报告题目：三维细观力学模型与应用

报告人：张锦华 研究员

报告时间：05月12日 14:30-15:30

报告地点：民航楼1213会议室

报告内容摘要：

报告题目：航空装备事故调查注意事项

报告人：张东方

报告时间：2023年5月15日 14:00-15:00

报告地点：将军路校区民航楼1213会议室

报告内容摘要：

报告题目：A Co-Opetitive Game Analysis of Platform Compatibility Strategies Under Add-on Services

报告所属学科：管理科学与工程

报告人：李武（加拿大温莎大学）

报告时间：2023年5月12日 15:00-18:00

报告地点：经管院702会议室

报告摘要：

Large-scale platforms (LSPs) with valuation and awareness advantages have enabled competing small-scale platforms (SSPs) to be embedded in their platforms. This compatibility strategy creates a new channel, the compatible channel, through which customers can purchase services from an SSP via the LSP. Meanwhile, more platforms have been introducing add-on services to enhance their profitability. This study develops stylized game models to characterize the interaction between an LSP and an SSP, and explores their strategic and operational decisions on platform compatibility under add-on services. Our major research findings are as follows. First, we identify the conditions for platform compatibility: compatibility becomes an equilibrium strategy if the proportion of demand through the compatible channel falls within an intermediate range. Second, compatibility has opposite impacts on service pricing: At a low proportion of demand through the compatible channel, the two platforms engage in a price war; otherwise, they both raise prices. Finally, some model extensions further verify the robustness of the conclusions.

报告人简介：

李武(Kevin W. Li)，现任加拿大温莎大学Odette商学院教授。1991年获厦门大学控制科学理学学士学位，1994年获厦门大学系统工程工学硕士学位，2003年获加拿大滑铁卢大学系统设计工程博士学位。2011年6月~12月和2015年5月~7月由日本学术振兴会(JSPS)外籍聘用研究员项目资助到东京工业大学价值与决策科学系进行访问研究。主要研究方向：物流与供应链管理、决策理论与方法、冲突分析。其研究获得三项加拿大自然科学与工程研究基金会(NSERC)发现基金项目的支持。自2001年以来，在《European Journal of Operational Research》、《IEEE Transactions on Fuzzy Systems》、《IEEE Transactions on Systems, Man, and Cybernetics》、《Information Sciences》、《International Journal of Production Economics》、《International Journal of Production Research》、《Transportation Research》、《Water Resources Research》等国际期刊发表67篇学术论文，被国内外同行广泛引用，SCI/SSCI累计引用2552次，h指数30。现任SSCI期刊《Group Decision and Negotiation》的副主编以及其他多家SCI/SSCI期刊编委。

报告题目：体系韧性评估方法探讨

报告所属学科：管理科学与工程

报告人：兑红炎（郑州大学管理学院）

报告时间：2023年5月13日 15:00-18:00

报告地点：经管学院703室

报告摘要：

鉴于体系的复杂性和动态性，提出韧性应以理解和塑造适应不断变化的各个分层关联子系统为出发点，构建“可感知、可预防、可恢复、可重构”体系评估方法。“可感知”，是对体系中各类主体的变化情况和变化趋势进行感知；“可预防”，是利用机器学习及人工智能等智能化辅助工具，可自主预防各种威胁发生的概率；“可恢复”，是通过常态化、动态化、精准化的数据捕获及时发现问题，表明故障时性能可恢复的程度或快慢；“可重构”，是基于体系关联多层结构进行的自动调整和自我完善，应对故障的结构重组能力，实现全生命周期的闭环管理。结合智能集群对抗体系和装备保障体系，考虑预防、降级、恢复和重构四个阶段，阐述体系各层之间的协同作战能力，提出跨域整组的韧性策略。

报告人简介：

兑红炎，男，郑州大学 管理学院，教授，博士，博士生导师，河南省高校科技创新人才、河南省青年骨干教师、学术技术带头人。从事复杂网络优化（韧性、脆弱性、稳定性等）、智能集群及制造系统大数据可靠性管理（集群对抗、保障体系、机械制造系统故障诊断等）；长期从事的重要度理论成果获得省部级自然科学奖；所提出的综合重要度计算方法已经被美国SAS系统JMP软件采用。主持2项国家自然科学基金、教育部规划基金、国际合作基金、装备发展部预研基金、科技处重大专项子课题等。发表SCI等学术期刊论文92篇，出版学术专著2部。担任国际期刊《Journal of Risk and Reliability》，《International Journal of Mathematical, Engineering and Management Sciences》等编委。

报告题目：Taming the Long Tail: The Gambler's Fallacy in Intermittent Demand Management

报告所属学科：管理科学与工程

报告人：毕晟（上海财经大学）

报告时间：2023年5月17日 15:00-17:00

报告地点：经管学院702室

报告摘要：

“Long tail” products with intermittent demand often tie up valuable warehouse space and capital investment for many companies. Furthermore, the paucity of demand data poses additional challenges for model estimation and performance evaluation. Traditional inventory solutions are not designed for products with intermittent demand. In this paper, we propose a new framework to optimize the choice of “replenishment timing” and “replenishment quantity” for managing the inventory metrics of long tail products, when evaluated over a finite horizon. Our analysis is motivated by a recent interesting observation that the gambler’s fallacy phenomenon actually holds in a finite number of coin tosses. We use this phenomenon to analyze the inventory problem for intermittent demand to demonstrate that classical inventory models using KPIs such as fill rate, average cost per cycle, or average cost per unit, etc., must necessarily “bias” the underlying demand distribution to account for the finite horizon effect. We provide the exact closed-form expressions of the biased distribution to account for this effect in performance evaluation. The results show that the choice of replenishment timing and replenishment quantity is essential to superior performanceon several key inventory metrics. For long tail products, the belief that it is less likely for another demand to arrive shortly after a preceding one (the gambler’s fallacy), turns out to be true when performance is tabulated over a finite horizon, even if demands across time are independent. So it pays to delay the replenishment of depleted stocks to save on holding cost and warehouse space. Managers can optimize the replenishment timing, besides choosing the replenishment quantity, to optimize the performance metrics of several classes of inventory problems. This is especially useful for companies managing a large number of long tail products.

报告人简介：

Bi Sheng is currently an assistant professor at School of Information Management and Engineering, Shanghai University of Finance and Economics. She received her Ph.D. degree in Analytics and Operations from National University of Singapore in 2021 and her Bachelor's degree in Industrial Engineering from Nanjing University in 2016. Her research interests are in the area of data-driven optimization, supply chain management and socially responsible operations.

报告题目：Stochastic Robust Facility Location: A Nested Decomposition Approach

报告所属学科：管理科学与工程

报告人：王曙明（中国科学院大学）

报告时间：2023年5月18日 16:00-18:00

报告地点：经管学院702室

报告摘要：

In this work, we investigate a broad class of facility location problems in the context of adaptive robust stochastic optimization. A state-wise ambiguity set is employed to model the distributional uncertainty associated with the demand in different states, where the conditional distributional characteristics in each state are described by support, mean as well as dispersion measures, which are conic representable. A robust sensitivity analysis is performed in which on the one hand we analyze the impact of the change in ambiguity set parameters (e.g., state probabilities, mean value abounds and dispersion bounds in different states) onto the optimal worst-case expected total cost using the ambiguity dual variables. On the other hand, we analyze the impact of the change in location design onto the worst-case expected second-stage cost, and show that the sensitivity bounds are fully described as the worst-case expected shadow capacity cost. As for the solution approach, we propose a nested Benders decomposition algorithm for solving the model exactly, which leverages the subgradients of the worst-case expected second-stage cost at the location decisions formed insightfully by the associated worst-case distributions. The nested Benders decomposition approach ensures a finite-step convergence, which can also be regarded as an extension of the classic L-shaped algorithm for two-stage stochastic programming to our state-wise robust stochastic facility location problem with conic representable ambiguity. Finally, the results of a series of numerical experiments are presented which justify the value of the state-wise distributional information incorporated in our robust stochastic facility location model, the robustness of the model and the performance of the exact solution approach.

报告人简介：

中国科学院大学经济与管理学院王曙明教授，主要从事鲁棒优化与随机规划研究及其在选址与物流网络优化、供应链风险管理、库存与收益管理、健康医疗管理等领域的应用。研究成果分别发表于Production and Operations Management, INFORMS Journal on Computing, Transportation Science, IISE Transactions, Naval Research Logistics, IEEE Trans. Cybernetics等权威杂志上。目前担任运筹学著名期刊《Computers and Operations Research》的领域编辑(Area Editor).