Robotics & Machine Learning Daily News2024,Issue(Jun.26) :32-32.

Findings from Hefei University of Technology in the Area of Robotics Reported (A Deep Reinforcement Learning Hyper-heuristic To Solve Order Batching Problem Wit h Mobile Robots)

合肥工业大学在机器人领域的研究成果报告(一种用移动机器人解决订单批量问题的深度强化学习超启发式)

Robotics & Machine Learning Daily News2024,Issue(Jun.26) :32-32.

Findings from Hefei University of Technology in the Area of Robotics Reported (A Deep Reinforcement Learning Hyper-heuristic To Solve Order Batching Problem Wit h Mobile Robots)

合肥工业大学在机器人领域的研究成果报告(一种用移动机器人解决订单批量问题的深度强化学习超启发式)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的新研究是一份报告的主旨。摘要:根据NewsRx编辑在安徽的新闻报道,研究表明:“在电子商务物流中,提高拣货系统的效率至关重要。受自动化物流应用的启发,我们考虑了基于移动机器人的拣货问题。”本研究经费来源于国家自然科学基金(NSFC)。我们的新闻记者从合肥工业大学的研究中得到一句话:“在这个问题上,”摘要:移动机器人将货架搬运到拣货站进行拣货,然后将货架退回.目标是在最小化延迟订单数量的同时减少货架数量.提出了一种基于深度强化学习的超优算法来优化系统中的订单分批策略.该方法自适应地选择订单分批策略.通过大量的测试,证明了该方法在一系列测试场景中的优越性,结果表明,该方法在一系列测试场景中优于其他启发式方法,提供了更加稳定有效的解决方案。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news reporting out of Anhui, People's Republic of Ch ina, by NewsRx editors, research stated, "In e-commerce logistics, it is critica l to enhance the efficiency of the order-picking system. Motivated by applicatio ns of automatic logistics, we consider the mobile robot based order batching pro blem." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Hefei Universit y of Technology, "In this problem, mobile robots carry shelves to the picking st ation for order picking and then return them. The objective is to reduce shelf m ovements while minimizing the number of delayed orders. We introduce a hyper-heu ristic method based on deep reinforcement learning to optimize the order batchin g strategy in the system. The proposed method adaptively selects the order batch ing strategy, significantly improving the sequential decision-making process in order picking. Through extensive tests, we demonstrate the superiority of the pr oposed method over several existing heuristic methods in a range of test scenari os. The results show that the proposed method outperforms other existing heurist ic methods in a range of test scenarios, offering more stable and effective solu tions."

Key words

Anhui/People's Republic of China/Asia/Emerging Technologies/Machine Learning/Nano-robot/Reinforcement Learning/Ro bot/Robotics/Hefei University of Technology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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