With the rapid development and implementation of ICT, academics and industrial practitioners are widely applying robotic process automation (RPA) to enhance their business processes and operational efficiencies. This paper intends to address the value creation of utilizing RPA under the cloud-based Cyber-Physical Systems (CPS) in Robotic Mobile Fulfillment System (RMFS). By providing a TO-BE analysis of RPA and cloud-based CPS framework, a data-driven approach is proposed for zone clustering and storage location assignment classification in RMFS. The purpose of the paper is to gain better operational efficiency in RMFS. A modified A* algorithm is adopted for calculating the total traveling cost of each moveable rack in the case company layout. Nine common clustering algorithms are applied for the RMFS’s zone clustering. The results from the zone clustering are considered as nine scenarios for data-driven order classification to solve the storage location assignment problem. Six common classification algorithms are applied for a detailed comparison which has been conducted with thousands of orders. The results reveal that K-means, Gaussian Mixture Models, and Bayesian Gaussian Mixture Model are worked well with six supervised classification algorithms which yield an average of 95% accuracy rate and a higher customers’ expectation can be achieved under the customer-driven e-commerce economy.

随着 ICT 的快速发展和实施，学术界和工业从业者广泛应用机器人流程自动化 (RPA) 来增强其业务流程和运营效率。本文旨在解决在机器人移动履行系统 (RMFS) 中基于云的网络物理系统 (CPS) 下利用 RPA 的价值创造问题。通过提供 RPA 和基于云的 CPS 框架的 TO-BE 分析，提出了一种数据驱动的方法，用于 RMFS 中的区域聚类和存储位置分配分类。本文的目的是在 RMFS 中获得更好的运行效率。采用改进的A*算法计算案例公司布局中每个移动货架的总行驶成本。RMFS 的区域聚类应用了九种常见的聚类算法。区域聚类的结果被认为是数据驱动订单分类的九种场景，以解决存储位置分配问题。应用六种常用分类算法进行详细比较，已对数千个订单进行了详细比较。结果表明，K-means、Gaussian Mixture Models 和 Bayesian Gaussian Mixture Models 与六种监督分类算法配合良好，平均准确率达到 95%，并且在客户驱动的 e-商贸经济。