Abstract
As an important part of Industry 4.0, a smart warehouse can offer smart tips and operational constraints for users. Improving its work efficiency is a promising growth driver for logistics companies and retailers. Therefore, a reinforcement-learning-based adaptive iterated local search (RAILS) approach is proposed to improve order-picking efficiency for a smart warehouse. A batching algorithm is proposed to deal with fluctuating orders efficiently and quickly obtain a high-quality initial solution. It can speed up the search for near-optimal solutions by extracting and using the features of the orders. Then, a perturbation mechanism is designed based on reinforcement learning that can adaptively select the perturbation type and determine the perturbation strength instead of a random way. Experimental results demonstrate that the proposed approach outperforms several existing ones, and its superiority becomes more significant as problems scale up.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
