Game Theoretic Models for Revenue Management in the Presence of Networks

收益管理（RM）被定义为“在正确的时间向正确的客户收取正确的价格”，与公司管理其需求的盈利能力有关。供应链管理（SCM）强调的是供给与需求的匹配。该提案旨在推进RM和SCM的分析模型和技术，以提高公司在一个相互联系（网络化）的世界中面临动态决策的效率。这种联系有多种形式，包括信息和实物的流动、公司之间的竞争或伙伴关系、互补或替代产品、客户-供应商关系以及最终消费者对彼此的影响。

Revenue management (RM) is defined as “charging the right price to the right customer at the right time” and pertains to the ability of companies to profitably manage their demand. Supply chain management (SCM) puts emphasis on the supply and matching it with demand. The proposal seeks to advance analytic models for RM and SCM and techniques to increase the efficiency of companies facing dynamic decisions in an interconnected (networked) world. The connections come in various forms including the flows of information and physical goods, competition or partnerships between companies, complementary or substitute products, customer-supplier relations and influences of the end-consumers on one another. Companies make many decisions that affect customer demand, including decisions regarding prices, inventories, assortments, features and quality of products, and special offers such as low-priced combinations (bundles) of products. Successful use of these instruments requires detailed mathematical models of customer behavior, often down to the individual level, that cover customer choice among products, demand evolution due to customer learning, strategic timing of purchases (called strategic behavior), and changes in demand due to the number of users for similar products. The latter phenomenon, called “network externality,” arises because the value of a product or service for a given customer is affected by the presence and behavior of other customers. The presence of many rational decision makers in combination with networks requires network-based extensions of game theoretic and other SCM and RM models. The fast-paced and competitive marketplace compels companies to use as complete and current a view of the market as possible, and to actively leverage emerging “Big Data” technologies in operations. These technologies help to collect, store, and effectively process large Volumes of data that may arrive at high Velocity from a Variety of data sources, as well as to address the problems arising from the data Veracity (four V's of Big Data). Given this challenging environment, this research program will 1. Help companies make operational decisions in the presence of networks of various types and under intense competition. 2. Provide insight into how operational decisions are affected by strategic and choice customer behavior, in particular, when customers have limited information and learn over time. 3. Leverage Big Data tools in the operational decisions of businesses affected by the above issues. 4. Advance game theoretic and large-scale stochastic optimization models for SCM and RM. The outcomes of the program are important for industry practitioners, decision makers, and academic researchers in RM and SCM. They provide ample opportunities for training of HQP in advanced business Analytics.