Repository logo
 

Parallel processors scheduling algorithms to minimise makespan in a galvanising plant

dc.contributor.authorDewa, Mendonen_US
dc.contributor.authorNleya, Bakheen_US
dc.date.accessioned2020-06-19T06:53:23Z
dc.date.available2020-06-19T06:53:23Z
dc.date.issued2020-03
dc.date.updated2020-04-30T06:50:02Z
dc.description.abstractGalvanising lines consist of load/loading stations and a series of processing tanks that are generally energy-intensive. Each raw workpart needs to go through a number of processing stages sequentially. Job sizes and processing time vary from part to part, hence the need to derive an optimal schedule to minimise total processing time in a batch. The problem of minimizing the makespan on parallel processing machines using different scheduling algorithms is studied in this paper. A set of 50 independent tasks were scheduled on parallel processors in order to minimize schedule length using Integer Linear Programming, Shortest Processing Time, Longest Processing Time, and Greedy Genetic algorithms. The experimental results demonstrated that our Greedy Genetic algorithm outperformed other algorithms on minimizing makespan on parallel processing machines.en_US
dc.format.extent9 p.en_US
dc.identifier.citationDewa, M. and Nleya, B. 2020. Parallel processors scheduling algorithms to minimise makespan in a galvanising plant. PONTE International Scientific Researches Journal. 76(3). Available: doi:10.21506/j.ponte.2020.3.18en_US
dc.identifier.doi10.21506/j.ponte.2020.3.18
dc.identifier.urihttp://hdl.handle.net/10321/3414
dc.language.isoenen_US
dc.publisherPonte Academic Journalen_US
dc.relation.ispartofPONTE International Scientific Researches Journal. Vol. 76, Issue 3en_US
dc.subjectMakespan minimisationen_US
dc.subjectGreedy genetic algorithmen_US
dc.subjectInteger linear programmingen_US
dc.subjectShortest processing timeen_US
dc.subjectLongest procressing timeen_US
dc.titleParallel processors scheduling algorithms to minimise makespan in a galvanising planten_US
dc.typeArticleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dewa_PISRJ_Vol76#3_9Pgs_2020.pdf
Size:
422.15 KB
Format:
Adobe Portable Document Format
Description: