Combining Heuristics and Machine Learning for Hybrid Flow Shop Scheduling Problems

Miriam Zacharias
This dissertation presents a new generic scheduling approach to makespan and flowtime minimization in hybrid flow shops with unrelated machines. In addition, possibilities of utilizing machine learning tools in job scheduling are explored.The proposed scheduling approach is twofold. First, a new heuristic based on the divide et impera strategy is introduced, that can be optionally enhanced by different local search improvement schemes. Moreover, several parameters are provided that enable a trade-off between solution quality and...
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