Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative
  Human-Robot Order Picking
Igor G. Smit Zaharah Bukhsh Mykola Pechenizkiy Kostas Alogariastos Kasper Hendriks Yingqian Zhang
In collaborative human-robot order picking systems, human pickers and
Autonomous Mobile Robots (AMRs) travel independently through a warehouse and
meet at pick locations where pickers load items onto the AMRs. In this paper,
we consider an optimization problem in such systems where we allocate pickers
to AMRs in a stochastic environment. We propose a novel multi-objective Deep
Reinforcement Learning (DRL) approach to learn effective allocation policies to
maximize pick efficiency while also aiming to improve workload fairness amongst
human pickers. In our approach, we model the warehouse states using a graph,
and define a neural network architecture that captures regional information and
effectively extracts representations related to efficiency and workload. We
develop a discrete-event simulation model, which we use to train and evaluate
the proposed DRL approach. In the experiments, we demonstrate that our approach
can find non-dominated policy sets that outline good trade-offs between
fairness and efficiency objectives. The trained policies outperform the
benchmarks in terms of both efficiency and fairness. Moreover, they show good
transferability properties when tested on scenarios with different warehouse
sizes. The implementation of the simulation model, proposed approach, and
experiments are published.