A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty

This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis,...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2013; H. 2013; S. 1 - 12
Hauptverfasser: Jin, Weihua, Chan, Christine W., Hu, Zhiying
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
John Wiley & Sons, Inc
Schlagworte:
Genetic algorithms
Heuristic
Interactive
Interactive systems
Linear programming
Mathematical models
Matlab
Municipal waste management
Mutation
Operations research
Problem solving
Production methods
Programming
Quadratic programming
Solid waste management
Solid wastes
Solvers
Uncertainty
Waste treatment
ISSN:1024-123X, 1563-5147
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Zusammenfassung:This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks). The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2013/272491