2019
DOI: 10.48550/arxiv.1908.09295
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A Complete Algebraic Solution to the Optimal Dynamic Rationing Policy in the Stock-Rationing Queue with Two Demand Classes

Abstract: In this paper, we apply the sensitivity-based optimization to propose and develop a complete algebraic transformational solution for the optimal dynamic rationing policy in inventory rationing across two demand classes. Our results provide a unified framework to set up a new transformational threshold type structure for the optimal dynamic rationing policy. Based on this, we can provide a complete description that the optimal dynamic rationing policy is either of critical rationing level (i.e. threshold type o… Show more

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Cited by 2 publications
(3 citation statements)
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References 95 publications
(143 reference statements)
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“…Pibernik (2016) analyzes the application of critical-level inventory-management policies to multiple customer classes of differentiated service levels. Li et al (2019) consider a stock-rationing queue with only two demand classes, and assume that inter-arrival time and service time are exponentially distributed. This work is further extended by Li et al (2023) with more detailed results.…”
Section: Optimizing Pricing and Inventory Strategiesmentioning
confidence: 99%
“…Pibernik (2016) analyzes the application of critical-level inventory-management policies to multiple customer classes of differentiated service levels. Li et al (2019) consider a stock-rationing queue with only two demand classes, and assume that inter-arrival time and service time are exponentially distributed. This work is further extended by Li et al (2023) with more detailed results.…”
Section: Optimizing Pricing and Inventory Strategiesmentioning
confidence: 99%
“…Using Lemma 2, we examine the sensitivity of blockchain-pegged policy on the long-run average profit of the dishonest mining pool. Substituting (2) and ( 6) into (12), we have…”
Section: Monotonicity and Optimalitymentioning
confidence: 99%
“…Thus, the sensitivity-based optimization theory has been applied to performance optimization in many practical areas. For example, the energy-efficient data centers by Xia et al [21] and Ma et al [14,15]; the inventory rationing by Li et al [12]; the multi-hop wireless networks by Xia and Shihada [22] and the finance by Xia [23].…”
Section: Introductionmentioning
confidence: 99%