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A heuristic-based genetic algorithm for the two-machine flowshop scheduling with learning consideration
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DOI：10.1016/j.jmsy.2015.02.002.png)
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In scheduling problems, the learning phenomenon is often seen in some practical applications such as in the processing of certain chemicals in oil refineries and in the steel plates or bars produced by a foundry. A review of the literature reveals that most researchers paid more attention to the scheduling with both the single-machine settings and the learning without a bound. This is at odds with reality and thereby highlights the importance of addressing the issue by different approaches. This paper tackles the issue by considering a two-machine flowshop problem with a truncated learning consideration where the objective function is to minimize the makespan. In order to solve the proposed model, a branch-and-bound algorithm is first developed for the optimal solution. Then four genetic heuristic-based algorithms are proposed for the near-optimal solution. In addition, the experimental results of all proposed algorithms are also provided. (C) 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
Keyword:
Scheduling
Two-machine
Makespan
Truncated learning effect
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