管科系系列讲座第234-235期预告

 

管科系系列讲座第234期预告

 

时 间:2019年5月28日(周二)13:30

地 点:思源楼524

主持人:田林副教授

主讲嘉宾:Yi Zhu is an Associate Professor of Marketing, Mary & Jim Lawrence Fellow at the University of Minnesota.

TitleProminent Retailer and Price Search

Abstract: Many markets feature a prominent retailer from which most consumers start their price searches. This study examines the implication of the prominence on price search when consumers have a limited consideration set. In our model, the consumers search for prices of the homogenous product across retailers at heterogenous costs. We define a retailer’s prominence level as its first search market share and the prominent retailer has the highest prominence level. Our results show that: first, compared with its rivals, the prominent retailer charges higher prices stochastically if its prominence level is higher enough; otherwise, its price is stochastically lower. Second, the rise of a prominent retailer might intensify the price competition in the market and lower the average market price. It suggests that more concentrated online traffic might benefit consumers. Furthermore, we find that the curse of prominence exists. That is, a greater prominence can lead to a lower profit for the prominent retailer.


 

管科系系列讲座第235期预告

 

时 间:2019年5月28日(周二)14:30

地 点:思源楼524

主持人:冯天俊教授(商务分析与运营创新研究中心)

主讲嘉宾:Ye Lu, Professor, School of Management, University of Science and Technology of China

TitleRobust Price-setting Newsvendor Problem

Abstract: The price-setting newsvendor problem is well studied in the literature. However, it is commonly assumed that retailers have complete demand information modeled as a function of price and random noise. In reality, a retailer may have very limited information on a demand model because a retailer who has exercised only a few prices does not have sufficient information to accurately estimate a demand model. This creates a gap between academic research and practical applications. In this talk, we consider the price-setting newsvendor problem in which the retailer knows the expected demand on a few exercised price points and the distribution of the random noise. Both additive and multiplicative demand models are studied. The retailer makes price and inventory decisions to minimize the maximum regret, defined as the difference between the expected profit based on limited demand information and the expected profit based on complete demand information. We show that this robust optimization problem can be reduced to a one-dimensional optimization problem, and derive the optimal price and inventory decisions. We also provide a demand learning policy that can reduce the minmax regret to any δ within O(1δ) steps. Extensive numerical studies show that our method has a great performance that dominates those of regression methods.


 

管理科学系

2019-5-14