參考文獻 |
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Amaruchkul, K., Cooper, W. L., & Gupta, D. (2007). Single-Leg Air-Cargo Revenue Management. Transportation Science, 41(4), 457–469. https://doi.org/10.1287/trsc.1060.0177
Azadian, F., Murat, A. E., & Chinnam, R. B. (2012). Dynamic routing of time-sensitive air cargo using real-time information. Transportation Research Part E: Logistics and Transportation Review, 48(1), 355–372. https://doi.org/10.1016/j.tre.2011.07.004
Baxter, G., & Srisaeng, P. (2018). THE USE OF AN ARTIFICIAL NEURAL NETWORK TO PREDICT AUSTRALIA’S EXPORT AIR CARGO DEMAND. INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING, 8(1), 15–30. https://doi.org/10.7708/ijtte.2018.8(1).02
Boeing: Commercial Market Outlook. (2020, January). http://www.boeing.com/commercial/market/commercial-market-outlook/index.page
Cao, J., & Kanafani, A. (1997). Real‐time decision support for integration of airline flight cancellations and delays part I: Mathematical formulation. Transportation Planning and Technology, 20(3), 183–199. https://doi.org/10.1080/03081069708717588
Chen, S.-C., Kuo, S.-Y., Chang, K.-W., & Wang, Y.-T. (2012). Improving the forecasting accuracy of air passenger and air cargo demand: The application of back-propagation neural networks. Transportation Planning and Technology, 35(3), 373–392. https://doi.org/10.1080/03081060.2012.673272
Derigs, U., & Friederichs, S. (2013). Air cargo scheduling: Integrated models and solution procedures. OR Spectrum, 35(2), 325–362. https://doi.org/10.1007/s00291-012-0299-y
Feng, B., Li, Y., & Shen, Z.-J. M. (2015). Air cargo operations: Literature review and comparison with practices. Transportation Research Part C: Emerging Technologies, 56, 263–280. https://doi.org/10.1016/j.trc.2015.03.028
Freisleben, B., & Gleichmann, G. (1993). Controlling airline seat allocations with neural networks. [1993] Proceedings of the Twenty-Sixth Hawaii International Conference on System Sciences, iv, 635–642. https://doi.org/10.1109/HICSS.1993.284243
Huang, S.-H. S., & Hsu, W.-K. K. (2016). Evaluating the service requirements of combination air cargo carriers. Asia Pacific Management Review, 21(1), 1–8. https://doi.org/10.1016/j.apmrv.2015.05.001
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31–44. https://doi.org/10.1109/2.485891
Kalogirou, S. A. (2014). Designing and Modeling Solar Energy Systems. In Solar Energy Engineering (pp. 583–699). Elsevier. https://doi.org/10.1016/B978-0-12-397270-5.00011-X
Kasilingam, R. G. (1997). An economic model for air cargo overbooking under stochastic capacity. Computers & Industrial Engineering, 32(1), 221–226. https://doi.org/10.1016/S0360-8352(96)00211-2
Kupfer, F., Meersman, H., Onghena, E., & Van de Voorde, E. (2017). The underlying drivers and future development of air cargo. Journal of Air Transport Management, 61, 6–14. https://doi.org/10.1016/j.jairtraman.2016.07.002
Lange, A. (2019). Does cargo matter? The impact of air cargo operations on departure on-time performance for combination carriers. Transportation Research Part A: Policy and Practice, 119, 214–223. https://doi.org/10.1016/j.tra.2018.10.005
Law, R., & Au, N. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 9.
Liu, J., Ding, L., Guan, X., Gui, J., & Xu, J. (2020). Comparative analysis of forecasting for air cargo volume: Statistical techniques vs. machine learning. Journal of Data, Information and Management, 2(4), 243–255. https://doi.org/10.1007/s42488-020-00031-1
Liu, Y., Yin, M., & Hansen, M. (2019). Economic costs of air cargo flight delays related to late package deliveries. Transportation Research Part E: Logistics and Transportation Review, 125, 388–401. https://doi.org/10.1016/j.tre.2019.03.017
Mongeau, M., & Bes, C. (2003). Optimization of aircraft container loading. IEEE Transactions on Aerospace and Electronic Systems, 39(1), 140–150. https://doi.org/10.1109/TAES.2003.1188899
Niu, B., Dai, Z., & Zhuo, X. (2019). Co-opetition effect of promised-delivery-time sensitive demand on air cargo carriers’ big data investment and demand signal sharing decisions. Transportation Research Part E: Logistics and Transportation Review, 123, 29–44. https://doi.org/10.1016/j.tre.2019.01.011
Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. 1990 IJCNN International Joint Conference on Neural Networks, 163–168 vol.2. https://doi.org/10.1109/IJCNN.1990.137710
Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23–38. https://doi.org/10.1109/34.655647
Totamane, R., Dasgupta, A., & Rao, S. (2014). Air Cargo Demand Modeling and Prediction. IEEE Systems Journal, 8(1), 52–62. https://doi.org/10.1109/JSYST.2012.2218511
Wang, Y.-J., & Kao, C.-S. (2008). An application of a fuzzy knowledge system for air cargo overbooking under uncertain capacity. Computers & Mathematics with Applications, 56(10), 2666–2675. https://doi.org/10.1016/j.camwa.2008.02.049
Wong, W. H., Zhang, A., Van Hui, Y., & Leung, L. C. (2009). Optimal Baggage-Limit Policy: Airline Passenger and Cargo Allocation. Transportation Science, 43(3), 355–369. https://doi.org/10.1287/trsc.1090.0266
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7 |