学习型零售商的供应链合同定价
Learning to Price Supply Chain Contracts against a Learning Retailer
摘要：
大数据分析的兴起使公司的决策自动化，并提高了供应链的敏捷性。本文研究了数据驱动的供应商需要对下游零售商的库存决策做出响应的供应链契约设计问题。供应商和零售商对市场需求都是不确定的，需要不断地了解。供应商的目标是开发数据驱动的定价策略，在一个固定的时间范围内，在广泛的可能的零售商库存政策的次线性遗憾界。为了捕捉动态引起的零售商的学习政策，我们首先作出连接到非平稳在线学习的概念变化预算。变化预算量化了零售商的学习策略对供应商决策的影响。然后，我们提出了动态定价策略的供应商离散和连续的需求。我们还注意到，我们提出的定价政策只需要获得需求分布的支持，但关键的是，不需要供应商有任何先验知识的零售商的学习政策或需求实现。我们研究了几个著名的数据驱动的零售商的政策，包括样本平均近似，分布鲁棒优化，和参数的方法，并表明我们的定价政策导致次线性遗憾界在所有这些情况下。在管理层面上，我们肯定地回答，有一个次线性后悔约束下的零售商的学习政策，即使她面对一个学习的零售商和一个未知的需求分布的定价政策。我们的工作还提供了一个新的视角，在数据驱动的运营管理中，校长必须学会对系统中其他代理所采用的学习策略做出反应。
英文摘要：
The rise of big data analytics has automated the decision-making of companies and increased supply chain agility. In this paper, we study the supply chain contract design problem faced by a data-driven supplier who needs to respond to the inventory decisions of the downstream retailer. Both the supplier and the retailer are uncertain about the market demand and need to learn about it sequentially. The goal for the supplier is to develop data-driven pricing policies with sublinear regret bounds under a wide range of possible retailer inventory policies for a fixed time horizon. To capture the dynamics induced by the retailer's learning policy, we first make a connection to non-stationary online learning by following the notion of variation budget. The variation budget quantifies the impact of the retailer's learning strategy on the supplier's decision-making. We then propose dynamic pricing policies for the supplier for both discrete and continuous demand. We also note that our proposed pricing policy only requires access to the support of the demand distribution, but critically, does not require the supplier to have any prior knowledge about the retailer's learning policy or the demand realizations. We examine several well-known data-driven policies for the retailer, including sample average approximation, distributionally robust optimization, and parametric approaches, and show that our pricing policies lead to sublinear regret bounds in all these cases. At the managerial level, we answer affirmatively that there is a pricing policy with a sublinear regret bound under a wide range of retailer's learning policies, even though she faces a learning retailer and an unknown demand distribution. Our work also provides a novel perspective in data-driven operations management where the principal has to learn to react to the learning policies employed by other agents in the system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
