as an integer programing mathematical formulation that uses multiple time slots and the basic constraint that allows one flight at one gate at one time. No rule based expert system is utilized algorithm; but in the system developed for Atatürk Airport, the constraints are processed by user defined rules. Teodorovic and Guberinic (1984) and Teodorovic and Stojkovic (1990) focus on total passenger delay and the number of flights 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.07.021 ⇑ Corresponding author at: The National Research Institute of Electronics and Cryptology, Information Technologies Institute, TR-41470 Gebze, Kocaeli, Turkey. Tel.: +90 262 6772624; fax: +90 262 6463187. E-mail address: murat.genc@bte.tubitak.gov.tr (H.M. Genç). Expert Systems with Applications 39 (2012) 316–327 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa