Barth, M., Younglove, T., & Scora, G. (2005). Development of a Heavy-Duty Diesel Modal Emissions and Fuel Consumption Model.
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. https://doi.org/10.1287/opre.1030.0065
Cai, L., Li, W., Zhou, B., Li, H., & Yang, Z. (2024). Robust multi-equipment scheduling for U-shaped container terminals concerning double-cycling mode and uncertain operation time with cascade effects. Transportation Research Part C, 158(September 2023), 104447. https://doi.org/10.1016/j.trc.2023.104447
Chargui, K., Zouadi, T., & Sreedharan, V. R. (2023). Computers and Operations Research Berth and quay crane allocation and scheduling problem with renewable energy uncertainty : A robust exact decomposition. Computers and Operations Research, 156(July 2022), 106251. https://doi.org/10.1016/j.cor.2023.106251
Chargui, K., Zouadi, T., Sreedharan, V. R., Fallahi, A. El, & Reghioui, M. (2023). A novel robust exact decomposition algorithm for berth and quay crane allocation and scheduling problem considering uncertainty and energy efficiency ✩. Omega, 118, 102868. https://doi.org/10.1016/j.omega.2023.102868
Demir, E., Bektaş, T., & Laporte, G. (2011). A comparative analysis of several vehicle emission models for road freight transportation. Transportation Research Part D: Transport and Environment, 16(5), 347–357. https://doi.org/10.1016/j.trd.2011.01.011
Fereidoonian, F., Sadjadi, S. J., Heydari, M., & Mirzapour Al-e-Hashem, S. M. J. (2024). A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminals. Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment. https://doi.org/10.1177/14750902231225694
Jian, W., Zhu, J., & Zeng, Q. (2021). An Optimization Model of Integrated AGVs Scheduling and Container Storage Problems for Automated Container Terminal Considering Uncertainty. Symmetry, 13(10), 1904.
Liu, J., Yu, B., Shan, W., Yao, B., & Sun, Y. (2021). Optimizing the container truck paths with uncertain travel time in container ports. Transport, 36(6), 444-462.
Liu, W., Zhu, X., Wang, L., Yan, B., & Zhang, X. (2021). Optimization approach for yard crane scheduling problem with uncertain parameters in container terminals. Journal of Advanced Transportation, 2021(1), 5537114.
Ma, S., Li, H., Zhu, N., & Fu, C. (2021). Stochastic programming approach for unidirectional quay crane scheduling problem with uncertainty. In Journal of Scheduling (Vol. 24). https://doi.org/10.1007/s10951-020-00661-8
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455–465. https://doi.org/10.1016/j.amc.2009.03.037
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20. https://doi.org/10.1016/j.fss.2012.04.010
Rodrigues, F., & Agra, A. (2021). An exact robust approach for the integrate d b erth allocation and quay crane scheduling problem under uncertain arrival times. European Journal of Operational Research, (xxxx). https://doi.org/10.1016/j.ejor.2021.03.016
Rodrigues, F., & Agra, A. (2022). Berth allocation and quay crane assignment/scheduling problem under uncertainty: A survey. European Journal of Operational Research, 303(2), 501–524. https://doi.org/10.1016/j.ejor.2021.12.040
Rodrigues, F., & Agra, A. (2024). Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model. International Transactions in Operational Research, 31(2), 721–748. https://doi.org/10.1111/itor.13325
Shang, X. T., Cao, J. X., & Ren, J. (2016). A robust optimization approach to the integrated berth allocation and quay crane assignment problem. Transportation Research Part E: Logistics and Transportation Review, 94, 44–65. https://doi.org/10.1016/j.tre.2016.06.011
Wang, W., Lin, S., & Zhen, L. (2023). Computers & Industrial Engineering Flexible storage yard management in container terminals under uncertainty. Computers & Industrial Engineering, 186(May), 109753. https://doi.org/10.1016/j.cie.2023.109753
Wu, Y., & Miao, L. (2020). A robust scheduling model for continuous berth allocation problem under uncertainty. Proceedings - 2020 5th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2020, 43–49. https://doi.org/10.1109/ICECTT50890.2020.00017
Yu, H., Ge, Y. E., Chen, J., Luo, L., Tan, C., & Liu, D. (2017). CO2 emission evaluation of yard tractors during loading at container terminals. Transportation Research Part D: Transport and Environment, 53, 17–36. https://doi.org/10.1016/j.trd.2017.03.014
Yu, H., Ning, J., Wang, Y., He, J., & Tan, C. (2021). Flexible yard management in container terminals for uncertain retrieving sequence. Ocean and Coastal Management, 212(March), 105794. https://doi.org/10.1016/j.ocecoaman.2021.105794
Zelany, M. (1974). A concept of compromise solutions and the method of the displaced ideal. Computers and Operations Research, 1(3–4), 479–496. https://doi.org/10.1016/0305-0548(74)90064-1
Zhang, H., Qi, L., Luan, W., & Ma, H. (2022). Double-cycling AGV scheduling considering uncertain crane operational time at container terminals. Applied Sciences, 12(10), 4820.
Zhang, X., Li, R., Wang, C., Xue, B., & Guo, W. (2024). Engineering Applications of Artificial Intelligence Robust optimization for a class of ship traffic scheduling problem with uncertain arrival and departure times. Engineering Applications of Artificial Intelligence, 133(PC), 108257. https://doi.org/10.1016/j.engappai.2024.108257
Zheng, H., Wang, Z., & Liu, H. (2023). The integrated rescheduling problem of berth allocation and quay crane assignment with uncertainty. Processes, 11(2), 522.
Zhong, R., Wen, K., Fang, C., & Liang, E. (2022). Real-time multi-resource jointed scheduling of container terminals with uncertainties using a reinforcement learning approach. ASCC 2022 - 2022 13th Asian Control Conference, Proceedings, (Ascc), 110–115. https://doi.org/10.23919/ASCC56756.2022.9828161