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Computational study demonstrates improved bi-criteria job scheduling across parallel machines using a hybrid flower pollination algorithm, suggesting greater efficiency for complex computing...
Parallel machine scheduling and its complex combinatorial optimisation problems are widely studied. A lot of research has been carried out in this area to determine how to effectively schedule parallel execution of different jobs to be carried out using different machines in order to achieve the optimisation criteria. In this paper, the formulations of multi-objective flower pollination algorithm obtained by the combination of weighted objectives or secondary storage for managing possible solutions along with their hybrids with genetic algorithm have been developed and applied to the problem of scheduling jobs on machines running in parallel in order to optimise bi-criteria, namely, maximum tardiness and weighted flow time. It has been empirically verified that multi-objective flower pollination algorithm using secondary storage and hybridised with genetic algorithm leads to better results as compared to the existing algorithms. The results obtained have been verified by numerical illustrations and statistical tests.
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Jeet et al. (2016) studied this question.
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