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We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items. 相似文献
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One approach to evaluating system reliability is the use of system based component test plans. Such plans have numerous advantages over complete system level tests, primarily in terms of time and cost savings. This paper considers one of the two basic building blocks of many complex systems, namely a system of n parallel components, and develops minimum cost component test plans for evaluating the reliability of such a system when the component reliabilities are known to be high. Two different decision rules are considered and the corresponding optimization problems are formulated and solved using techniques from mathematical programming. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44 : 401–418, 1997 相似文献
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