ABSTRACT
We introduce a real-world job shop scheduling problem where the objective is to minimize configuration costs that depend on the sliding pairwise similarity between two assets ordered one after the other in a processing batch. This implies that our fundamental challenge is to learn from the costs what constitutes asset similarity in the context of batching locally and optimizing a multi-line end to end. We present a 3 component scheduling system: simulator, scheduler and hyper-optimizer. The scheduler relies upon a machine learning algorithm -- hierarchical clustering, to select, from an entry yard, assets for a batch based on weighted similarity. It then utilizes a weighted distance matrix to sequence the assets. The weights used by the scheduler are optimized online with an evolutionary algorithm.
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Index Terms
- Multi-Line Batch Scheduling by Similarity
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