Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2022

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 propose Efficient Adaptive Task Planning, a framework for large-scale robotized warehouses with time-varying item arrivals. It adaptively selects racks to fulfill at each timestamp via rein-forcement learning, accounting for the time-varying bottleneck of the fulfillment cycles. Then it finds paths for robots to transport the selected racks. The framework adopts a series of efficient optimizations on both time and memory to handle large-scale item throughput. Evaluations on both synthesized and real data show an improvement of 37.1% in effectiveness and 75.5% in efficiency over the state-of-the-arts.

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE), Kuala Lumpur, 2022 May 9-12

First Page

3327

Last Page

3339

Identifier

10.1109/ICDE53745.2022.00314

Publisher

IEEE

City or Country

Kuala Lumpur, Malaysia

Additional URL

http://doi.org/10.1109/ICDE53745.2022.00314

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