Chance Constrained Integer Programming Models for Capital Budgeting in Fuzzy Environments

This paper attempts to model capital budgeting problems by chance constrained integer programming in a fuzzy environment (rather than a stochastic environment). Some examples are also provided to illustrate the potential applications of new models. Finally, a fuzzy simulation based genetic algorithm...

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Veröffentlicht in:The Journal of the Operational Research Society Jg. 49; H. 8; S. 854 - 860
Hauptverfasser: Iwamura, K., Liu, B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basingstoke Macmillan Press 01.08.1998
Palgrave
Taylor & Francis Ltd
Schlagworte:
Algorithms
Applied sciences
Capital budgeting
Chromosomes
Crossovers
Exact sciences and technology
Flows in networks. Combinatorial problems
Fuzzy logic
Genetic algorithms
Genetic mutation
Goal programming
Integer programming
Integers
Mathematical models
Mathematical vectors
Operational research and scientific management
Operational research. Management science
Operations research
Programming models
Scheduling, sequencing
Studies
Theoretical Papers
Integer programming
Production system
Genetic algorithm
Budget
Profit
Fuzzy programming
Production capacity
Capital
ISSN:0160-5682, 1476-9360, 0160-5682
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper attempts to model capital budgeting problems by chance constrained integer programming in a fuzzy environment (rather than a stochastic environment). Some examples are also provided to illustrate the potential applications of new models. Finally, a fuzzy simulation based genetic algorithm is designed for solving chance constrained integer programming models with fuzzy parameters.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0160-5682
1476-9360
0160-5682
DOI:10.1038/sj.jors.2600576