题目：Intelligent Urban Logistics – Challenges, Current Practices and Future Research
摘要：Logistics is a very important economic activity in smart city development. In this survey cum research talk, I will discuss the challenges of urban logistics, particularly in the context of last-mile delivery of freight into mega-cities like Singapore. I will first present several case studies in Europe and Asia on the operation of Urban Consolidation Centers (UCCs) which enable collaboration among shippers, carriers, and retailers to consolidate deliveries. I will then discuss my research on market mechanisms and system that provide the necessary technology and incentives for multiple stakeholders to derive win-win benefits from operating within UCCs. In particular, I discuss a profit-maximizing auction mechanism for using the UCC’s service. We model the winner determination problem as mixed-integer program (MIP). Then, we provide a greedy approximation algorithm to solve the MIP in reasonable time. Our experiments indicate that the proposed auction along with the greedy approximation algorithm is able to maximize the UCC’s profit to near optimality with reasonable computational budget.
第28期:Intelligent Urban Logistics-Urban Mobility
Logistics and transportation are two very aspects in smart city development. After reviewing several important problems in urban management, I will discuss last-mile delivery of passengers from a transport hub (such as a major train/bus station) to their homes via taxi ride-sharing. I will discuss a 2-level planning framework for making real-time clustering and routing decisions. The first upper/strategic level selects, from the set of current requests, the ones to be scheduled in a given period. The second lower/operational level clusters and then routes the selected requests. The upper level is formulated as a Markov decision process (MDP). For the operational problem, we use a standard vehicle routing heuristic.
Lau’s research in the interface of Artificial Intelligence and Operations Research has been widely applied to decision support and optimization, and has contributed to advances of algorithms and applications to a variety of complex resource planning and scheduling problems in logistics, transportation, tourism and health-care. At SMU, he was awarded the SMU李光耀Research Fellowship for research excellence in 2008. He currently serves on the editorial board of the IEEE Transactions on Automation Science and Engineering and guest editors in the Journal of Heuristics and Web Intelligence and Agent Systems Journal.
He has been involved in consulting projects in logistics and transportation, for companies such as DHL, Bax Global, PSA, EADS, ST Dynamics, and various government agencies. He is a chartered member of the Chartered Institute of Logistics and Transportation, and currently serves on the CILT (Singapore) board of directors. For his work with the Singapore Ministry of Defense, he won the National Innovation and Quality Convention Star Award in 2006, and was nominated for the prestigious Defense Technology Prize (individual category) in 2007.
题目：Trends and New Directions in Data Clustering
讲座人：Ming Dong, Wayne State University, USA
Today digital data are accumulated at the faster than ever speed in science, engineering, biomedicine, and real-world sensing. Clustering has been widely used in data mining to discover the interesting patterns and gain insights from large amounts of data. Usually, real-world data are highly complex, sharing one or several following prominent characteristics: they are tremendous in size with millions of objects; they come unstructured with heterogeneous features; knowledge is often embedded in large amounts of noisy, even confusing data. The complexity of the acquired digital data overwhelms the useful information and makes it extremely difficult to derive true understanding from it. This talk presents our recent efforts in addressing some of these challenges: (1) How to handle relational and heterogeneous data? We proposed a novel graph theoretic approach to perform pairwise and high-order heterogeneous data co-clustering; (2) How to incorporate prior or background knowledge to improve the quality of clustering? We developed a non-negative matrix factorization framework for semi-supervised clustering, in which user is able to provide pairwise constraints on a few data objects specifying whether they “must” or “cannot” be clustered together; (3) How to efficiently mine and visualize extremely large-scale data? We proposed an exemplar-based clustering and visualization technique, which provides high efficiency and high interpretability through the use of exemplars. Applications of data clustering in biomedical imaging, bioinformatics, text mining and web analysis will also be discussed.
Ming Dong received his B. S. degrees in electrical engineering and industrial management engineering from Shanghai Jiao Tong University, Shanghai, China, in 1995. He received his Ph. D degree in electrical engineering from University of Cincinnati in 2001. He joined the faculty of Wayne State University in 2002 and is currently an Professor in the Department Computer Science and the director of Machine Vision and Pattern Recognition Laboratory. Dr. Dong’s areas of research include pattern recognition, data mining, and multimedia content analysis. His research is funded by National Science Foundation, State of Michigan, and Industries. He has published over 90 technical articles in premium journals and conferences in related fields, e.g., TPAMI, TKDE, TNN, TVCG, CVPR, ACM MM and WWW, and received over 1,000 citations until now. He was as an associate editor of IEEE Transactions on Neural Networks (2008-2011) and Pattern Analysis and Applications (2007-2010), and served in many conference program committees and US National Science Foundation panels. He also served as senior research consultant in Baidu Inc. and Ford Motor Company, and has given over twenty invited talks in various institutes.