scholarly journals Dynamic programming is optimal for certain sequential decision processes

1980 ◽  
Vol 73 (1) ◽  
pp. 134-137 ◽  
Author(s):  
Arnon Rosenthal
2001 ◽  
Vol 5 (1) ◽  
pp. 47-59
Author(s):  
Omar Ben-Ayed

Operations Research techniques are usually presented as distinct models. Difficult as it may often be, achieving linkage between these models could reveal their interdependency and make them easier for the user to understand. In this article three different models, namely Markov Chain, Dynamic Programming, and Markov Sequential Decision Processes, are used to solve an inventory problem based on the periodic review system. We show how the three models converge to the same (s,S) policy and we provide a numerical example to illustrate such a convergence.


1961 ◽  
Vol 16 (04) ◽  
pp. 261-274
Author(s):  
Brian Gluss

Dynamic programming, a mathematical field that has grown up in the past few years, is recognized in the U.S.A. as an important new research tool. However, in other countries, little interest has as yet been taken in the subject, nor has much research been performed. The objective of this paper is to give an expository introduction to the field, and give an indication of the variety of actual and possible areas of application, including actuarial theory.In the last decade a large amount of research has been performed by a small body of mathematicians, most of them members of the staff of the RAND Corporation, in the field of multi-stage decision processes, and during this time the theory and practice of the art have experienced great advances. The leading force in these advances has been Richard Bellman, whose contributions to the subject, which he has entitledDynamic Programming[1], have had effects not only in immediate fields of application but also in general mathematical theory; for example, the calculus of variations (see chapter IX of [1]), and linear programming (chapter VI).


2019 ◽  
Vol 11 (1) ◽  
pp. 833-858 ◽  
Author(s):  
John Rust

Dynamic programming (DP) is a powerful tool for solving a wide class of sequential decision-making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision in any situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence with the comparative paucity of its real-world applications to improve the decision making of individuals and firms. The fuzziness of many real-world decision problems and the difficulty in mathematically modeling them are key obstacles to a wider application of DP in real-world settings. Nevertheless, I discuss several success stories, and I conclude that DP offers substantial promise for improving decision making if we let go of the empirically untenable assumption of unbounded rationality and confront the challenging decision problems faced every day by individuals and firms.


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