具有正交货期的销售损失、报废库存系统:系统点交叉方法

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
K. Preethi, A. Shophia Lawrence, B. Sivakumar
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引用次数: 0

摘要

本文综合分析了基于不同补货策略的$(s, s)$和$(s,Q)$两种连续评审销售损失库存系统。假设需求到达时间形成泊松过程,需求规模呈指数分布。我们假设库存中的物品在指数时间后可能会过时。补货的前置时间是指数级的。我们还假设超额需求和缺货期间发生的需求都损失了。利用水平交叉口的系统点法和积分方程法,明确地导出了库存水平的稳态概率分布。在得到一些系统性能度量之后,我们计算了总预期成本率。我们还提供了涉及不同参数和成本的敏感性分析的数值示例。作为我们的数值分析的结果,我们提供了一些关于具有正交货期的报废物品库存系统的最佳$(s, s)$和$(s,Q)$策略的见解。维持库存的较好策略可以用数字来量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lost sales obsolescence inventory systems with positive lead time: a system-point level-crossing approach
In this article, we provide a comprehensive analyses of two continuous review lost sales inventory system based on different replenishment policies, namely $(s,S)$ and $(s,Q)$ . We assume that the arrival times of demands form a Poisson process and that the demand sizes have i.i.d. exponential distribution. We assume that the items in stock may obsolete after an exponential time. The lead time for replenishment is exponential. We also assume that the excess demands and the demands that occurred during stock out periods are lost. Using the system point method of level crossing and integral equation method, we derive the steady-state probability distribution of inventory level explicitly. After deriving some system performance measures, we computed the total expected cost rate. We also provide numerical examples of sensitivity analyses involving different parameters and costs. As a result of our numerical analysis, we provide several insights on the optimal $(s,S)$ and $(s,Q)$ policies for inventory systems of obsolescence items with positive lead times. The better policy for maintaining inventory can be quantified numerically.
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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