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Multi-objective decision processes u...

Cheng, Lifei.

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Multi-objective decision processes under uncertainty: Applications, problem formulations, and solution strategies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-objective decision processes under uncertainty: Applications, problem formulations, and solution strategies./
作者:	Cheng, Lifei.
面頁冊數:	243 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0943.
Contained By:	Dissertation Abstracts International64-02B.
標題:	Operations Research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3082618

Multi-objective decision processes under uncertainty: Applications, problem formulations, and solution strategies.
Cheng, Lifei.

Multi-objective decision processes under uncertainty: Applications, problem formulations, and solution strategies. - 243 p.

Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0943.

Thesis (Ph.D.)--Carnegie Mellon University, 2003.

Operating in a changing and uncertain environment, firms must make strategic and operational decisions in order to satisfy many conflicting goals. Problems of this type, which we refer to as “multi-objective decision processes under uncertainty”, arise in many important decision contexts in various industries and pose challenges for both practitioners and researchers. We consider a general class of problems with the following characteristics: (1) the decision context is changing and involves uncertainty; (2) decisions are made at different levels and different times; and (3) multiple conflicting objectives are involved. Our contribution in this work is to develop a general framework for formulating appropriate decision models and for developing tailored solution strategies to solve specific problems efficiently.Subjects--Topical Terms:

626629
Operations Research.

Multi-objective decision processes under uncertainty: Applications, problem formulations, and solution strategies.
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245 1 0 $a Multi-objective decision processes under uncertainty: Applications, problem formulations, and solution strategies.
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500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0943.
500 $a Adviser: Arthur W. Westerberg.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2003.
520 $a Operating in a changing and uncertain environment, firms must make strategic and operational decisions in order to satisfy many conflicting goals. Problems of this type, which we refer to as “multi-objective decision processes under uncertainty”, arise in many important decision contexts in various industries and pose challenges for both practitioners and researchers. We consider a general class of problems with the following characteristics: (1) the decision context is changing and involves uncertainty; (2) decisions are made at different levels and different times; and (3) multiple conflicting objectives are involved. Our contribution in this work is to develop a general framework for formulating appropriate decision models and for developing tailored solution strategies to solve specific problems efficiently.
520 $a We first contend that it is critical to formulate the right model for the underlying problem we intend to solve. We strongly believe that decision making under uncertainty is naturally a multi-objective problem. We emphasize that it is important to formulate the model properly with the understanding of the underlying problem, for example, when decisions are made and what information is available.
520 $a We have developed various solution strategies to solve this class of problems, e.g., rigorous and approximate, analytical and numerical. We believe that one should select, combine and tailor different solution approaches according to the problem specific characteristics and requirement. We first start with developing a rigorous stochastic dynamic programming algorithm to compute and propagate the Pareto optimal frontier recursively backward in time. The numerical difficulty due to “curse of dimensionality” prohibits the rigorous algorithm and necessitates approximation methods. We then develop a simulation-based optimization framework to compute parameterized policies with structural properties obtained from problems under simplifying assumptions. Finally, we propose an approximation architecture that decomposes the decision problem and combines the advantages of different solution approaches to find the optimal first stage(s) decisions in a computationally efficient manner.
520 $a We have investigated several specific applications within the wide range of applications of this class of problems. We present examples and results on capacity planning and inventory control problems. We also present several industrial applications on logistics and supply chain management.
590 $a School code: 0041.
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