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- W2951220015 abstract "Uncertainty is often present in real-life problems.Deciding how to deal with this uncertainty can be difficult. Theproper formulation of a problem can be the larger part of the workrequired to solve it. This thesis is intended to be used by adecision maker to determine how best to formulate a problem. Robustoptimization and partially observable Markov decision processes(POMDPs) are two methods of dealing with uncertainty in real lifeproblems. Robust optimization is used primarily in operationsresearch, while engineers will be more familiar with POMDPs. For adecision maker who is unfamiliar with one or both of these methods,this thesis will provide insight into a different way of problemsolving in the presence of uncertainty. The formulation of eachmethod is explained in detail, and the theory of common solutionmethods is presented. In addition, several examples are given foreach method. While a decision maker may try to solve an entireproblem using one method, sometimes there are natural partitions toa problem that encourage using multiple solution methods. In thisthesis, one such problem is presented, a military planing problemconsisting of two parts. The first part is best solved with POMDPsand the second with robust optimization. The reasoning behind thispartition is explained and the formulation of each part ispresented. Finally, a discussion of the problem types suitable foreach method, including multiple applications, isprovided." @default.
- W2951220015 created "2019-06-27" @default.
- W2951220015 creator A5018912613 @default.
- W2951220015 date "2017-01-01" @default.
- W2951220015 modified "2023-09-24" @default.
- W2951220015 title "Dealing with uncertainty : a comparison of robustoptimization and partially observable Markov decisionprocesses; Comparison of robust optimization and partiallyobservable Markov decision processes" @default.
- W2951220015 hasPublicationYear "2017" @default.
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