A genetic-algorithms-based approach for programming linear and quadratic optimization problems with uncertainty
type: Item
instance of: scholarly article
title: A genetic-algorithms-based approach for programming linear and quadratic optimization problems with uncertainty
zbMATH Open document ID: 1296.90146
DOI: 10.1155/2013/272491
author: Weihua Jin
author: Zhiying Hu
author: Christine W. Chan
published in: Mathematical Problems in Engineering
publication date: 2014-10-13T00:00:00Z
review text: Summary: 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\(^{TM}\). 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.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C05
Mathematics Subject Classification ID: 90C20
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6354594
Wikidata QID: Q59025456
describes a project that uses: Matlab
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/272491
OpenAlex ID: W2079717677
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description: scientific article; zbMATH DE number 6354594