https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Chaudhry_ELBADAWI_2017aChaudhry ELBADAWI 2017a - Revision history2026-04-07T05:47:02ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Chaudhry_ELBADAWI_2017a&diff=236728&oldid=prevImran chaudhry: Imran chaudhry moved page Draft Chaudhry 931014228 to Chaudhry ELBADAWI 2017a2022-02-28T17:01:06Z<p>Imran chaudhry moved page <a href="/public/Draft_Chaudhry_931014228" class="mw-redirect" title="Draft Chaudhry 931014228">Draft Chaudhry 931014228</a> to <a href="/public/Chaudhry_ELBADAWI_2017a" title="Chaudhry ELBADAWI 2017a">Chaudhry ELBADAWI 2017a</a></p>
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</td></tr></table>Imran chaudhryhttps://www.scipedia.com/wd/index.php?title=Chaudhry_ELBADAWI_2017a&diff=236727&oldid=prevImran chaudhry: Created page with " == Abstract == In recent years research on parallel machine scheduling has received an increased attention. This paper considers minimisation of total tardiness for scheduli..."2022-02-28T17:01:04Z<p>Created page with " == Abstract == In recent years research on parallel machine scheduling has received an increased attention. This paper considers minimisation of total tardiness for scheduli..."</p>
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== Abstract ==<br />
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In recent years research on parallel machine scheduling has received an increased attention. This paper considers minimisation of total tardiness for scheduling of n jobs on a set of m parallel machines. A spread-sheet-based genetic algorithm (GA) approach is proposed for the problem. The proposed approach is a domain-independent general purpose approach, which has been effectively used to solve this class of problem. The performance of GA is compared with branch and bound and particle swarm optimisation approaches. Two set of problems having 20 and 25 jobs with number of parallel machines equal to 2, 4, 6, 8 and 10 are solved with the proposed approach. Each combination of number of jobs and machines consists of 125 benchmark problems; thus a total for 2250 problems are solved. The results obtained by the proposed approach are comparable with two earlier approaches. It is also demonstrated that a simple GA can be used to produce results that are comparable with problem-specific approach. The proposed approach can also be used to optimise any objective function without changing the basic GA routine.<br />
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== Full document ==<br />
<pdf>Media:Draft_Chaudhry_931014228-3580-document.pdf</pdf></div>Imran chaudhry