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Enhanced Order Based Single Leap Big Bang—Big Crunch Optimization Approach to Multi-Objective Gate Assignment Problem
Hakki Murat Genç, Osman Kaan Erol, Ibrahim Eksin and Cesur Cevdet Okutan

In the last few decades, rapid growth in demand for air transportation led to the development of numerous operation research practices in the airline / airport industry. The most widespread practice is the ground scheduling applications, and specifically, gate assignment optimization. An appropriate and efficient gate assignment is of great importance in airport ground operations since it plays a major role in increasing revenues. In this paper, a multi-objective gate assignment problem (MOGAP) is formulated with the objectives of maximizing gate allocation, minimizing passenger walking distance and maximizing flight to gate preference and a solution strategy based on the evolutionary Single Leap Big Bang – Big Crunch optimization method is developed. The MOGAP is a non-deterministic polynomial-time hard (NP-hard) quadratic assignment problem. In the literature, to the best of our knowledge, there is only a single effort to solve the MOGAP for obtaining a pareto front representation of solutions by utilizing nature inspired computation methods. As the major contributions of this paper, a novel multi-objective nature inspired solution technique is proposed and high fidelity problem instance generation is discussed. The effectiveness of the proposed methodology has been illustrated by comparing the simulation results of the method with the previously reported algorithm both on artificially generated problem instances and real world data obtained from Turkey’s biggest airport, Atatürk International in Istanbul.

Keywords: Evolutionary multi-objective optimization, airport gate assignment problem, nature inspired computing, big-bang big crunch optimization.

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