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上海交通大学郑欢教授:An Approximate Dynamic Programming Approach to Dynamic Pricing for Network Revenue Management
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发布日期:2018-05-03 点击数:

喻园管理论坛 2018年第38期(总第382期)

演讲主题: An Approximate Dynamic Programming Approach to Dynamic Pricing for Network Revenue Management

主 讲 人: 郑欢教授, 上海交通大学安泰经管学院

主 持 人: 胡鹏教授,生产运作与物流管理系

活动时间: 2018年5月10日(周四)10:00-11:50

活动地点: 管理学院121

主讲人概况:郑欢教授现为上海交通大学安泰经管学院管理科学系系主任,研究领域为供应链管理与优化、柔性结构设计;在Operations Research, Productions and Operations Management, INFORMS Journal on Computing等国际知名期刊上发表多篇学术论文。

活动概况: Much of the network revenue management literature considers capacity control problems where product prices are fixed and the product availability is controlled over time. However, for industries with imperfect competition, firms typically retain some pricing power and dynamic pricing models are more realistic than capacity control models. Dynamic pricing problems are more challenging to solve; even the deterministic version is typically nonlinear. In this paper, we consider a dynamic programming model and use approximate linear programs (ALPs) to solve the problem. Unlike capacity control problems, the ALPs are semi-infinite linear programs. Nevertheless, we show that for quite general demand functions, the ALPs can be solved to any desired accuracy with a column generation algorithm. Furthermore, for the affine approximation under a linear independent demand model, we show that the ALP can be reformulated as a compact second order cone program (SOCP). The size of the SOCP formulation is linear in model primitives, including the number of resources, the number of products, and the number of periods. We conduct numerical experiments with off-the-shelf commercial solvers to show that solving the SOCP formulation is orders of magnitude faster than solving the original ALP using column generation. We also test the heuristic control policies based on the approximation on a set of hub-and-spoke problem instances.

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