SOAR: Deep RL Framework for Real-Time Warehouse Optimization
A recent study presents SOAR, a comprehensive Deep Reinforcement Learning framework designed for the real-time joint optimization of order allocation and robot scheduling within Robotic Mobile Fulfillment Systems (RMFS). These systems utilize mobile robots for automated inventory transport, yet the task of optimizing order allocation alongside robot scheduling proves difficult due to stringent real-time demands and the complex interdependence of multi-phase decisions. Current approaches either break the issue into separate sub-tasks, compromising global optimality for quicker responses, or depend on costly global optimization models that do not fit dynamic industrial settings. SOAR fills this void by integrating order allocation and robot scheduling into a single process, employing soft order allocations as observations. It establishes an Event-Driven Markov Decision Process for concurrent scheduling in reaction to asynchronous events. This research is available on arXiv under identifier 2605.03842.
Key facts
- SOAR is a Deep Reinforcement Learning framework for RMFS.
- It jointly optimizes order allocation and robot scheduling in real time.
- Existing methods either decompose the problem or use expensive global models.
- SOAR uses soft order allocations as observations.
- It formulates an Event-Driven Markov Decision Process.
- The paper is on arXiv with ID 2605.03842.
- RMFS relies on mobile robots for automated inventory transportation.
- The framework enables simultaneous scheduling in response to asynchronous events.
Entities
Institutions
- arXiv