Optimizing Efficiency and Fairness in Collaborative Human-Robot Order Picking Systems
A novel multi-objective Deep Reinforcement Learning approach that learns allocation policies to jointly optimize efficiency and workload fairness in collaborative human-robot order picking systems.
