Game Theoretic Models for Revenue Management in the Presence of Networks

收入管理(RM)的定义是“在正确的时间向正确的客户收取正确的价格”，并与公司盈利地管理其需求的能力有关。供应链管理强调供应与需求的匹配。该提案寻求推进RM和SCM的分析模型和技术，以提高在互联(网络)世界中面临动态决策的公司的效率。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、补充或替代产品、客户与供应商的关系以及最终消费者对彼此的影响。 公司做出许多影响客户需求的决定，包括关于产品的价格、库存、种类、功能和质量的决定，以及产品的低价组合(捆绑)等特殊优惠。这些工具的成功使用需要客户行为的详细数学模型，通常是个人级别的，其中包括客户在产品中的选择、由于客户学习而导致的需求演变、购买的战略时机(称为战略行为)，以及由于相似产品的用户数量而导致的需求变化。后一种现象称为“网络外部性”，是因为产品或服务的价值受到其他客户的存在和行为的影响。许多理性决策者的存在与网络相结合，需要基于网络的博弈论和其他SCM和RM模型的扩展。 快节奏和竞争的市场迫使企业尽可能地使用完整和最新的市场观点，并在运营中积极利用新兴的“大数据”技术。这些技术有助于收集、存储和有效处理可能从各种数据源高速到达的大量数据，以及解决数据准确性(大数据的四个V)引起的问题。考虑到这种具有挑战性的环境，这项研究计划将 1.在各种类型的网络和激烈的竞争中帮助公司做出运营决策。 2.深入了解战略和选择客户行为对运营决策的影响，特别是当客户的信息有限且随着时间推移而学习的时候。 3.在受上述问题影响的企业的运营决策中利用大数据工具。 4.提出了供需双方的博弈论和大规模随机优化模型。 该项目的结果对于RM和SCM的行业从业者、决策者和学术研究人员来说很重要。它们为HQP在高级业务分析方面的培训提供了大量机会。

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.