Boyuan Xia
ABSTRACT
The1 Aircraft-Gateway Allocation Problem (AGAP) has long been a key research issue in the field of management and operations. When a satellite hall is expanded, the passengers' transit process and time will increase. This paper re-models and optimizes AGAP. From the perspectives of airports, passengers and airlines, the objective of minimizing overall transit tension is added to the traditional optimization goals of maximizing the number of flights allocated to appropriate gates and minimizing the average passengers transit time. A non-dominated genetic algorithm (NSGA-III) is used to solve the new multi-objective AGAP model. Finally, using the modified case data of Pudong Airport in China Eastern Airlines, the models and methods are analyzed. Firstly, the feasibility of the model and method is verified. Secondly, by comparing with the effect of NSGA-II algorithm, the NSGA- III is proved better in obtaining a more ideal Pareto frontier in solving three target optimization problems.
- T. Obata. 1979. Quadratic Assignment Problem: Evaluation of Exact and Heuristic Algorithms, Rensselaer Polytechnic Institute, Troy, NY, USA.Google Scholar
- Andreas Drexl, Yury Nikulin. 2008. Multicriteria airport gate assignment and Pareto simulated annealing. IIE Transactions, 40, 4 (Feb. 2007), 385--397.Google ScholarCross Ref
- Hu X B, Paolo E D. 2009. A ripple-spreading genetic algorithm for the airport gate assignment problem. In Proceeding of the IEEE Congress on Evolutionary Computation, Cec 2009, Trondheim, Norway, 1857--1864. Google ScholarDigital Library
- Genç H M, Erol O K, İbrahim Eksin, et al. 2012. A stochastic neighborhood search approach for airport gate assignment problem. Expert. Syst. Appl. 39, 1 (Jan. 2012), 316--327. Google ScholarDigital Library
- Marinelli M, Dell' Orco M, Sassanelli D. 2015. A Metaheuristic Approach to Solve the Flight Gate Assignment Problem. Transport. Res. Proced. 5, 2 (2015), 211--220.Google ScholarCross Ref
- C. A. C. Coello, G. B. Lamont, and D. A. V. Veldhuizen. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed. New York, NY, USA: Springer. Google ScholarDigital Library
- S. Shroff and V. Dabhi, 2013. Dew point modelling using GEP based multiobjective optimization, CoRR. (Apr 2013), http://arxiv.org/abs/1304.5594Google Scholar
- Li Z., Li Z., Rudolph G. 2007. On the Convergence Properties of Quantum-Inspired Multi-Objective Evolutionary Algorithms. In: Huang DS., Heutte L., Loog M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg.Google ScholarCross Ref
- Wiem Mkaouer, Marouane Kessentini, Adnan Shaout, Patrice Koligheu, Slim Bechikh, Kalyanmoy Deb, and Ali Ouni. 2015. Many-Objective Software Remodularization Using NSGA-III. ACM Trans. Softw. Eng. Methodol. 24, 3, Article 17 (May 2015), 45 pages. Google ScholarDigital Library
Index Terms
- Study on multi-objective optimization for the allocation of transit aircraft to gateway considering satellite hall
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