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Titlebook: Markovian Demand Inventory Models; Dirk Beyer,Feng Cheng,Michael Taksar Book 2010 Springer-Verlag US 2010 (s,S) policy.Analysis.Inventory

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發(fā)表于 2025-3-21 19:05:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Markovian Demand Inventory Models
編輯Dirk Beyer,Feng Cheng,Michael Taksar
視頻videohttp://file.papertrans.cn/625/624652/624652.mp4
概述Only book coverage of this important area of research.Suresh Sethi is the most renowned researcher in the field.Appropriate for researchers, practitioners, and Ph.D. students.Includes supplementary ma
叢書(shū)名稱International Series in Operations Research & Management Science
圖書(shū)封面Titlebook: Markovian Demand Inventory Models;  Dirk Beyer,Feng Cheng,Michael Taksar Book 2010 Springer-Verlag US 2010 (s,S) policy.Analysis.Inventory
描述.Inventory management is concerned with matching supply with demand and a central problem in Operations Management. The problem is to find the amount to be produced or purchased in order to maximize the total expected profit or minimize the total expected cost. Over the past two decades, several variations of the formula appeared, mostly in trade journals written by and for inventory managers. A critical assumption in the inventory literature is that the demands in different periods are independent and identically distributed. However, in real life, demands may depend on environmental considerations or the events in the world such as the weather, the state of economy, etc. Moreover, these events are represented by stochastic processes - exogenous or controlled...In Markovian Demand Inventory Models, the authors are concerned with inventory models where these world events are modeled by Markov processes. Their research on Markovian demand inventory models was carried out over a period of ten years beginning in the early nineties..
出版日期Book 2010
關(guān)鍵詞(s,S) policy; Analysis; Inventory theory; Markov process; Markovian demand; Stochastic Processes; base-sto
版次1
doihttps://doi.org/10.1007/978-0-387-71604-6
isbn_softcover978-1-4614-2466-6
isbn_ebook978-0-387-71604-6Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer-Verlag US 2010
The information of publication is updating

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發(fā)表于 2025-3-21 22:06:33 | 只看該作者
Conclusions and Open Research Problemsved. Specifically, we show that the (., .)-type policies shown to be optimal for a large class of inventory models with independent demands continue to be optimal for Markovian demand models, with one difference. That is, with Markovian demands, the (., .) values depend on the state of the Markov process.
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Discounted Cost Models with Lost Saless situations, it is quite often that demand that cannot be satisfied on time is lost. This is particularly true in a competitive business environment. For example, in many retail establishments such as a supermarket or a department store, a customer chooses a competitive brand and goes to another store if his/her preferred brand is out of stock.
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Discount Cost Models with Polynomially Growing Surplus Costdback policies is established with Markovian demand: unbounded, ordering costs that are l.s.c., and surplus costs that are l.s.c. with polynomial growth. Furthermore, optimality of (., .)-type policies is proved when the ordering cost consists of fixed and proportional cost components and the surplus cost is convex.
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Average Cost Models with Backordersrun average cost of inventory/backlog and ordering. The purpose is to establish the dynamic programming equation or average cost optimality equation for the problem, prove the existence of an optimal feedback (or Markov) policy, and show that a feedback policy of (., .)-type is optimal.
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