摘要
机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑发布了关于人工智能的新研究结果。根据NewsRx C或受访者来自智利圣地亚哥的新闻，研究表明："智能交通和先进的移动技术专注于帮助运营商有效地管理智能城市的导航任务，提高成本效率，提高安全性，并减少COS Ts。"这项研究的财政支持者包括Anid。我们的新闻记者从安德烈斯贝洛大学的研究中获得了一句话：“尽管该领域在开发智能城市的大规模监控方面取得了重大进展，但在向客户订单分配送货人员方面仍然存在一些挑战。为了解决这个问题，本文提出了一种优化配送人员任务分配问题的体系结构，提出了利用确定性和机器学习技术得到的不同成本函数，特别比较了线性回归和多项式回归方法构造具有订单和配送人员信息的矩阵表示的不同成本函数的性能，并应用匈牙利优化算法求解了配送人员任务分配问题。结果表明，与其他方法相比，线性回归用于估计距离信息时，我们的数据集的估计误差最大可减少568.52 km(1.51%)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news originating from Santiago, Chile, by NewsRx c orrespondents, research stated, “Intelligent transportation and advanced mobilit y techniques focus on helping operators to efficiently manage navigation tasks i n smart cities, enhancing cost efficiency, increasing security, and reducing cos ts.” Financial supporters for this research include Anid. Our news reporters obtained a quote from the research from University of Andres Bello: “Although this field has seen significant advances in developing large-sc ale monitoring of smart cities, several challenges persist concerning the practi cal assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with determin istic and machine learning techniques. In particular, we compared the performanc e of linear and polynomial regression methods to construct different cost functi ons represented by matrices with orders and delivery people information. Then, w e applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate t hat when used to estimate distance information, linear regression can reduce est imation errors by up to 568.52 km (1.51%) for our dataset compared to other methods.”
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