题目：Feedback Stackelberg Games for Dynamic Supply Chains with Cost Learning

报告人：加拿大皇家科公司院士、美国德克萨斯大学达拉斯分校Suresh P. Sethi首席教授

报告时间：2019年10月30日（周三） 上午 10:00

地点：管理楼管阶一

报告人简介： 

Suresh P. Sethi教授，加拿大皇家科公司院士，美国德克萨斯大学达拉斯分校首席教授，Charles & Nancy Davidson运作管理杰出教授，智能供应网络中心主任(the Center for Intelligent Supply Networks)。Sethi教授曾为Production and Operations Management协会主席，同时为Production and Operations Management期刊编辑部主任，SIAM Journal on Control and Optimization的责任编辑，Automatica的副主编。担任Management Science、Operations Research、IIE Transactions、Manufacturing & Service Operations Management、Production and Operations Management和Journal of Operations Management等20余种国际重要期刊的编委或审稿专家。其在运作管理、营销、工业工程、最优控制等领域作出杰出贡献，最为人熟知的是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教授已在国际知名期刊Management Science，Operations Research，Production and Operations Management，Manufacturing & Service Operations Management等发表多篇学术论文。

摘要：

We consider a decentralized two-period supply chain in which a manufacturer produces a product with benefits of cost learning, and sells it through a retailer facing a price-dependent demand. The manufacturer’s second-period production cost declines linearly in the first-period production, but with a random learning rate. The manufacturer may or may not have the inventory carryover option. We formulate the resulting problems as two-period Stackelberg games and obtain their feedback equilibrium solutions explicitly. We then examine the impact of mean learning rate and learning rate variability on the pricing strategies of the channel members, on the manufacturer’s production decisions, and on the retailer’s procurement decisions. We show 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 obtain 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.