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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Vydáno v:Mathematical problems in engineering Ročník 2013; číslo 2013; s. 1 - 12
Hlavní autoři: Jin, Weihua, Chan, Christine W., Hu, Zhiying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
John Wiley & Sons, Inc
Témata:
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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Abstract 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.
AbstractList 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.
Author Jin, Weihua
Chan, Christine W.
Hu, Zhiying
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CitedBy_id crossref_primary_10_1016_j_jenvman_2017_02_075
Cites_doi 10.1080/02630259208970657
10.1080/00207729308949477
10.1016/0038-0121(95)98604-T
10.1016/0305-0548(74)90052-5
10.1016/S0377-2217(99)00374-4
10.1016/j.jenvman.2009.09.014
10.1089/ees.2009.0340
10.1061/(ASCE)0733-9372(1992)118:3(420)
10.1080/02630259408970147
10.1006/wmre.1996.0045
10.1016/j.jenvman.2010.09.011
10.1016/j.wasman.2010.02.031
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10.1016/S0377-2217(96)00024-0
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10.1061/(ASCE)0733-9488(1994)120:3(132)
10.1287/mnsc.28.11.1270
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10.1016/0377-2217(94)00093-R
10.1061/JUPDAJ.0000205
10.1061/(ASCE)0733-9372(1991)117:5(614)
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Copyright Copyright © 2013 Weihua Jin et al.
Copyright © 2013 Weihua Jin et al. Weihua Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright_xml – notice: Copyright © 2013 Weihua Jin et al.
– notice: Copyright © 2013 Weihua Jin et al. Weihua Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snippet This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The...
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SubjectTerms 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
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Title A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty
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