A real-time integrated optimization of the aircraft holding time and rerouting under risk area (original) (raw)

References

• Abdalla, T. Y., Abed, A. A., & Ahmed, A. A. (2017). Mobile robot navigation using PSO-optimized fuzzy artificial potential field with fuzzy control. Journal of Intelligent & Fuzzy Systems, 32(6), 3893–3908. https://doi.org/10.3233/IFS-162205.
  Article Google Scholar
• Alexandre, J., & Odoni, A. R. (2017). An integrated scheduling and operations approach to airport congestion mitigation. Operations Research, 63(6), 1390–1410.
  Google Scholar
• Alonso-Ayuso, A., Escudero, L. F., & Martín-Campo, F. J. (2014). On modeling the air traffic control coordination in the collision avoidance problem by mixed integer linear optimization. Annals of Operations Research, 222, 89–105. https://doi.org/10.1007/s10479-013-1347-y.
  Article Google Scholar
• Andrews, J. W. (1993). Impact of weather event uncertainty upon an optimum ground-holding strategy. Air Traffic Control Quarterly, 1(1), 59–84.
  Article Google Scholar
• Antony, D. E., Paul, L., & Banavar, S. (2018). Predicting the operational acceptance of airborne flight reroute requests using data mining. Transportation Research Part C: Emerging Technologies, 96, 270–289. https://doi.org/10.1016/j.trc.2018.09.024.
  Article Google Scholar
• Coy, S. (2006). A global model for estimating the block time of commercial passenger aircraft. Journal of Air Transport Management, 12(6), 300–305. https://doi.org/10.1016/j.jairtraman.2006.07.005.
  Article Google Scholar
• D’Ariano, A., Pistelli, M., & Pacciarelli, D. (2012). Aircraft retiming and rerouting in vicinity of airports. IET Intelligent Transport Systems, 6(4), 433–443.
  Article Google Scholar
• Dimitris, B., & Sarah, S. P. (2000). The traffic flow management rerouting problem in air traffic control: A dynamic network flow approach. Transportation Science, 34(3), 239–255.
  Article Google Scholar
• Dorndorf, U., Jaehn, F., & Pesch, E. (2012). Flight gate scheduling with respect to a reference schedule. Annals of Operations Research, 194, 177–187. https://doi.org/10.1007/s10479-010-0809-8.
  Article Google Scholar
• Federal Aviation Administration. (2020). NextGen weather. United States Department of Transport. Retrieved April 10, 2020, from https://www.faa.gov/nextgen/programs/weather/.
• Flight states. (2019). Airline on-time performance report. Retrieved August 12, 2019, from https://www.flightstats.com/v2/monthly-performance-reports/airlines.
• Ge, S. S., & Cui, Y. J. (2000). New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation (S1046-296X), 16(5), 615–620.
  Article Google Scholar
• Guo, L. X. (2019). Research on Genetic Algorithm in distribution path planning. Journal of Physics: Conference Series, 1237, 022–128. https://doi.org/10.1088/1742-6596/1237/2/022128.
  Article Google Scholar
• Hauf, T., Hupe, P., Sauer, M., Rokitansky, C. H., Lang, J., Sacher, D., et al. (2017). Aircraft route forecasting under adverse weather conditions. Meteorologische Zeitschrift, 26(2), 189–206. https://doi.org/10.1127/metz/2016/0786.
  Article Google Scholar
• Jaehn, F., Dorndorf, U., et al. (2017). Flight gate assignment and recovery strategies with stochastic arrival and departure times. OR Spectrum, 39, 65–93. https://doi.org/10.1007/s00291-016-0443-1.
  Article Google Scholar
• James, C. J., David, J. L., & Michael, O. B. (2017). Stochastic optimization models for transferring delay along flight trajectories to reduce fuel usage. Transportation Science, 52(special SI), 1–16. https://doi.org/10.1287/trsc.2016.0689.
  Article Google Scholar
• Khatib, O. (1986). Real time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5(1), 90–98.
  Article Google Scholar
• Koetse, M. J., & Rietveld, P. (2009). The impact of climate change and weather on transport: an overview of empirical findings. Transportation Research Part D: Transport and Environment, 14(3), 205–221. https://doi.org/10.1016/j.trd.2008.12.004.
  Article Google Scholar
• Lin, T., Zhang, H., Zhang, K., Tu, Z. B., & Cui, N. (2017). An adaptive multiobjective estimation of distribution algorithm with a novel Gaussian sampling strategy. Soft Computing, 21(20), 1–19. https://doi.org/10.1007/s00500-016-2323-7.
  Article Google Scholar
• Liu, W. M., Zhu, X. H., & Qi, Y. L. (2016). Integrated fleet assignment and aircraft routing based on delay propagation. Sādhanā, 41(7), 713–719. https://doi.org/10.1007/s12046-016-0506-7.
  Article Google Scholar
• Min, G. P., & Min, C. L. (2003). Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In _IEEE/ASME international conference on advanced intelligent mechatronics (AIM) (S7803_-7759), USA: IEEE (pp. 735–740). https://ieeexplore.ieee.org/document/1225434.
• Montied, O., Sepulveda, R., & Orozco-rosas, U. (2015). Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. Journal of Intelligent and Robotic Systems, 79(2), 237–257. https://doi.org/10.1007/s10846-014-0124-8.
  Article Google Scholar
• Mukherjee, M., & Hansen, M. (2005). Dynamic stochastic optimization model for air traffic flow management with en route and airport capacity constraints. In 6th USA/Europe air traffic management R&D seminar.
• Mukherjee, A., & Hansen, M. (2009). A dynamic rerouting model for air traffic flow management. Transportation Research Part B: Methodological, 43(1), 159–171.
  Article Google Scholar
• Nakazawa, K., Takahashi, K., & Kaneko, M. (2013). Unified environment-adaptive control of accompanying robots using artificial potential field. ACM/IEEE International Conference on Human-Robot Interaction, 28(2), 199–200.
  Google Scholar
• Ng, K. K. H., & Lee, C. K. M. (2017). Aircraft scheduling considering discrete airborne delay and holding pattern in the near terminal area. Intelligence Computing Theories and Application, ICIC, Part I, 10361, 567–576. https://doi.org/10.1007/978-3-319-63309-1_50.
  Article Google Scholar
• Odoni, A. R. (1987). The flow management problem in air traffic control, flow control of congested networks (pp. 269–288). Berlin: Springer-Verlag.
  Book Google Scholar
• Odoni, L. A. (2007). The European air traffic flow management problem. Transportation Science, 41(4), 431–443. https://doi.org/10.3182/20060829-3-NL-2908.00017.
  Article Google Scholar
• Ozgur, M., & Cavcar, A. (2014). 0–1 integer programming model for procedural separation of aircraft by ground holding in ATFM. Aerospace Science and Technology, 33(1), 1–8. https://doi.org/10.1016/j.ast.2013.12.009.
  Article Google Scholar
• Park, J. H., & Huh, U. Y. (2015). Local path planning for mobile robot using artificial neural network—Potential field algorithm. The Transactions of The Korean Institute of Electrical Engineers, 64(10), 1479–1485.
  Article Google Scholar
• Racha, E. H., Rym, N. G., Aziz, M., & Mehdi, S. (2020). A PSO based algorithm with an efficient optimal split procedure for the multiperiod vehicle routing problem with profit. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03540-9.
  Article Google Scholar
• Sauer, M., Hauf, T., Sakiew, L., Chan, P. W., Tse, S. M., & Hupe, P. (2017). On the identification of weather avoidance routes in the terminal maneuvering area of Hong Kong International Airport. Journal of Zhejiang University-SCIENCE A, 17(3), 171–185. https://doi.org/10.1631/jzus.A1500186.
  Article Google Scholar
• Savkin, A. V., & Huang, H. (2017). Optimal aircraft planar navigation in static threat environments. IEEE Transactions on Aerospace and Electronic Systems, 53(5), 2413–2426.
  Article Google Scholar
• Seyyed, M. H. R., Arun, K. S., Wang, J., & Liu, X. Z. (2019). Obstacle avoidance of mobile robots using modified artificial potential field algorithm. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1–19. https://doi.org/10.1186/s13638-019-1396-2.
  Article Google Scholar
• Song, B., Wang, Z., & Sheng, L. (2016). A new genetic algorithm approach to smooth path planning for mobile robots. Assembly Automation, 36(2), 138–145. https://doi.org/10.1108/AA-11-2015-094/full/html.
  Article Google Scholar
• Thomas, H., Patrick, H., Manuela, S., Carl-Herbert, R., Jürgen, L., Daniel, S., et al. (2016). Aircraft route forecasting under adverse weather conditions. Traffic Meteorology, 26, 189–206. https://doi.org/10.1127/metz/2016/0786.
  Article Google Scholar
• Tian, Y., Wan, L. L., Han, K., & Ye, B. (2018). Optimization of terminal airspace operation with environmental considerations. Transportation Research Part D: Transport and Environment., 63, 872–889. https://doi.org/10.1016/j.trd.2018.06.018.
  Article Google Scholar
• Vranas, P. B., Bertsimas, D. J., & Odoni, A. R. (1994). The multi-airport ground-holding problem in air traffic control. Operations Research, 42(2), 249–261. https://doi.org/10.1287/opre.42.2.249.
  Article Google Scholar
• Wang, X. L., Li, H., & Wang, F. (2012). Flight rerouting path planning based on artificial potential field and genetic algorithm. Applied Mechanics and Materials, 183(182), 1413–1417.
  Google Scholar
• Wu, Z. Y., Hu, G., Feng, L., & Wu, J. P. (2016). Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling. Assembly Automation, 36(3), 318–332. https://doi.org/10.1108/AA-01-2016-008/full/html.
  Article Google Scholar
• Yang, Q., Chen, W. N., Li, Y., Chen, C. L. P., Xu, X. M., & Zhang, J. (2017). Multimodal estimation of distribution algorithms. IEEE Transactions on Cybernetics, 47(3), 636–650. https://doi.org/10.1109/TCYB.2016.2523000.
  Article Google Scholar
• Yang, X., Yang, W., Zhang, H. J., Chang, H., Chen, C. Y., & Zhang, S. C. (2016). A new method for robot path planning based artificial potential field. In The 11th IEEE conference on industrial electronics and application. ICIEA (pp. 1294–1299). https://ieeexplore.ieee.org/document/7603784.
• Yao, Y., Zhou, X. S., Zhang, K. L., & Dong, D. (2010). Dynamic trajectory planning for unmanned aerial vehicle based on spare A* search and improved artificial potential field. Control Theory and Applications, 27(7), 953–959. in Chinese.
  Google Scholar
• Zhang, X. W., & Hu, Y. B. (2019). Multiconstrained routing based on artificial bee colony algorithm and dynamic fireworks algorithm. Journal of Physics: Conference Series, 1237, 022–058. https://doi.org/10.1088/1742-6596/1237/2/022058.
  Article Google Scholar
• Zhang, X. G., & Mahadevan, S. (2017). Aircraft re-routing optimization and performance assessment under uncertainty. Decision Support Systems, 96, 67–82. https://doi.org/10.1016/j.dss.2017.02.005.
  Article Google Scholar

Download references