Adaptive Task Planning for Large-Scale Robotized Warehouses
Dingyuan Shi, Yongxin Tong, Zimu Zhou, Ke Xu, Wenzhe Tan, Hongbo Li

TL;DR
This paper introduces an adaptive task planning framework for large-scale robotized warehouses that optimizes robot paths and rack selection using reinforcement learning to handle time-varying item arrivals and improve throughput.
Contribution
It proposes a novel adaptive task planning approach called Efficient Adaptive Task Planning that effectively manages large-scale, dynamic warehouse operations, outperforming existing methods.
Findings
37.1% improvement in effectiveness
75.5% increase in efficiency
Effective handling of time-varying item arrivals
Abstract
Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. Direct extensions from state-of-the-art path finding methods are ineffective to solve the TPRW problem because they fail to adapt to the bottleneck variations of fulfillment cycles. In response, we…
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Taxonomy
TopicsTransportation and Mobility Innovations · Advanced Manufacturing and Logistics Optimization · Optimization and Search Problems
