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9-21 加拿大皇家科学院院士,美国德克萨斯大学达拉斯教授Sethi Suresh Pal.学术讲座:Feedback Stackelberg games for dynamic supply chains with cost learning

题目:Feedback Stackelberg games for dynamic supply chains with cost learning
主讲人:Sethi Suresh Pal. 教授(美国德克萨斯大学达拉斯分校)
时间:2015年9月21日10点
地点:主楼418
主讲人简介:
    Suresh P. Sethi任教于德克萨斯大学达拉斯分校,现为加拿大皇家科学院院士,Charles & Nancy Davidson运作管理的杰出教授,智能供应网络中心主任(the Center for Intelligent Supply Networks)。Sethi教授在运作管理、营销、工业工程、最优控制等领域做出了杰出贡献,最为人熟知是Sethi advertising model、DNSS Points以及有关最优控制的教科书,如Optimal Control Theory: Applications to Management Science and Economics。
    Sethi教授在国际主流期刊上发表学术论文264篇,其中Management Science 11篇, Operations Research 17篇, Production and Operations Management 16篇, Manufacturing & Service Operations Management 5篇。
    Sethi教授现为Production and Operations Management协会主席,同时担任Production and Operations Management期刊(UT Dallas 24种顶级期刊之一)的Department Editor,SIAM Journal on Control and Optimization的责任编辑,Automatica的副主编。现担任Management Science、Operations Research(OR)、IIE Transactions(IIE) 、Manufacturing & Service Operations Management等多种权威期刊的审稿专家。
内容简介:
    We study a two-period supply chain in which a manufacturer produces a product, learns to reduce cost , and sells it through a retailer with a price-dependent demand. The manufacturer's second-period production cost declines linearly in the first-period production with a random learning rate. The manufacturer may or may not have the option to carry inventory. We model the problem as a dynamic Stackelberg game and obtain an explicit feedback Stackelberg equilibrium. The explicit solution allows us to examine the impact of mean learning rate and learning rate variability on the manufacturer's production and pricing decisions, as well as on the retailer's procurement and pricing decisions. We demonstrate that as the mean learning rate or the learning rate variability increases, the traditional double marginalization problem becomes more severe, leading to greater efficiency loss in the channel. We provide revenue sharing contracts that can coordinate the dynamic supply chain. In particular, when the manufacturer may hold inventory, we identify two major drivers for inventory carryover: market growth and learning rate variability. Lastly, we demonstrate the robustness of our results by examining a model in which learning takes place continuously.


(承办:技术经济与管理系、科研与学术交流中心)