A multi-agent reinforcement learning model with hierarchical parallel decoding achieves state-of-the-art min-max travel distance on mixed-shelves picker routing and generalizes to larger instances.
European Journal of Operational Re- search 262(2), 550–562 (2017)
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Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding
A multi-agent reinforcement learning model with hierarchical parallel decoding achieves state-of-the-art min-max travel distance on mixed-shelves picker routing and generalizes to larger instances.