“双碳”目标下2022大数据时代交通与物流国际会议 (2022 International Conference on Intelligent Transportation and Logistics with Big Data) 暨第十届国际决策科学高峰论坛 (The Tenth International Forum on Decision Sciences)原定于2022年7月2-3日在黑龙江哈尔滨召开，由于当前疫情形势影响，本次会议将采取线上为主的方式进行，如有线下参会者请与会务组联系。

一、主办单位：

中国优选法统筹法与经济数学研究会

船海经济管理专业委员会

二、承办单位：

bat365中国在线平台官方网站

大数据与商务智能技术工信部重点实验室

黑龙江区域创新驱动发展研究中心

黑龙江省管理学学会

三、会议时间：2022年7月2-3日

四、线上会议地址：

主会场：7月2日腾讯会议号193-401-429

7月3日腾讯会议号924-652-135

五、联系人及联系方式：

联系人：魏志飞

电话：17854251893

邮箱：839560942@qq.com

六、会议日程

七、分会场论文报告名单

八、报告专家简介

1.刘心报

刘心报，合肥工业大学教授，国家自然科学基金委创新研究群体负责人，高等学校学科创新引智基地（111计划）负责人，兼任国务院学位委员会第八届管理科学与工程学科评议组成员、中国系统工程学会智能制造系统工程专业委员会主任委员。主要从事复杂产品研制资源优化与调度、复杂产品质量评估与控制等方面的教学与科研工作。主持国家863项目、国家自然科学基金创新研究群体项目、国家自然科学基金重点项目、国家重点研发计划项目课题等国家级科研项目以及企事业单位委托的重要项目，在《Production and Operations Management》《Omega》《European Journal of Operational Research》《系统工程理论与实践》等学术期刊发表学术论文150余篇，在Springer出版社出版专著《Optimization and Management in Manufacturing Engineering》1部。长期坚持将理论研究成果与信息系统技术深度融合，并将其运用到多家大型企业的制造工程管理实践中，取得了显著的经济社会效益，获国家科技进步二等奖2项、教育部自然科学一等奖1项、中国机械工业科技进步一等奖1项、安徽省科技进步一等奖1项；坚持立德树人、科研育人，获国家级教学成果奖二等奖1项。

报告题目：如何在B2C和C2C模式下进行企业内部与外部共享决策？ 

2.李建平

李建平，中国科学院大学特聘教授、中国科学院大学经济与管理学院常务副院长。兼任国际信息技术与量化管理学会（IAITQM）秘书长；中国优选法统筹法与经济数学研究会副理事长、风险管理分会理事长；中国运筹学会决策科学分会理事长；《中国管理科学》主编、《管理评论》主编等。主要研究领域为：风险管理、大数据管理决策。获 “中国青年科技奖”、“全国优秀科技工作者”、“中科院优秀导师奖” 、爱思唯尔中国高被引学者等。在国内外学术期刊上发表论文130余篇，获得省部级自然科学/科技进步奖一等奖2项，二等奖4项。

报告题目：多源数据驱动的财务欺诈风险分析

报告摘要：分析大数据时代对财务欺诈风险分析的管理体制、工具以及决策模式的影响，重点包括多源数据驱动的财务欺诈风险分析的理论、机理、技术和典型案例。首先综述财务欺诈风险分析数据从结构化、半结构化到非结构化的全景式多源大数据的演变，指出了多源数据对财务欺诈风险分析带来的机遇和挑战，分析为了应对更高数据处理要求而提出的欺诈风险分析方法的演变，并总结目前财务欺诈风险分析中仍然面临的主要挑战。

3.余乐安

余乐安，国际系统与控制科学院院士，现为北京化工大学教授、博士生导师。出版专著5部，发表SCI/SSCI论文100余篇，SCI/SSCI引用5000多次，Google Scholar引用10000多次，H指数为56，多篇论文被评为“ESI高被引论文”和/或“ESI热点论文”。先后获得Elsevier中国高被引学者、加拿大研究理事会全球Top 1%高被引学者、中国青年科技奖、教育部自然科学奖一等奖和北京市科学技术奖一等奖等奖励。主要研究领域为商务智能、大数据挖掘、经济预测与金融管理等。

报告题目：大数据与智慧城市治理

报告摘要：随着智慧城市进入国家战略，新型智慧城市建设进程不断推进，全球1000多个城市(其中中国有500多个城市)已把建设智慧城市作为未来发展重点，而大数据的兴起为智慧城市治理提供了有效手段。本报告主要讲述基于北京市政交通一卡通大数据进行智慧城市治理的基本思路与解决方案。报告首先介绍北京市政交通一卡通大数据平台的数据基础与应用平台情况；其次，以公交IC卡刷卡数据等时空行为大数据，介绍了城市大数据感知以及客流预测相关研究结果；最后，基于市政交通一卡通大数据，从政策效果评估、城市规划支撑、特定人群分析和公共交通优化等角度，讲述了基于大数据开展城市治理的具体解决方案与建议。

4.熊熊

熊熊，天津大学管理与经济学部教授、博士生导师，副主任。主要研究领域是大数据金融，计算实验金融学，企业发展与金融策略等。任中国系统工程学会副秘书长，金融系统工程专业委员会主任。中国运筹学会决策分会副理事长。中国优选法统筹法与经济数学研究会量化金融与保险分会副理事长，中国信息经济学会金融科技专业委员会副主任。发表学术论文80余篇，出版专著2部。主持完成了包括国家自然科学基金重点项目等10余项国家、省部级项目。目前主持国家自然科学基金重大专项项目“基于中国“实体经济-金融系统”复杂关联的计算实验建模研究”（2022.01-2026.12）.2007年获得教育部“新世纪优秀人才支持计划。2010年获得天津市青年科技奖。2017年带领“大数据金融量化团队” 入选天津市“高层次创新创业团队” ，2018年入选天津市宣传文化“五个一批”人才，2020年带领“金融工程与数字金融创新团队”入选天津市“重点领域创新团队”。。

报告题目：社交媒体会扭曲价格发现吗？ 来自并购谣言的证据

报告摘要：移动互联时代下，社交媒体的信息影响资本市场的的方式和程度愈加重要。本文研究了在面对潜在的虚假并购谣言时，社交媒体活动是否会阻碍价格发现，以揭示社交媒体作为一种信息渠道的不利影响。研究发现：（a）异常的社交媒体发帖量与并购消息的实现呈正相关；（b）社交媒体活动引起了市场对并购消息更大的短期反应，即使并购消息是虚假的谣言；（c）与社交媒体活动相关的价格扭曲在并购谣言出现后的几周内仍然存在；(d) 当帖子的影响力较大，公司的机构所有权较低，以及并购新闻是由主流媒体报道时，与谣言工厂效应相关的价格扭曲更加明显。

5. Kannan Govindan

Kannan Govindan is currently a Chair Professor and Head of the Center for Sustainable Supply Chain Engineering with the Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark. He has been recognized as a Highly Cited Researcher within “Engineering” for the forth year in a row (2018, 2019, 2020, and 2021) from Thomson-Reuters/Clarivate Analytics. He has authored/coauthored more than 325 international journal articles (more than 40100+ citations and an H-index of 108) in leading journals such as Nature, Omega, Decision Sciences, European Journal of Operational Research, Journal of the Operational Research Society, Journal of Environmental Management, Renewable and Sustainable Energy Reviews, Journal of Cleaner Production, Computers and Industrial Engineering, Ecological Indicators, Transportation Research Part E: Logistics and Transportation Review, Transportation Research Part D: Transport and Environment, Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, European Management Journal, Computers and OR, International Journal of Production Economics, and International Journal of Production Research. Many of his papers were selected as the ESI top 1% highly cited papers or 0.1% hot papers or identified and highlighted as the Key Scientific Article contributing to the excellence in Engineering and Environmental research (Source: Web of Science). His research interests include digital supply chain, Industry 4.0 on supply chain, sustainable development goals, reverse logistics, closed-loop supply chain, digitalized sustainable circular economy, green supply chain management, and sustainable supply chain management. Dr. Govindan is an Executive Editor of the Journal of Cleaner Production, an Associate Editor of Transportation Research Part E: Logistics and Transportation Review, a Senior Editor of Industrial Management and Data Systems, an Editor of Annals of Operations Research, an Area Editor of INFOR: Information Systems and Operational Research, an Area Editor of Computers and Industrial Engineering, and was a Guest Editor in journals such as Decision Sciences, European Journal of Operations Research, Computers and OR, Annals of OR, Journal of Cleaner Production, and International Journal of Production Economics. Finally, he was an Editorial Board Member of several international journals.

Title: Artificial Intelligence and its role in Supply Chain Resilience

Abstract: Logistics and supply chain management (LSCM) is always considered one of the key areas to focus on to improve overall organizational performance. However, it is not easy to have an effective supply chain, unlike in earlier years. The dynamic nature of the business environment combined with a competitive economy results in the increased complexity of LSCM operations. In addition, the recent pandemic COVID exposed the weak links of existing systems of LSCM. Therefore, it becomes a mandate option for the industries to enhance their logistics and supply chains to be reliable, resilient, and profitable. In recent years, Industry 4.0 technologies have been started to integrate with various logistics and supply chain operations. The industries have considered the Industry 4.0 technologies as the disruptive technologies for the existing challenges. Among several Industry 4.0 technologies, in recent years, artificial intelligence gained more momentum with the concern of LSCM. Hence, this talk will explore the role of artificial intelligence in supply chain resilience.

 
6.Francisco saldanha-da-Gama

Francisco Saldanha da Gama is professor of Operations Research in the Department of Statistics and Operations Research at the Faculty of Science, University of Lisbon, where he received his PhD in 2002. He has a large teaching experience both in terms of undergraduate and postgraduate programs focusing on the fields of Operations Research, Mathematical Programming,Discrete Optimization, Stochastic Optimization, and Logistics. He has co-supervised several PhD students and has been in more than 20 PhD committees worldwide.

Published more than 50 articles in scientific international journals mostly in the areas of location analysis, supply chain management, logistics and combinatorial optimization. His work has resulted in approximately 3100 citations in Scopus with an h-index of 26. Co-edited the two editions of the volume “Location Science” published by Springer International Publishing. Presented more than 100 contributed talks in scientific events being invited to innumerable scientific events as a plenary/semi-plenary/keynote speaker.

Awarded several prizes and honors such as the EURO prize for the best EJOR review paper (2012) and the Elsevier prize for the EJOR top cited article 2007–2011 (2012).

Member of innumerable scientific committees of international conferences and other scientific events.Member of various international scientific organizations such as INFORMS—Institute for the Operations Research and Management Science, USA, CMAFcIO—Centro de Matem´atica Aplica¸c˜oes Fundamentais e Investiga¸c˜ao Operacional da Funda¸c˜ao da Faculdade de Ciˆencias, University of Lisbon, ECCO—European Chapter on Combinatorial Optimization, EWGSO—Working Group on Stochastic Optimization, SOLA—INFORMS Section on Location Analysis), and EWGLA—EURO Working Group on Locational Analysis, of which he isone of the past coordinators.

Currently Editor-in-Chief of Computers & Operations Research as well as member of the Editorial Advisory Board of the Journal of the Operational Research Society (UK) and Operations Research Perspectives.

The research interests include stochastic mixed-integer optimization, location theory and project scheduling.

Title: The Role of Multi-criteria Chance-Constrained Facility Location in Humanitarian Logistics Planning

Abstract: In this presentation some advances on multi-criteria modeling frameworks forstochastic discrete single-allocation facility location problems are discussed. Demand is assumed to be uncertain and a minimum service level imposed by means of a set of probabilistic constraints. A minimum throughput at the facilities is also assumed to justify operating them. Two multicriteria paradigms are discussed: vectorial optimization and goal programming. Several objective functions of interest in the context of humanitarian logistics are discussed. The general modeling frameworks proposed are then applied to the so-called stochastic shelter-site location problem, which is a problem emerging in the context of preventive disaster management. The adopted objective functions include the average distance traveled to a shelter site as well as the average and minimum quality of the selected shelter sites. The resulting models are discussed and assessed using two real benchmark data sets. The results show that considering uncertainty and multiple objectives in the type of facility location problems investigated leads to solutions that may better hedge against the uncertainty underlying a disastrous event such as an severe earthquake.

7.张国庆

Guoqing Zhang (张国庆), PhD, PEng, is a Professor in Department of Mechanical, Automotive & Materials Engineering, University of Windsor. He received a BE degree from Southeast University, and PhD from City University of Hong Kong. His recent research interests include supply chain and logistics optimization, data-driven optimization, algorithms design and development, dual-channel and closed-loop supply chains. He has published over 80 articles on those areas in journals such as Operations Research, Comput. Optim. Appl., IISE Transactions, EJOR, Omega, COR, TRE, TRB, IJPE, IJPR,  and so on. He developed a solver for large-scale linear programming, and provided consulting to several world leading companies on Automotive, Energy, Airline, and Food industries. Dr. Zhang and his team won the First Practice Prize of the Canadian Operational Research Society in 2015.  He is the lead guest editor of the special issues of IJPE and AOR, and a member of NSERC (Canada) discovery grant evaluation group.

Title: Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America

Abstract: We present an integrated approach to explore the effects of Industry 4.0 and related information and communication technologies (ICT) on smart supply chains, by combining introduction of the current national strategies in North America, the research status analysis on ICT assisted supply chains from the major North American national research councils, and a systematic literature review of the subject. Besides, we introduce a smart supply chain hierarchical framework with multi-level intelligence. Furthermore, the challenges faced by supply chains under Industry 4.0 and future research directions are discussed as well. 

8. Witold Pedrycz

Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of  several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.

His main research directions involve Computational Intelligence, Granular Computing, knowledge discovery, data science, and knowledge-based neural networks among others.

Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).  

Title: Federated Learning, Knowledge Transfer, and Knowledge Distillation: Developments in Green and Granular Machine Learning

Abstract: The visible trends of Machine Learning (ML) are inherently associated with the diversity of data and innovative ways they are used in order to carry out learning pursuits. The ongoing objectives of the research agenda are also investigated in the context of green ML (usually referred to as green AI). One can identify three ongoing challenges with far-reaching methodological implications, namely (i)completing designs in the presence of strict constraints of privacy and security, (ii) efficient model building completed with limited data of varying quality, and (iii) a reduction of computing effort knowledge transfer and distillation.

We advocate that to conveniently address these quests, it becomes beneficial to engage the fundamental framework of Granular Computing to enhance the existing approaches (such as e.g., federated learning in case of (i) and transfer knowledge in (iii)) or establish new directions to the problem formulation. Likewise, it is also essential to establish sound mechanisms of evaluation of the performance of the ML architectures. It will be demonstrated that various ways of conceptualization of information granules in terms of fuzzy sets, sets, rough sets, and others may lead to efficient solutions.

To establish a suitable conceptual ML framework, we include a brief discussion of concepts of information granules and Granular Computing. We show how granular models endow numeric models with their quantification mechanisms to deliver a prerequisite for machine learning constructs with an emphasis on associated computational overhead.

To proceed with a detailed discussion, a concise information granules-oriented design of rule-based architectures is outlined. A way of forming the rules through unsupervised federated learning is investigated along with algorithmic developments. A granular characterization of the model formed by the server vis-a-vis data located at individual clients is presented. It is demonstrated that the quality of the rules at the client’s end is described in terms of granular parameters and subsequently the global model becomes represented as a granular construct. The roles of granular augmentations of models in the setting of granular knowledge distillation are outlined. It is shown how the agenda of green ML is effectively realized by exploring information granules and stressing an importance of the holistic perspective at critical trade-offs among interpretability, enormous computational overhead, and transparency of predictors and classifiers.  

9.杨立兴

杨立兴，北京交通大学教授，博士生导师。长期从事交通优化管理与控制方面的研究，在国内外著名高水平学术期刊发表学术论文150余篇、出版学术专著1部、获授权发明专利11项、软件著作权10项。多篇论文入选 “ESI高被引论文”和“ESI热点论文”；先后获教育部自然科学奖一等奖（2项）、中国自动化学会自然科学奖二等奖、“钟家庆运筹学奖”、“詹天佑铁道科学技术奖”、中国运筹学会“运筹新人奖”。兼任中国管理科学与工程学会理事、中国系统工程学会理事、中国运筹学会理事、中国运筹学会智能计算分会理事长、亚太工业工程与管理学会Fellow、交通运输顶级期刊Transportation Research Part B编委会编辑、Urban Rail Transit副主编、全国性一级学术期刊《系统工程学报》编委、《交通运输工程与信息学报》编委等。

报告题目：城市轨道交通车辆灵活运用优化模型与算法

报告摘要：随着城市规模的不断扩大, 城市轨道交通对通勤客流的吸引力不断增长, 从而导致了通勤客流的复杂特征，为城市轨道交通的运营组织带来了极大挑战。本报告针对通勤客流需求的动态性和不均衡性, 采用运筹优化技术，从客流-车流最佳耦合的角度，着重介绍基于灵活编组的城市轨道交通车底运用计划和列车运行图协同优化模型和相应的求解算法，并通过数值计算验证所提方法的合理性及有效性。相关方法对降低城市轨道交通的运营成本、提高运营效率具有较强的理论指导意义。

10.郭熙铜

郭熙铜，哈尔滨工业大学教授、博士生导师。主要研究领域为数字医疗健康。研究成果应用于多地卫生机构，入选国家自然科学基金资助项目优秀成果选编（七）。入选爱思唯尔中国高被引学者。主持国家自然科学基金重点、优青、面上、青年等项目。担任Internet Research、Electronic Commerce Research and Applications、管理科学学报（英文版）副编辑、《管理科学》管理信息系统领域主编。任第24届国际信息系统发展会议（ISD）程序主席（2015）。参加2011年诺贝尔奖获得者经济学大会。培养的博士毕业生任教于西安交大、南开、北航等985高校。

报告题目：数据驱动的医疗健康决策研究与实践

11. 王云

王云，四维图新政企业务部产品总监，超过10年面向位置服务售前、产品及项目管理经验。曾策划和落地了多个行业内千万级位置大数据项目，参与并主导北京交通委、浙江交通厅、顺丰、DHL、VIVO、字节跳动、中国移动等多个位置服务类项目。服务的客户群体遍布交通、物流、运营商、互联网、政府、公共安全等多个领域。

报告题目：基于数字孪生技术的位置大数据应用场景探讨

12. 孙蕊

孙蕊，北京市智慧交通发展中心（北京市机动车调控管理事务中心）数据服务部副部长。主要从事智能交通领域关键技术研究及应用工作，重点在城市停车领域、多源数据融合、信息化技术的业务应用等方面开展关键技术应用研究及标准化工作。先后主持或参与多项国家、科技部、交通部、北京市等科技项目，获北京市科学技术奖二等奖2项、获得发明专利1项、软件著作权1项、发表论文10余篇（其中SCI检索1篇，IE检索3篇，核心期刊2篇）。

报告题目：北京智慧停车大数据应用现状及探索