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Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

机译:随机需求与固定订货成本协调库存控制与定价策略:有限地平面案例

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摘要

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy.
机译:我们分析一个有限期的,单一产品的定期审查模型,在该模型中,价格和生产/库存决策是同时制定的。不同时期的需求是相互独立的随机变量,其分布取决于产品价格。在每个时期的开始就做出定价和订购决定,所有短缺都被积压。订购成本包括与订购数量成比例的固定成本和可变成本。目的是找到一种库存策略和定价策略,以在有限的范围内使预期利润最大化。我们表明,当需求模型是可加的时,获利函数是k凹的,因此(s,S,p)策略是最优的。在这种策略中,根据经典(s,S)策略管理期间库存,并根据每个期间开始时的库存位置确定价格。对于更一般的需求函数,即乘法和加法函数,我们证明了获利能力函数不一定是k凹的,并且(s,S,p)策略不一定是最优的。我们引入了一个新概念,即对称k凹函数,并将其应用于提供最优策略的特征。

著录项

  • 作者

    Chen Xin; Simchi-Levi David;

  • 作者单位
  • 年度 2005
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
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