Fuzzy approach for multi-level programming problems

Multi-level programming techniques are developed to solve decentralized planning problems with multiple decision makers in a hierarchical organization. These become more important for contemporary decentralized organizations where each unit or department seeks its own interests. Traditional approach...

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Vydáno v:Computers & operations research Ročník 23; číslo 1; s. 73 - 91
Hlavní autoři: Shih, Hsu-Shih, Lai, Young-Jou, Stanley Lee, E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 1996
Elsevier Science
Pergamon Press Inc
Témata:
Applied sciences
Computer programming
Decision making models
Decision theory. Utility theory
Exact sciences and technology
Fuzzy logic
Fuzzy sets
Mathematical programming
Operational research and scientific management
Operational research. Management science
Operations research
Studies
Membership function
Organization management
Decentralized system
Decision making
Multiobjective programming
Tolerance
Multilevel programming
Planning
Fuzzy set
Hierarchized structure
Mathematical programming
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:Multi-level programming techniques are developed to solve decentralized planning problems with multiple decision makers in a hierarchical organization. These become more important for contemporary decentralized organizations where each unit or department seeks its own interests. Traditional approaches include vertex enumeration and transformation approaches. The former is in search of a compromise vertex based on adjusting the control variable(s) of the higher level and thus is rather inefficient. The latter transfers the lower-level programming problem to be the constraints of the higher level by its Kuhn-Tucker conditions or penalty function; the corresponding auxiliary problem becomes non-linear and the decision information is also implicit. In this study, we use the concepts of tolerance membership functions and multiple objective optimization to develop a fuzzy approach for solving the above problems. The upper-level decision maker defines his or her objective and decisions with possible tolerances which are described by membership functions of fuzzy set theory. This information then constrains the lower-level decision maker's feasible space. A solution search relies on the change of membership functions instead of vertex enumeration and no higher order constraints are generated. Thus, the proposed approach will not increase the complexities of original problems and will usually solve a multilevel programming problem in a single iteration. To demonstrate our concept, we have solved numerical examples and compared their solutions with classical solutions.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/0305-0548(95)00007-9