Abstract
The International Maritime Organization (IMO) has proposed to impose the carbon taxation on ports in the long term. The implementation of the carbon taxation policy will increase the operating cost and decrease its efficiency. Therefore, there is a trade-off between improving port operational efficiency and reducing carbon emission in ports. This study investigates the berth and quay cranes assignment planning, which aims at minimizing the total delay completion cost of all tasks, the total operating cost of quay cranes and the carbon taxation cost. In order to reflect the reality, some important uncertain factors are also considered. A recoverable robustness berth and quay crane assignment planning model is proposed. Numerical experiments are performed to demonstrate the applicability of the proposed model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, S., Zhen, L., Zhuge, D.: Dynamic programming algorithms for selection of waste disposal ports in cruise shipping. Transp. Res. Part B Methodol. 108, 235–248 (2018)
Wang, S., Qu, X., Yang, Y.: Estimation of the perceived value of transit time for containerized cargoes. Transp. Res. Part A Policy Pract. 78, 298–308 (2015)
Lee, C.-Y., Song, D.-P.: Ocean container transport in global supply chains: overview and research opportunities. Transp. Res. Part B Methodol. 95, 442–474 (2017)
Zhen, L., Wang, K.: A stochastic programming model for multi-product oriented multi-channel component replenishment. Comput. Oper. Res. 60, 79–90 (2015)
Zhen, L.: Tactical berth allocation under uncertainty. Eur. J. Oper. Res. 247(3), 928–944 (2015)
Geerlings, H., van Duin, R.: A new method for assessing CO2-emissions from container terminals: a promising approach applied in Rotterdam. J. Clean. Prod. 19(6), 657–666 (2011)
Wang, T., Wang, X., Meng, Q.: Joint berth allocation and quay crane assignment under different carbon taxation policies. Transp. Res. Part B Methodol. 117, 18–36 (2018)
Zeng, Q., Yang, Z., Hu, X.: Disruption recovery model for berth and quay crane scheduling in container terminals. Eng. Optim. 43(9), 967–983 (2011)
Zhen, L., Lee, L.H., Chew, E.P.: A decision model for berth allocation under uncertainty. Eur. J. Oper. Res. 212(1), 54–68 (2011)
Giallombardo, G., et al.: Modeling and solving the tactical berth allocation problem. Transp. Res. Part B Methodol. 44(2), 232–245 (2010)
Wang, K., et al.: Column generation for the integrated berth allocation, quay crane assignment, and yard assignment problem. Transp. Sci. 52(4), 812–834 (2018)
Zhen, L., et al.: Daily berth planning in a tidal port with channel flow control. Transp. Res. Part B Methodol. 106, 193–217 (2017)
Wang, S., Wang, X.: A polynomial-time algorithm for sailing speed optimization with containership resource sharing. Transp. Res. Part B Methodol. 93, 394–405 (2016)
Shang, X.T., Cao, J.X., Ren, J.: A robust optimization approach to the integrated berth allocation and quay crane assignment problem. Transp. Res. Part E Logist. Transp. Rev. 94, 44–65 (2016)
Iris, Ç., et al.: Integrated berth allocation and quay crane assignment problem: set partitioning models and computational results. Transp. Res. Part E Logist. Transp. Rev. 81, 75–97 (2015)
Chang, D., et al.: Integrating berth allocation and quay crane assignments. Transp. Res. Part E Logist. Transp. Rev. 46(6), 975–990 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, Q., Zhen, L., Xiao, L., Tan, Z. (2019). Recoverable Robustness Considering Carbon Tax in Weekly Berth and Quay Crane Planning. In: Qu, X., Zhen, L., Howlett, R., Jain, L. (eds) Smart Transportation Systems 2019. Smart Innovation, Systems and Technologies, vol 149. Springer, Singapore. https://doi.org/10.1007/978-981-13-8683-1_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-8683-1_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8682-4
Online ISBN: 978-981-13-8683-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)