The rapid growth of e-commerce has intensified the demand for efficient urban logistics. Electric Vehicles (EVs), with their eco-friendly and high-efficiency features, have emerged as a promising solution for improving urban logistics efficiency. However, due to their limited battery capacity, EVs often require recharging during operations, and improper charging decisions may lead to delivery delays, resulting in a loss of platform revenue. In this paper, we explore a novel EV Charging-Aware Task Assignment (ECTA) problem in urban logistics scenarios, where the objective is to maximize platform revenue by ensuring timely task completion while meeting the charging needs of EVs. To address this challenge, we present e-Charge, an efficient two-stage framework that enables real-time optimization of two continuous processes: task assignment and charging decision. For task assignment, which focuses on matching tasks to suitable EVs, we construct a hybrid weight model that incorporates charging penalties to calculate matching weights for EVs in both active and charging states, thus improving task assignment quality. Additionally, we implement an effective vehicle selection strategy to expedite the matching process, ensuring the efficiency of task assignment. For charging decision, which focuses on determining when and where EVs should be charged, we propose a multi-agent reinforcement learning (MARL) approach to dynamically select the charging timing for EVs. To further enhance decision-making quality, we devise a hierarchical communication graph that enables better collaboration between EVs and facilitates adaptive charging decisions. Finally, extensive experiments demonstrate thate-Chargesignificantly outperforms compared methods, achieving higher revenue and task completion ratio across a wide range of parameter settings.

Top- k Publish/Subscribe (TkPS) service is widely studied in spatial database, with various cache-based methods proposed to address its efficiency challenge in top- k result maintenance. These methods require in-depth exploration of relationships between cache updates and different factors (e.g., data distribution) to optimize cache performance. However, there is currently no system available that assists developers in conducting comprehensive cache analyses within TkPS services. We therefore introduce Catcher , a multi-functional cache analysis system designed for TkPS services. It not only enables users to intuitively analyze the entire maintenance process of top- k results but also aids in identifying bottlenecks and potential optimization spaces of caches. Catcher provides two user-friendly interfaces that allow users to employ simple and easy-to-use consoles to perform statistical analysis. Furthermore, Catcher offers the real-time evaluation of cache-based methods, providing users with instant analysis. We have demonstrated the usability of Catcher on real-world datasets. A short video of our demonstration can be found at https://youtu.be/qI81HoypB0w.

The wide adoption of GPS-enabled smart devices has greatly promoted spatial crowdsourcing, where the core issue is how to assign tasks to workers efficiently and with high quality. In this paper, we build a novel visual analysis system for spatial crowdsourcing, namely AMRAS, which can not only intuitively present the task allocation for workers under different time window scales to users (e.g., data analysts and managers) in real-time, but also help users analyze task assignment decision model and its learning process. AMRAS has the following novel features. First, AMRAS provides two user-friendly interfaces that allow users to employ simple and easy-to-use console to perform statistical analysis. Secondly, AMRAS provides three powerful visualization tools, such as the visualization of assignment results, assignment process, and assignment decision model, which not only allow users to intuitively analyze the whole process of task assignment, but also help users discover the computational bottleneck of their task assignment solution. Finally, AMRAS enables online access to real-time data, providing users with instant assignment and instant analysis. We have implemented and deployed AMRAS on Alibaba Cloud and demonstrated its usability and efficiency in real-world datasets. The demonstration video of AMRAS has been uploaded to Google Drive.