Green Truck Appointment System at Container Terminals: Overview and Research Opportunities

被引:1
|
作者
Lin, Xin [1 ]
Liu, Ruitong [2 ]
Lin, Fuxin [1 ]
Qin, Zhiqi [1 ,3 ]
Jin, Beilei [4 ]
Xiao, Xisheng [5 ]
机构
[1] Zhejiang Univ, Sch Management, Hangzhou 310058, Peoples R China
[2] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China
[3] Dalian Univ Technol, Sch Econ & Management, Dalian 116024, Peoples R China
[4] China Jiliang Univ, Coll Econ & Management, Hangzhou 310018, Peoples R China
[5] Guizhou Light Ind Polytech Coll, Bigdata Technol Inst, Guiyang 550025, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Truck Appointment; Green Seaport; Environmental Cost; Quantifying Pollution Methodology; PORTS;
D O I
10.1109/CCDC52312.2021.9602170
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The increase in international trade causes a sharp increase in the business of container terminals, with the growing of traffic congestion and environmental pollution in seaport surroundings. Among numerous research directions, truck appointment system (TAS) has gradually become a research hotspot due to its advantages in reasonable planning of truck arrival and optimization of scheduling. In addition, with the increasing awareness of environmental protection all over the world, the green seaport problem and its subproblem, the green truck appointment problem, gradually get the attention of academics. In this paper, the current status of research in green truck appointment problem is presented in the form of a comprehensive literature review which divides papers into three aspects: traditional TAS's contribution to green seaports, green TAS's methodologies of quantifying environmental costs, and coordinated optimization. By means of analyzing and comparing their strengths and weaknesses, valuable research directions in measurement of environmental cost are proposed, such as enriching pollutant types, more precise measurement of pollution emission, emission trading, etc. In addition, this paper also suggests that future research work should focus on data-driven green TAS modeling method and efficient algorithm design, so as to provide reference for the application transformation of research results.
引用
收藏
页码:4227 / 4232
页数:6
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