TY - JOUR
T1 - Yard truck retrofitting and deployment for hazardous material transportation in green ports
AU - Zhang, Qian
AU - Wang, Shuaian
AU - Zhen, Lu
N1 - Funding Information:
The authors thank the editor-in-chief, the associate editor, and the reviewers for their valuable comments and suggestions, which have greatly improved the quality of this paper. This work is supported by the National Natural Science Foundation of China (grand numbers 71831008, 72025103, and 72071173).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - In the design of green ports, the strategic decision on what types of container transportation equipment are appropriate is extremely important. Yard trucks (YTs) are indispensable in container transportation. In this paper, we propose a YT retrofitting and deployment problem that considers hazardous material transportation in green ports. A stochastic mixed-integer programming model is developed to minimize the costs of purchasing, retrofitting, and chartering YTs and the operation costs during the planning horizon. An enhanced Benders decomposition based on a Lagrangian relaxation algorithm is developed to solve the model. We conduct numerical experiments to verify the effectiveness of the proposed algorithms. We find that the larger free carbon emission quotas provided to enterprises by the government are not always an optimum solution. This research also provides suggestions that can inform decisions about YT retrofitting and deployment and that can contribute to the sustainable development of green ports.
AB - In the design of green ports, the strategic decision on what types of container transportation equipment are appropriate is extremely important. Yard trucks (YTs) are indispensable in container transportation. In this paper, we propose a YT retrofitting and deployment problem that considers hazardous material transportation in green ports. A stochastic mixed-integer programming model is developed to minimize the costs of purchasing, retrofitting, and chartering YTs and the operation costs during the planning horizon. An enhanced Benders decomposition based on a Lagrangian relaxation algorithm is developed to solve the model. We conduct numerical experiments to verify the effectiveness of the proposed algorithms. We find that the larger free carbon emission quotas provided to enterprises by the government are not always an optimum solution. This research also provides suggestions that can inform decisions about YT retrofitting and deployment and that can contribute to the sustainable development of green ports.
KW - Enhanced Benders decomposition based on Lagrangian relaxation
KW - Green ports
KW - Stochastic programming
KW - Yard truck retrofitting and deployment
UR - http://www.scopus.com/inward/record.url?scp=85123095776&partnerID=8YFLogxK
U2 - 10.1007/s10479-021-04507-0
DO - 10.1007/s10479-021-04507-0
M3 - Journal article
AN - SCOPUS:85123095776
SN - 0254-5330
JO - Annals of Operations Research
JF - Annals of Operations Research
ER -