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1057. Learning Efficient and Fair Policies for Collaborative Human-Robot Order Picking
Invited abstract in session MC-3: (Deep) Reinforcement Learning for Combinatorial Optimization 1, stream Data Science Meets Optimization.
Monday, 12:30-14:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Igor Smit
|
| Eindhoven University of Technology | |
| 2. | Zaharah Bukhsh
|
| Industrial Engineering, TU Eindhoven | |
| 3. | Yingqian Zhang
|
| Industrial Engineering, TU Eindhoven |
Abstract
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 good 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 network architecture that captures regional information and extracts information from efficiency and workload features effectively. 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. The trained policies outperform the benchmarks in terms of efficiency and fairness, and moreover, they show good transferability properties when being tested with different scenarios in different sizes of the warehouse.
Keywords
- Artificial Intelligence
- Combinatorial Optimization
- Multi-Objective Decision Making
Status: accepted
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