Foundations of Stochastic Inventory TheoryStanford University Press, 2002 - 299 Seiten In 1958, Stanford University Press published Studies in the Mathematical Theory of Inventory and Production (edited by Kenneth J. Arrow, Samuel Karlin, and Herbert Scarf), which became the pioneering road map for the next forty years of research in this area. One of the outgrowths of this research was development of the field of supply-chain management, which deals with the ways organizations can achieve competitive advantage by coordinating the activities involved in creating products--including designing, procuring, transforming, moving, storing, selling, providing after-sales service, and recycling. Following in this tradition, Foundations of Stochastic Inventory Theory has a dual purpose, serving as an advanced textbook designed to prepare doctoral students to do research on the mathematical foundations of inventory theory and as a reference work for those already engaged in such research. The author begins by presenting two basic inventory models: the economic order quantity model, which deals with "cycle stocks," and the newsvendor model, which deals with "safety stocks." He then describes foundational concepts, methods, and tools that prepare the reader to analyze inventory problems in which uncertainty plays a key role. Dynamic optimization is an important part of this preparation, which emphasizes insights gained from studying the role of uncertainty, rather than focusing on the derivation of numerical solutions and algorithms (with the exception of two chapters on computational issues in infinite-horizon models). All fourteen chapters in the book, and four of the five appendixes, conclude with exercises that either solidify or extend the concepts introduced. Some of these exercises have served as Ph.D. qualifying examination questions in the Operations, Information, and Technology area of the Stanford Graduate School of Business. |
Inhalt
Two Basic Models | 1 |
Structured Probability Distributions | 9 |
A Continuous Time Model | 14 |
Recursion | 27 |
FiniteHorizon Markov Decision Processes | 41 |
ཝཿབྲབ བྱབྱནྣཱཐག | 45 |
Exercises | 54 |
FiniteHorizon Theory | 77 |
InfiniteHorizon Theory | 171 |
Bounds and Successive Approximations | 181 |
Computational Markov Decision Processes | 193 |
91 | 213 |
Appendix A Convexity | 223 |
Appendix B Duality | 241 |
Discounted Average Value | 261 |
Preference Theory and Stochastic Dominance | 279 |
Myopic Policies | 91 |
Dynamic Inventory Models | 103 |
Monotone Optimal Policies | 119 |
Empirical Bayesian Inventory Models | 151 |
| 293 | |
| 294 | |
Häufige Begriffe und Wortgruppen
admissible decision rules assume base stock level base stock policy Chapter compute concave convex function convex set cost function decreasing defined demand distribution density discount factor dominates dynamic Dynamic Programming effective transition matrix example Exercise exists an optimal expected present value exponentially distributed feasible finite ft(x ft+1 function f given Gt(x Hint holding cost horizon increasing incurred infinite-horizon value inventory level inventory model K-convex Lagrangian Lemma lottery maximize minimizer newsvendor node notation objective function one-period problem optimal decision rule optimal policy optimal solution optimal value function optimality equations policy is optimal Porteus probability Proof Prove Lemma quantity random variable real numbers recursion result scale parameter Section setup cost shortage cost Show St(Q stationary policy stochastic stochastic dominance strategy strictly positive submodular supermodular Suppose terminal value function Theorem unit value of starting vector zero
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Information Control Problems in Manufacturing 2004 (2-volume Set) Peter Kopacek,Gerard Morel,Carlos Eduardo Pereira Eingeschränkte Leseprobe - 2005 |
Recherche opérationnelle pour ingénieurs, Band 2 Jean-François Hêche,Thomas M. Liebling,Dominique de Werra Eingeschränkte Leseprobe - 2003 |