2022世界交通运输大会

港航运输规划与管理论坛

一、论坛背景

本论坛主要围绕港航资源分配与调度问题展开，包括泊位分配问题（Berth allocation problem）、堆场空间分配问题（Yard storage space allocation problem）、船舶舱位分配问题（Ship slot allocation problem）等。这个研究主题是港航运输规划与管理领域的热点问题之一。

二、论坛总览

时间（Time）：2022年11月6日，13:30-17:00

地点（Location）：武汉中国光谷科技会展中心三层会议厅3-3（Function 3-3）

主题（Theme）

港航资源分配与调度

Resources Allocating and Scheduling in Harbor & Navigation

主持人（Moderator）

郑建风 

大连海事大学

Zheng Jianfeng

 Dalian Maritime University

三、组织机构

主办单位  Host

WTC水上运输学部

WTC Waterborne Transportation

大连海事大学

Dalian Maritime University

四、论坛议程

13:30  Prescriptive analytics for transport system management: state-of-the-art development

报告人：王帅安 香港理工大学

Reporter：Wang Shuai'an The Hong Kong Polytechnic University

13:50  受潮汐限制的内河航道实时驳船调度

Real-time barge scheduling in a river-to-sea channel with tidal restrictions

报告人：金建钢 上海交通大学

Reporter：Jin Jiangang Shanghai Jiaotong University

14:10  Optimal compliance portfolio for a fleet of vessels under the sulphur regulation

报告人：盛典 华中科技大学

Reporter：Sheng Dian Huazhong University of Science and Technology

14:30  An improved Benders decomposition method for stochastic yard template planning in container terminals

报告人：胡鸿韬 上海海事大学，副院长

Reporter：Hu Hongtao Shanghai Maritime University，Vice dean

14:50  Data fusion and machine learning for ship fuel/emission efficiency analysis

报告人：杜玉泉 塔斯马尼亚大学

Reporter：Du Yuquan University of Tasmania

15:10  集装箱班轮的舱位分配问题研究

Dynamic Container Slot Allocation for a Liner Shipping Service

报告人：汪挺松 上海大学

Reporter：Wang Tingsong Shanghai University

15:30  茶歇  Tea Break

16:00  集装箱船舶多贝位并行装船作业优化建模

Modeling of containerships multiple-bays parallel loading operations

报告人：祝慧灵 大连海事大学

Reporter：Zhu Huiling Dalian Maritime University

16:20  多港口泊位分配问题研究

The multi-port berth allocation problem

报告人：郭力铭 大连海事大学

Reporter：Guo Liming Dalian Maritime University

五、专家信息

郑建风 大连海事大学

Zheng Jianfeng Dalian Maritime University

专家简介（Introduction）

郑建风，大连海事大学交通运输工程学院教授、博士生导师。在北京交通大学获得学士和博士学位，在新加坡国立大学做两年博士后。在Transportation Science, Transportation Research Part B/D/E等期刊上发表50余篇SCI/SSCI期刊论文。主持国家自然科学基金青年基金、面上、重点子课题等项目。

王帅安 香港理工大学

Wang Shuai'an The Hong Kong Polytechnic University

专家简介（Introduction）

Dr. Wang is a Professor at the Hong Kong Polytechnic University (PolyU). Prior to joining PolyU, he worked at Old Dominion University, USA, and at the University of Wollongong, Australia. Dr. Wang dedicates to rethinking and proposing innovative solutions to improve the efficiency of maritime and urban transportation systems, to promote environmentally friendly and sustainable practices, and to transform business and engineering education. https://sites.google.com/site/wangshuaian/home

报告题目（Report）

Prescriptive analytics for transport system management: state-of-the-art development

报告精华（Report Summary）

With the development of machine learning algorithm, the advancement of computing equipment, and the increased availability of transportation data, the literature on data-driven optimization for transportation management has been rapidly expanding. Most studies have adopted a two-step approach: in the first step, machine learning models are used to predict the values of uncertain parameters; in the second step, the predicted values are used as input for an optimization model to derive a decision. However, such a two-step approach is flawed in several aspects. In this seminar, we present recent methodological developments that address the deficiencies of the two-step approach. Hopefully, the theoretical advances can be applied by transportation engineers to solve practical problems.

金建钢 上海交通大学

Jin Jiangang Shanghai Jiaotong University

专家简介（Introduction）

金建钢，上海交通大学船舶海洋与建筑工程学院教授、博士生导师。分别于清华大学和新加坡国立大学获得学士和博士学位。曾任新加坡-麻省理工学院研究与科技联盟研究助理。专注于大规模组合优化、整数规划和网络优化等运筹优化方法在交通和物流系统中的应用研究。在Transportation Science, Transportation Research Part A/B/C/E等期刊上发表40余篇SCI/SSCI期刊论文。主持4项国家自然科学基金项目。入选交通部交通运输青年科技英才、上海市启明星计划、上海市晨光计划。获美国运筹学与管理科学学会（INFORMS）铁路优化竞赛一等奖、新加坡国立大学校长奖。担任国际期刊Computers & Industrial Engineering领域编辑。https://naoce.sjtu.edu.cn/teachers/5745.html

报告题目（Report）

受潮汐限制的内河航道实时驳船调度

Real-time barge scheduling in a river-to-sea channel with tidal restrictions

报告精华（Report Summary）

内河码头驳船船队调度受到码头装卸流程、航道内船舶航行条件、拖船与驳船的匹配等众多因素影响，船队作业计划需要协同考虑码头作业、潮汐影响、浅滩通航条件，不合理的作业计划会大幅降低运输效率，如何制定复杂航道条件下船队配置和作业调度计划具有较大挑战。本研究针对中远海运某矿石运输项目的码头过驳转运，开展驳船船队调度优化研究。采用运筹优化、仿真优化等方法开展码头-内河驳运-海上中转的整个内河驳运作业过程的资源配置和调度优化，评估不同船型、船队规模下的船队运输能力，为项目在船舶选型、船队配置决策方面提供定量科学的决策支持。

The scheduling of barge transportation in narrow channels is critical to transportation efficiency, especially when the tide exits and affects the channel navigation of barges. Barge channel movements are frequently correlated with other activities (e.g., berth allocation) and must thus be considered concurrently. To enhance transportation efficiency, effective scheduling algorithms are required. In this study, we examine the real-time barge scheduling problem arising from a river-to-sea channel with tidal restrictions in the real world, where barges' loading and unloading operations are placed at both ends of the navigation channel and must be planned and coordinated with their channel movements. To reduce the overall completion time for all barges, we develop a mixed integer linear programming model with several effective valid inequalities. A straightforward yet efficient variable neighborhood search algorithm is proposed. Real-world case studies based on a barge transportation project in Boffa, Guinea are conducted. It is demonstrated that the proposed approach is applicable for real-time decision-making and effective in assisting barges avoid missing feasible tide windows by slightly altering the first-come-first-serve schedule of the barges. With the proposed approach, the size and speed of the barge fleet can be optimized, and the tidal influence is also evaluated quantitatively.

盛典 华中科技大学

Sheng Dian，Huazhong University of Science and Technology

专家简介（Introduction）

Dr. Sheng is an Associate Professor at the HuaZhong University of Science and Technology (HUST). Prior to joining HUST, he worked at National University of Singapore as a research fellow. Dr. Sheng dedicates to transportation economics studies in aviation and maritime shipping. http://cm.hust.edu.cn/info/1770/28818.htm

报告题目（Report）

Optimal compliance portfolio for a fleet of vessels under the sulphur regulation

报告精华（Report Summary）

As of January 2020, the global sulphur cap on maritime bunkers is reduced from 3.50% to 0.50%, which applies to all vessels sailing outside the emission control areas. The new sulphur limit, imposed by the IMO, could certainly cause huge extra costs for the shipping sector. This paper investigates how an ocean carrier can adapt to the latest sulphur regulation by choosing between sulphur scrubber (SS) and fuel switching (FS) for its vessels. An annual cost minimization model is proposed to determine the optimal compliance choices, considering factors such as prices of complaint fuels, capital cost difference between SS and FS vessels, and shipping service profile (e.g., SECA ratio of the voyage, fuel consumption, port time). Analytical results are derived, allowing us to visualize the interplay among different factors simultaneously. It is found that the optimal compliance choice depends on the tradeoff between the capital cost advantage of FS and the fuel cost advantage of SS, and such tradeoff is moderated by the vessel’s annual fuel consumption. Ignoring the speed differentiation behavior of FS vessels would certainly exaggerate the benefits of SS vessels. But whether it can lead to a wrong compliance choice depends on the extent of overestimation, which only depends on the SECA ratio and the fuel prices of MGO and VLSFO. When fuel price volatility is factored in, a portfolio of FS and SS might be better than simply choosing either one for all its vessel fleet. The triggering condition for the compliance portfolio is identified. The results highlight the importance of using a sulphur compliance portfolio to hedge the risk of fuel price volatility.

胡鸿韬 上海海事大学，副院长

Hu Hongtao Shanghai Maritime University，Vice dean

专家简介（Introduction）

胡鸿韬，教授，博导，上海市曙光学者，上海市青年东方学者，上海市浦江人才。获复旦大学和上海交通大学学士和博士学位，并在新加坡国立大学担任研究员2年。现为上海海事大学物流工程学院副院长、物流工程与管理硕士点负责人，上海市重点在线课程《运筹学》与《生产线建模与仿真》负责人。主要研究方向是港航、物流及供应链的管理与优化，近年来发表SCI或SSCI源国际期刊论文30篇（第一作者16篇，JCR一区10篇），编写著作2部。主持和参与了多项科研课题，主持国家自然科学基金项目3项，省部级项目1项，市局级项目1项。获新加坡国际海事奖（Singapore International Maritime Awards）金奖（最高奖）。担任国际期刊Asia-Pacific Journal of Operational Research领域编辑。https://ls.shmtu.edu.cn/2021/0107/c7763a66468/page.htm

报告题目（Report）

An improved Benders decomposition method for stochastic yard template planning in container terminals

报告精华（Report Summary）

This research presents a two-stage stochastic programming model for yard template planning, with the aim of minimizing the maintenance and manpower costs of activating yard cranes for container handling and the carbon dioxide emitted from yard trucks. An improved Benders decomposition-based solution method is designed to solve the proposed model with large-scale problem instances. Several experiments were performed to validate the effectiveness of the model and the efficiency of the method.

杜玉泉 塔斯马尼亚大学

Du Yuquan University of Tasmania

专家简介（Introduction）

Dr Yuquan (Bill) Du is a Senior Lecturer of Transport, Logistics, and Supply Chain Management in the Centre for Maritime and Logistics Management, Australian Maritime College (AMC), University of Tasmania (UTAS). His current research concentrates on applying machine learning and optimisation approaches to the decision-making problems in transport and logistics systems. He is a highly cited researcher in the area of global freight transport and logistics studies. Some of his studies have gained high academic or industrial reputation, such as INFORMS President’s Pick (2015), and Industry Mention of IBM’s Optimisation Team on CPLEX. One of his papers also ranks in the MOST CITED ARTICLES since 2011 of Transportation Research Part E. He is the recipient of AMC Service Award (June 2021). He has been the Lead Applicant/Chief Investigator/Academic Collaborator of research projects with a total funding in excess of $1 million. https://www.utas.edu.au/profiles/staff/amc/yuquan-du

报告题目（Report）

Data fusion and machine learning for ship fuel/emission efficiency analysis

报告精华（Report Summary）

The shipping industry is concerned about ship fuel/energy efficiency due to the motivation of saving bunker fuel cost and mitigating ship emissions. A foundation for various energy/emission-efficient measures is the accurate quantification of bunker fuel consumption of a ship in one day or hour given its sailing speed, draft/displacement, trim, weather conditions, and sea conditions. This study takes advantage of four industry data sources including voyage report data, AIS data, sensor data, and meteorological data, and fuses these data sources to find the best datasets for ship fuel efficiency analysis. Based on fused datasets, we experimented with state-of-the-art machine learning models to quantify a ship’s daily/hourly bunker fuel consumption, over eight 8,100-TEU to 14,000-TEU containerships of a global shipping company. When voyage report data is used as the basis for ship fuel/emission analysis, meteorological data and AIS data can be combined into voyage report data to improve the data quality. The fit errors of best machine learning models over the recommended datasets are normally within 5 ton/day, and can be as low as less than 1 ton/day. When sensor data is considered, combining meteorological data (waves, sea currents, sea water temperature) into sensor data will significantly improve the modeling accuracy. The best machine learning models achieve their R2 at 0.999 or 1.000 on the training sets, and their R2 values over the test sets are also all above 0.966. Their fit errors are below 0.75 ton/day (RMSE), or below 0.52 ton/day (MAE). The proposed datasets and models would be useful for sailing speed optimization, trim optimization, weather routing, voyage planning, and virtual (just-in-time) arrivals. We also published our computer code in Python and trained machine learning models in GitHub which is accessible to the public.

汪挺松 上海大学

Wang Tingsong Shanghai University

专家简介（Introduction）

汪挺松，上海大学管理学院教授。2012年1月博士毕业于新加坡国立大学。汪挺松教授主要从事港航系统的低碳减排与运营管理研究，论文发表在交通科学和运筹优化研究领域主流学术期刊，包括Transportation Science、Transportation Research Part B/E、European Journal of Operational Research等。https://ms.shu.edu.cn/info/1261/15983.htm

报告题目（Report）

集装箱班轮的舱位分配问题研究

Dynamic Container Slot Allocation for a Liner Shipping Service

报告精华（Report Summary）

In this paper, we study a dynamic container slot allocation problem (DCSAP) for a liner container shipping company that aims to make an acceptance or rejection decision to each dynamically arriving container slot booking request. To capture the dynamic arrival feature and real-time acceptance/rejection decision of the booking request, we formulate the DCSAP as a dynamic programming (DP) model with the objective of maximizing the total revenues generated by accepted container booking requests over the entire booking horizon. As the well-known curse of dimensionality of solving a DP model, we develop a series of models to transform the intractable DP model into a solvable approximate linear programming model. We further propose a spatiotemporal-heterogeneity-based (STH-based) decomposition solution method to solve the approximate linear programming model by identifying the spatiotemporal property of the DCSAP. Extensive numerical experiments are conducted to assess the applicability of the developed research methodology.

祝慧灵 大连海事大学

Zhu Huiling Dalian Maritime University

专家简介（Introduction）

祝慧灵，女，博士，讲师。2018年毕业于大连海事大学物流工程与管理专业，获得工学博士学位。2018年12月-2021年4月在大连海事大学管理科学与工程博士后流动站工作。目前为大连海事大学交通运输工程学院物流系专任教师，主要研究方向为航运系统优化。主持国家自然科学基金青年基金1项，博士后基金1项，参与自然科学基金、省部级课题10余项；获得省部级奖励1项；出版专著1部；在《Naval Research Logistics》、《Applied Mathematical Modelling》等SCI、EI、CSSCI检索期刊发表论文10余篇。http://jt.dlmu.edu.cn/info/1041/2971.htm

报告题目（Report）

集装箱船舶多贝位并行装船作业优化建模

Modeling of containerships multiple-bays parallel loading operations

报告精华（Report Summaries）

本研究考虑了集装箱码头船舶多贝位并行装船作业过程。根据所需的船舶贝位和初始堆场空间布局，同时决策最优装船顺序和翻倒落箱位置。本文建立了一个整数线性规划模型，在船舶贝位无倒箱的基础上，最大限度地减少堆场空间内的倒箱次数。使用 CPLEX 在小规模场景下测试模型的准确性。考虑到现实世界中的问题规模，本文提出了一种启发式方法，该方法与描述提取、装船和翻倒操作的数学模型相结合。最后，本文并将启发式算法与文献中的其他算法进行了比较。 大量的数值实验表明了所提出的启发式算法的有效性。

This study considers the containerships multiple-bays parallel loading process in container terminals. The optimal loading sequence and relocation location are simultaneously decided on the basis of the desired ship-bay and initial yard space configuration. An integer linear programming model is developed to minimize the number of relocations in the yard space on the basis of no shifts in the ship bay. The accuracy of the model is tested on small-scale scenarios by using CPLEX. Considering the problem size in the real world, this study presents a heuristic method that is combined with a mathematical model for the removal, loading, and relocation operations. Finally, the heuristic algorithm is compared with other algorithms in the literature. The extensive numerical experiments show the efficiency of the proposed heuristic algorithm.

郭力铭 大连海事大学

Guo Liming Dalian Maritime University

专家简介（Introduction）

郭力铭，大连海事大学物流工程与管理专业博士研究生。主要研究方向为集装箱码头物流调度优化。在交通运输领域国内外顶级与权威期刊Transportation Research Part E、Computer & Industry Engineering、交通运输系统工程与信息等上发表多篇论文，参与国家自然科学基金项目、省部级课题等3项。

报告题目（Report）

多港口泊位分配问题研究

The multi-port berth allocation problem

报告精华（Report Summary）

研究了合作环境下的多港口泊位分配问题（MPBAP），目标是为到达任意相邻港口的船舶确定停泊时间和停泊位置。以往的MPBAP认为港口间已经建立了稳定的合作关系，而忽略了港口合作稳定性问题（PCSP）。对此，本文不仅在MPBAP中考虑了PCSP，同时也整合了船舶转移问题，即在港等待时间过长的船舶可以被转移到邻近的港口。对于PCSP，我们研究如何将多个相邻港口分成一些的稳定港口组，然后从中选择最佳港口组。对于所有可能的港口组，我们提出了一个关于MPBAP的混合整数规划模型，并设计了列生成方法来求解。基于所有港口组的关于MPBAP的最优解，利用合作博弈理论来评价每个港口组的稳定性，然后构建成本最小化的最优港口组选择模型。

主题论坛名称

01  中德可持续交通论坛

02  “一带一路”国际交通研讨会——国际化交通人才培养与提升

03  加快建设交通强国论坛

04  第三届交通运输厅长论坛

05  中国交通企业经营管理论坛

06  交通与能源融合发展论坛

07  交通物流融合发展论坛

08  第六届耐久性路面研究论坛

09  第二届特种道面铺装国际论坛

10  公路路面智慧管养技术论坛

11  高性能功能型路面发展论坛

12  道路工程新材料论坛

13  道路工程可持续化发展论坛

14  公路工程耐久性论坛

15  绿色与安全道路交通学术论坛

16  第五届桥梁发展论坛

17  第二届桥梁结构健康与安全论坛

18  第二届桥梁美学论坛

19  高铁桥梁智能建造和绿色发展论坛

20  世界人行桥与景观桥创新发展论坛

21  大跨度桥梁建设技术进展论坛

22  桥梁疲劳断裂和长寿命设计与运维论坛

23  水中悬浮隧道科技论坛

24  隧道智能建造技术论坛

25  隧道与地下工程防灾减灾技术论坛

26  复杂环境隧道灾害控制理论与方法论坛

27  国家公交都市发展论坛

28  2022中国公路学会学术年会

29  交通运输行业数字化转型论坛——打造智慧交通生命体

30  数字公路产业化发展论坛

31  车路协同智慧交通国际论坛

32  空间信息技术赋能交通基础设施发展论坛

33  智慧路网论坛

34  交通基础设施工业化智能建造论坛

35  第六届BIM技术在交通领域的推广应用论坛

36  公路设施数字化建设与运维论坛

37  智能交通基建设施与安全管控技术论坛

38  陆路交通基础设施低影响生态融合与智能化技术论坛

39  寒区交通基础设施智能化与安全保障技术论坛

40  数字化施工与管理技术论坛

41  智能驱动新型基础设施建养高品质发展论坛

42  第五届高速公路出行服务论坛

43  新一代航运系统论坛

44  国际运输与物流论坛

45  港航运输规划与管理论坛

46  港口群与智慧管理论坛

47  第三届真空管道磁浮交通国际学术论坛  ☞查看议程

48  高速铁路线路工程结构服役安全国际论坛

49  航空交通运输前沿技术论坛

50  智慧机场与可持续发展技术论坛

51  交通交叉学科前沿论坛

52  第三届交通与旅游融合发展论坛

53  第二届路衍经济产业发展论坛

54  迈向零碳交通：科学前沿与实践探索论坛

55  面向交通碳中和的科技创新与产业发展论坛

56  低碳与低影响绿色交通技术论坛

57  综合立体交通系统与出行行为研究前沿与趋势论坛  ☞查看议程

58  综合立体交通多网融合规划设计与运行组织论坛

59  交通基础设施投融资发展与风险防范论坛

60  全国交通建设项目管理创新论坛

61  第二届世界交通科技期刊论坛

62  WTC2022国际学生论坛

63  桥梁工程优秀博士硕士论文论坛

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