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Fuzzy multi-level optimization.

Shih, Hsu-Shih.

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Fuzzy multi-level optimization.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Fuzzy multi-level optimization./
作者:	Shih, Hsu-Shih.
面頁冊數:	120 p.
附註:	Source: Dissertation Abstracts International, Volume: 57-01, Section: B, page: 0611.
Contained By:	Dissertation Abstracts International57-01B.
標題:	Engineering, Industrial. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9614291

Fuzzy multi-level optimization.
Shih, Hsu-Shih.

Fuzzy multi-level optimization. - 120 p.

Source: Dissertation Abstracts International, Volume: 57-01, Section: B, page: 0611.

Thesis (Ph.D.)--Kansas State University, 1995.

Multi-level mathematical programming (MLP) is a practical and useful approach, which simulates the actual decision-making process of the hierarchy structure of an organization, to decentralized planning problems. However, there exist no effective techniques for obtaining the numerical solution of a reasonably sized MLP problem. In this study, an effective numerical technique is developed by using the approximate nature of the decision-making process of a large organization. This approximation is imbedded in the allowed tolerance of the membership function by the use of fuzzy optimization approaches. In other words, the multi-level system is modeled as a nested hierarchical fuzzy optimization problem for decentralized planning with multiple decision makers. The upper-level decision maker first states his or her decision with allowable tolerances represented by membership functions. The lower-level decision maker then makes his or her decision based on these allowable tolerances. The search procedure relies on the change of membership functions instead of vertex enumeration as in existing approaches. The proposed approach does not increase the number of constraints and, due to the use of the tolerance or fuzzy nature, the procedure is considerably simplified. Another advantage is that the procedure can be easily extended to n-level or n-level decentralized problems. Furthermore, interactive steps can also be added to adjust the degree of satisfaction of the various levels. Besides the common max-min operation for decision making, some compensatory operators are also discussed as parts of extension. To compare and illustrate the approaches, several numerical examples are solved.Subjects--Topical Terms:

626639
Engineering, Industrial.

Fuzzy multi-level optimization.
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100 1 $a Shih, Hsu-Shih. $3 1685302
245 1 0 $a Fuzzy multi-level optimization.
300 $a 120 p.
500 $a Source: Dissertation Abstracts International, Volume: 57-01, Section: B, page: 0611.
500 $a Major Professor: E. Stanley Lee.
502 $a Thesis (Ph.D.)--Kansas State University, 1995.
520 $a Multi-level mathematical programming (MLP) is a practical and useful approach, which simulates the actual decision-making process of the hierarchy structure of an organization, to decentralized planning problems. However, there exist no effective techniques for obtaining the numerical solution of a reasonably sized MLP problem. In this study, an effective numerical technique is developed by using the approximate nature of the decision-making process of a large organization. This approximation is imbedded in the allowed tolerance of the membership function by the use of fuzzy optimization approaches. In other words, the multi-level system is modeled as a nested hierarchical fuzzy optimization problem for decentralized planning with multiple decision makers. The upper-level decision maker first states his or her decision with allowable tolerances represented by membership functions. The lower-level decision maker then makes his or her decision based on these allowable tolerances. The search procedure relies on the change of membership functions instead of vertex enumeration as in existing approaches. The proposed approach does not increase the number of constraints and, due to the use of the tolerance or fuzzy nature, the procedure is considerably simplified. Another advantage is that the procedure can be easily extended to n-level or n-level decentralized problems. Furthermore, interactive steps can also be added to adjust the degree of satisfaction of the various levels. Besides the common max-min operation for decision making, some compensatory operators are also discussed as parts of extension. To compare and illustrate the approaches, several numerical examples are solved.
520 $a In the last chapter, a minimum-cost flow network problem with imprecise parameters is formulated and solved by the use of the possibility concept. This problem is first formulated as a fuzzy, single-objective minimum-cost flow problem with a modification of capacity constraint. Then the problem is extended to fuzzy, multi-objective and multi-level, minimum-cost problems subject to preference and possibility distributions.
590 $a School code: 0100.
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Operations Research. $3 626629
690 $a 0546
690 $a 0796
710 2 $a Kansas State University. $3 1017593
773 0 $t Dissertation Abstracts International $g 57-01B.
790 1 0 $a Lee, E. Stanley, $e advisor
790 $a 0100
791 $a Ph.D.
792 $a 1995
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