MAXIMUM COMPLETION TIME UNDER LEARNING EFFECT IN PERMUTATION FLOWSHOP SCHEDULING PROBLEM
DOI:
https://doi.org/10.23055/ijietap.2018.25.2.3179Keywords:
learning effect, flowshop, makespan, genetic algorithm, kangaroo algorithm, variable neighborhood search algorithmAbstract
Permutation flowshop scheduling problem under position based learning effect is addressed in this study. Minimization of the maximum completion time is considered for the identified problem. The mathematical programming model is established to find optimal solutions for small-sized problems. Meta-heuristics such as population based genetic algorithm and single solution based kangaroo and variable neighborhood search algorithms are developed to achieve effective solutions for large scale problems encountered in the real applications. In addition, different solution methods which are in the literature for similar problem strucrutes, are also used. Improved heuristics are evaluated according to optimal results for small-sized problems and according to performance difference between each other for large scale problems.Published
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