Studies on Learning to Extend and Improve Scheduling Algorithms for Batch Chemicals Production

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The objective of this research is to explore the use of automatic learning techniques to enhance the performance of scheduling algorithms. The work combines operations research and artificial intelligence methodologies to exploit the structure of the scheduling problem and the morphology of the data. The expected results are fundamental advances in the science base of planning and scheduling problems and the development of strategies for the efficient solution of practical batch scheduling problems.