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چکیده
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This paper explores a no-wait hybridflow shop scheduling problem (NWHFSSP) with realistic assumptions, including unrelated parallel machines at each stage, machine eligibility, sequence-dependent set-up times and different ready times, in order to minimise the mean tardiness. The largest position value rule is proposed to transmute continuous vectors of each solution into job permutations. Also, a novel biogeography-based optimisation (BBO) algorithm is developed to solve the aforementioned problem. To evaluate the effect of various parameters on the performance of the proposed BBO algorithm, response surface methodology (RSM) is employed. Production scenarios for small-scale and large-scale problems are created and tested for the validation purposes. Computational experiment results indicate that the proposed BBO outperforms all of the tested algorithms in terms of four measures, namely, mean relative percentage deviation (RPD), standard deviation of RPD, best RPD and worst RPD. It is shown that BBO produces the best solutions among the tested algorithms in terms of not only the four RPD measures but also computation time
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