Game Theoretic Models for Revenue Management in the Presence of Networks

网络存在下收入管理的博弈论模型

• 批准号: RGPIN-2017-05467
• 负责人: Levin, Yuri
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682395
• 关键词: Game; Theoretic; Models; Revenue; Management

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.

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。公司做出的许多决定会影响顾客的需求，包括有关价格、库存、品种、特性和产品质量的决定，以及诸如低价组合（捆绑）产品等特别优惠。成功地使用这些工具需要详细的客户行为数学模型，通常可以达到个人水平，涵盖客户对产品的选择，由于客户学习而产生的需求演变，购买的战略时机（称为战略行为），以及由于类似产品的用户数量而导致的需求变化。后一种现象被称为“网络外部性”，因为特定客户的产品或服务的价值受到其他客户的存在和行为的影响。许多理性的决策者与网络相结合的存在需要对博弈论和其他供应链管理和管理模型进行基于网络的扩展。***快节奏和竞争激烈的市场迫使公司尽可能使用完整和最新的市场视图，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4v）带来的问题。鉴于这种具有挑战性的环境，该研究项目将***1。帮助企业在各种类型的网络存在和激烈的竞争下做出经营决策。* * * 2。提供关于运营决策如何受到战略和选择客户行为的影响的见解，特别是当客户信息有限并且随着时间的推移而学习时。* * * 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。提出了供应链管理和供应链管理的博弈论和大规模随机优化模型。***该计划的成果对RM和SCM的行业从业者，决策者和学术研究人员很重要。它们为HQP在高级商业分析方面的培训提供了充足的机会。