Coordinating Inventory Control and Pricing Strategies

这笔赠款为协调定价、生产和库存决策的模型的分析分析提供资金，试图为它们开发最优或接近最优的算法。有趣的是，凸性或k-凸性等概念已被证明对经典库存模型有效，但并不直接适用于具有一般的、价格相关的随机需求过程的模型。因此，为了分析包含定价决策的模型，对称k-凸函数的概念被发展并应用于各种定价问题，包括有限和无限范围的单一产品模型。这一概念解决了一个长期存在的猜想，并刻画了每个问题的最优策略的结构。本研究将继续分析、开发和实施整合定价、生产和库存决策的不同模型的算法。重点是开发利用可用信息来提高供应链效率并最终提高公司底线的理论和实践方法。具体地说，研究将集中于(I)将对称k-凸函数的概念应用于各种定价模型，包括具有对价格和提前期的敏感度的多类客户的模型，以及具有可自由支配销售的模型；(Ii)开发计算方法并使用从工业合作伙伴那里获得的数据来测试其有效性；以及(Iii)将对称k-凸性的概念应用于其他运营管理问题。

This grant provides funding for the analytical analysis of models that coordinate pricing, production and inventory decisions in an attempt to develop optimal or near optimal algorithms for them. Interestingly, concepts such as convexity or k-convexity, that have been proven effective for classical inventory models, do not directly apply for models with general, price dependent, stochastic demand processes. Thus, to analyze models that incorporate pricing decisions, the concept of symmetric k-convex functions has been developed and applied to a variety of pricing problems including finite and infinite horizon single product models. This concept settled a long-standing conjecture and characterized the structure of the optimal policy for each of these problems.This research will continue with the analysis, development and implementation of algorithms for different variants of models that integrate pricing, production and inventory decisions. The focus is on developing theoretical and practical methods that utilize the available information to improve supply chain efficiencies and ultimately the company bottom line. Specifically, the research will focus on (i) applying the notion of symmetric k-convex functions to a variety of pricing models including models with multiple classes of customers differentiated by their sensitivity to price and lead-time, as well as models with discretionary sales; (ii) develop computational methods and test their effectiveness using data received from industrial partners; and (iii) apply the notion of symmetric k-convexity to other operational management problems.