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演讲者介绍：David Simchi-Levi是美国麻省理工大学教授，供应链管理研究的先驱和领军人物。他是管理科学国际顶尖期刊Operations Research的前任主编，国际权威期刊Naval Research Logistics的前任主编。目前仍担任多个国际知名期刊的编辑，例如Management Science, Networks, Transportation Science。David Simchi-Levi教授的主要研究方向是物流和供应链，在国际顶尖期刊发表许多论文，并出版多本专著。他的学生多数在美国名校任职。
Title: Understanding the Effectivene of Sparse Proce Flexibility
Abstract: In this talk, we review new theory that explains the effectivene of sparse flexibility design in any finite size manufacturing network.Under stochastic demand, we establish two fundamental properties of sparse designs, a supermodularity property and a decomposition property.These properties are then used to provide the first theoretical justification for several well-known observations in the proce flexibility literature, and to derive new insights into designing flexible procees in large systems.Under worst-case demand, we propose the plant cover index and establish its relation with the worst-case sales.Applying this relation, we demonstrate the effectivene of a certain sparse design, called the long chain.Finally, we discu the combination of proce flexibility and strategic inventory as an effective disruption mitigation strategy.Bio: David Simchi-Levi is a Profeor of Engineering Systems at MIT and Chairman of OPS Rules Management Consultants, an operations strategy consulting company.He is considered one of the premier thought leaders in supply chain management.Prof.Simchi-Levi has been the principal investigator for more than five million dollars in funded academic research.He is the former Editor-in-Chief of Operations Research, Naval Research Logistics and a member of the board for several scientific journals including Management Science, Networks, Transportation Science and Telecommunication Systems, and a former Area Editor of Transportation for Operations Research.His Ph.D.students have accepted positions in leading academic institutes including Berkeley, Columbia U., U.of Illinois Urbana-Champaign, U.of Michigan, Purdue U., Georgia Tech, and Virginia Tech.His research focuses on developing and implementing robust and efficient techniques for logistics and manufacturing systems.He has published widely in profeional journals on both practical and theoretical aspects of logistics and supply chain management.Profeor Simchi-Levi coauthored the books Managing the Supply Chain(McGraw-Hill, 2004), The Logic of Logistics(Springer 2005), as well as the award winning Designing and Managing the Supply Chain(McGraw-Hill, 2007).His new book Operations Rules: Delivering Customer Value through Flexible Operations was published by MIT Pre in October 2010.Profeor Simchi-Levi has consulted and collaborated extensively with private and public organizations.He is the founder of LogicTools which provides software solutions and profeional services for supply chain planning.LogicTools is now part of IBM.演讲者介绍：Jiawei Zhang是纽约大学Leonard N.Stern商学院的终身教授，研究兴趣主要为数学规划、医疗管理与供应链优化。他在国际知名期刊Operations Research, Mathematics of Operations Research, Mathematical Programming, Psychometrika, SIAM Journal on Computing发表许多论文。Jiawei Zhang博士毕业于斯坦福大学，之前就读于清华大学。
Title:
Proce Flexibility: A Distribution-Free Bound on the Performance of k-Chain
Abstract: In this paper, we present a distribution-free bound on the expected sales of the long chain compared to that of fully flexible structure.Our bound uses only mean and standard deviation of the demand distribution, but compares very well with the bound with known distributions.This suggests the robustne of the performance of the long chain under different distributions.We also obtain a similar bound for k-chain, a more general flexibility structure.Bio: Jiawei Zhang is an Aociate Profeor of Information, Operations and Management Sciences at New York University's Leonard N.Stern School of Busine.He joined NYU Stern's Operations Management Group in September 2004.Profeor Zhang's primary research interests include mathematical programming, health care operations, and supply chain optimization.His publications have appeared in Mathematics of Operations Research, Mathematical Programming, Operations Research, Psychometrika, SIAM Journal on Computing, etc.Profeor Zhang received his Master of Science degree in Operations Research from Tsinghua University, China, and his PhD in Management Science and Engineering from Stanford University.演讲者介绍：Xiuli Chao是密歇根大学教授，研究兴趣包括排队论、随机调度优化、金融工程、库存管理与供应链管理。他在国际顶尖期刊发表许多论文，并出版多部学术专著。1998年获得Erlang Prize奖，2005年获得David F.Baker Distinguished Research Award。他博士毕业于哥伦比亚大学。
Title:
Simple approximation policies with worst case performance bounds for several analytically intractable stochastic inventory systems
Abstract: We consider several stochastic inventory systems that lack of structures for their optimal control policies, including periodic-review inventory models with setup cost and finite ordering capacity, remanufacturing systems with setup cost and finite production capacities, joint replenishment models, and perishable inventory systems with arbitrary product lifetimes.The demand procees can be non-stationary and correlated over time, such as Markov modulated demand procees, Martingale models for forecast evolution(MMFE), etc.The exact computations of the optimal policies for such systems are not poible even in the special cases when the optimal solutions exhibit nice structure due to curse of dimensionality.We develop easily computable operational policies for such systems that have provably worst case performance bounds, and numerical tests show that these policies perform near optimal.This talk is based on joint works with Xiting Gong, Retsef Levi, Cong Shi, and Robert Zhang.Bio: Xiuli Chao is a profeor of Industrial and Operations Engineering at The University of Michigan, Ann Arbor.His research interests include queueing theory, stochastic scheduling and optimization, financial engineering, inventory control, and supply chain management, and has published extensively in these areas.He is the co-author of two books, “Operations Scheduling with Applications in Manufacturing and Services”(Irwin/McGraw-Hill, 1998), and “Queueing Networks: Customers, Signals, and Product Form Solutions”(John Wiley & Sons, 1999).Chao received the 1998 Erlang Prize from the Applied Probability Society of INFORMS, and the 2005 David F.Baker Distinguished Research Award from Institute of Industrial Engineers(IIE).Xiuli Chao received his doctoral degree in Operations Research from Columbia University.演讲者介绍：Vernon Hsu是香港中文大学商学院副院长，研究兴趣包括物流和供应链、优化理论在供应链管理以及信息系统中的应用。他在国际顶尖期刊Management Science, Manufacturing and Service Operations Management, Naval Research Logistics, Operations Research, Productions and Operations Management, IIE Transactions发表多篇论文。
Title:
Integrative Management of Transfer Pricing and Global Sourcing Decision in a Multinational Firm
Abstract: Many multinational firms(MNFs)in recent years have put their efforts into developing tax-effective supply chain strategies that integrate tax considerations acro different countries and take advantage of specific tax incentives.These strategies oftentimes involve the management of internal transactions taking place between divisions or subsidiaries belonging to the same MNF but located in different countries.Transfer pricing(TP), the pricing for transactions between divisions of an MNF, has been used not just as a tool to facilitate internal transactions;many MNFs also recognize the importance of combining the TP decisions with their tax considerations in improving their overall profitability, e.g., reducing their tax liabilities by shifting some income from high tax countries/regions to low tax ones.In this presentation, we will present a model for integrative management of a multinational firm's transfer pricing(TP)and global sourcing decisions to take advantage of different tax rates acro subsidiaries and to maximize the firm's global after-tax profits.With various decision timelines(ex-ante and ex-post TP decisions)and structures(centralized and decentralized sourcing decisions), we characterize optimal TP and sourcing decisions in various scenarios.Several new and important managerial insights will be discued.Bio: Prof.Vernon Hsu is a Chair Profeor and Aociate Dean for Research in the CUHK Busine School, The Chinese University of Hong Kong.He has also held faculty positions at the University of New South Wales, Australia, George Mason University, USA, and Hong Kong University of Science & Technology.Profeor Hsu’s research interests include logistics and supply chain management and the application of optimization theory in Operations Management and Information Systems.Some of his current research projects investigate the integration of international tax planning with global supply chain design.His research has been published in journals such as IIE Transactions, Management Science, Manufacturing and Service Operations Management, Naval Research Logistics, Operations Research and Productions and Operations Management.演讲者介绍：Shaohui Zheng是香港科学技术大学商学院教授，运营管理系主任。研究兴趣包括供应链管理、服务与制造系统运营管理、应用概率模型、运筹和市场研究的交叉问题。他在Operations Research, Management Science, Production and Operations Management, IIE Transactions, Journal of Applied Probability, IEEE Transactions on Automatic Control, and Queueing Systems发表多篇论文，并出版一部专著。他博士毕业于哥伦比亚大学，目前担任Operations Research等多个杂志的副编。
Title: Source Diversification and Pricing for Systems with Unreliable Suppliers
Abstract: It is common for a firm to make use of multiple suppliers of different delivery lead times, reliabilities, and costs.In this paper, we are concerned the joint pricing and inventory control problem for such a firm that has a quick-response supplier and a regular supplier that both suffer random disruptions, and faces price-sensitive random demands.The random disruption procees of the two suppliers are modeled as independent Markov chains.We aim at characterizing the optimal ordering and pricing policies in each period over a planning horizon, and analyzing the impacts of supply source diversification and supplier reliability, on the firm's optimal profit and operational policies, on its customers, and on its suppliers.We show that, when both suppliers are unreliable, the optimal inventory policy in each period is a reorder point policy and the optimal price is decreasing in the starting inventory level of the period.In addition, we show that having supply source diversification or higher supplier reliability increases the firm's optimal profit and lowers the optimal selling price;hence it benefits both the firm and its customers.We also demonstrate that, with the selling price as a decision, a supplier may receive even more order from the firm after an additional supplier is introduced, which seems counter-intuitive and is different from the result in the case when the selling price is exogenously given.For the special case where the quick-response supplier is perfectly reliable, we further show that the optimal inventory policy is of a base-stock type and the optimal pricing policy is a list-price policy with markdowns.This is a joint work with Xiuli Chao and Xiting Gong.Bio: Prof.Shaohui Zheng is a profeor in the School of Busine and Management at HKUST, and is in charge of the Operations Management division.He received his Ph.D.degree in Operations Research from Columbia University.His current research interests include supply chain management, operations of manufacturing and service systems, the interface of operations and marketing, and general applied probability models.His research works appear in Operations Research, Management Science, Production and Operations Management, IIE Transactions, Journal of Applied Probability, IEEE Transactions on Automatic Control, and Queueing Systems etc.He has one academic book published by Springer-Verlag.Profeor Zheng is an aociate editor for Operations Research.He was also a guest editor for Annals of Operations Research, and an aociate editor for Asia-Pacific Journal of Operational Research.演讲者介绍：Chung-Piaw Teo是新加坡国立大学商学院教授，前任院长，现任决策科学系主任。研究兴趣包括随机优化、博弈论、港口物流、供应链等。他博士毕业于麻省理工大学。目前担任Operations Research杂志supply chain management的部门主编。在国际顶尖期刊Operations Research, Management Science发表许多论文。
Title: Judgment Error in Lottery Play: When the Hot-Hand Meets the Gambler’s Fallacy
Abstract: We demonstrate that lottery players can be influenced to believe erroneously in the existence of “hot” numbers, where past winning numbers are perceived to have a greater probability of winning in future draws, even though past and the future events are independent.The existence of this “hot-hand” effect in lottery games is surprising, as works by Clotfelter and Cook(1993)and Terrell(1994)have documented instead the presence of the opposite effect, the “gambler’s fallacy”, in the US lottery market—which means that the amount of money bet on a particular number falls sharply after the number is drawn.We use two sets of lottery game data to show that both the gambler’s fallacy and hot-hand fallacy can prevail under different gaming environments, contingent on the design(e.g., prize structures)of the lottery games.We develop a quasi-Bayesian model that is consistent with our empirical findings to investigate the conditions in the environment that determine which fallacy dominates.Our results also provide a new explanation for the “lucky store” effect(Guryan and Kearney, 2008)—why players in the US believe that lightning will strike twice in the case of lottery vendors, but not in the case of lottery numbers.This is joint work with Qingxia Kong and Nicolas Lambert.Bio: Chung-Piaw Teo is currently a Profeor and Head of Department of Decision Sciences in the NUS Busine School, National University of Singapore.He graduated from MIT with a PhD in Operations Research, and has taught in NUS since then.His research interest include discrete and stochastic optimization, planning under uncertainty, social choice etc., with emphasis on applications in gaming and port industry, logistics and supply chain, and more recently, busine analytics.演讲者介绍：Zuo-Jun(Max)Shen是加州大学伯克利分校教授，研究兴趣包括供应链设计与管理、市场机制设计、应用优化理论、有限信息决策理论等。在国际顶尖期刊发表多篇论文，并担任多个国际顶尖杂志编辑。2003年获得美国国家自然科学基金委CAREER award。2000年博士毕业于西北大学。
Title: Behind forecast inflation/deflation: Learning forecaster’s preference with optimization models
Abstract: Forecast is ubiquitous in all areas of operations management, finance, and economics.However, it is widely observed that practical forecasts are usually inconsistent with actual realizations.This phenomenon is known as forecast inflation(overforecast)or deflation(underforecast).This is due to the fact that forecasters typically have asymmetric preferences towards overforecast and underforecast.Given historical forecast and realization data, we develop inverse and robust optimization models that can efficiently recover forecaster’s preference.We conduct a numerical study using simulated data to show the performance of the optimization models.We then present an empirical study on forecast sharing in the PC manufacturing supply chain and show managerial insights on the manufacturer's forecast preference.Bio：Zuo-Jun(Max)Shen is the Chancellor's Profeor in the department of Industrial Engineering and Operations Research at UC Berkeley.He received his Ph.D.from Northwestern University in 2000.He has been active in the following research areas: integrated supply chain design and management, market mechanism design, applied optimization, and decision making with limited information.He is currently on the editorial/advisory board for several leading journals.He received the CAREER award from National Science Foundation in 2003.
