Baolong Mei

The rapid growth of e-commerce has driven an increasing demand for robotized warehouses to handle large-scale logistics orders. Upon receiving orders, a warehouse engages in task planning that involves two crucial stages: matching the orders with racks that contain the required items, and planning the robot routes to deliver those racks for order fulfillment. Hence, effective task planning is essential for maximizing order throughput. However, while existing techniques perform well for orders that involve items from a single rack, they exhibit low efficiency and poor performance when dealing with complex orders that require multiple items from different racks. In this paper, we introduce the robotized warehouse complex task planning problem and propose a novel Complex Order Online Planning (COOP) framework to address the challenge. Specifically, the framework matches orders with racks using a maximal coverage matching method, optimized through vector similarity search and a residual matching strategy. Then, it adopts an effective progressive prioritized pathfinding algorithm to transport matched racks with minimal delivery cost. Finally, the framework introduces an enhanced pathfinding-aware rack selection model that considers rack delivery costs from the pathfinding stage to collaboratively optimize rack matching and overall planning scheme. Extensive experiments on real-world and synthetic datasets demonstrate that our approaches exhibit strong performance across various parameter configurations.

Social images shared on social media are often associated with geo-tagged information and text descriptions. Given a set of keywords and a spatial location, geo-tagged social image search can retrieve top-k image objects that best match query parameters in terms of spatial distance and tag similarity of social images. However, due to improper parameter settings, users may notice that some expected images are missing and wonder why these objects do not appear in the query results. This paper studies the why-not top-k social image search question and proposes efficient query refinement algorithms, aiming to minimally modify users' initial queries to reintroduce missing objects. We first develop a baseline algorithm that traverses each possible query parameter sequentially to find the best refinement parameters. Then, we propose a fast search algorithm with two optimization strategies named lower ranking nodes pruning and early stop pruning, which can improve performance by quickly removing low-ranking social images. In addition, we propose an efficient boundary search algorithm that can determine the ranking of missing images at a low time cost. We also extend the proposed techniques to handle multiple missing images. Extensive experimental results demonstrate that the proposed solution is two orders of magnitude faster than baseline and is effective in a wide range of settings.

With the rapid development of wireless networks and smart devices, spatial crowdsourcing (SC) has become increasingly prevalent. The key issue in SC is efficiently assigning spatial tasks, such as parcel and food delivery, to mobile workers in order to maximize platform utility. Existing works mainly focus on task assignment based on real-time spatio-temporal constraints of workers and tasks, neglecting the influence of future spatio-temporal distributions of tasks on current assignments. In this paper, we propose a novel problem in SC called Prediction-aware Task Assignment (PTA) , where the platform adaptively assigns spatial tasks to workers by considering their current and future spatio-temporal constraints to maximize overall platform revenue. To address this problem, we introduce a two-stage framework composed of task prediction and task assignment. In the task prediction stage, we develop a powerful Bilateral Spatial-Temporal Graph Convolutional Network (BSTGCNet) to predict the time and location where potential tasks may appear in the future. In the task assignment stage, we present a Deep Reinforcement Learning (DRL) approach to dynamically partition tasks into batches based on the current and future status of tasks, and conduct bipartite graph matching for spatial tasks and workers in a batch-wise manner. Finally, extensive experiments on real-world datasets validate the effectiveness and efficiency of our proposed solution.

As a new computing paradigm, crowdsourced mobility service is booming with the rapid development of sharing economy. In the typical crowdsourced mobility service, a large number of part-time workers perform the spatial tasks offered by the platform and share the benefits in proportion, thereby, the strategy of task assignment directly affects the level of revenue and fairness among workers. In order to balance the revenue and fairness of workers, in this paper we study a novel type of fairness-aware spatial crowdsourcing problem, namely F airness- G uaranteed T ask A ssignment (FGTA), which aims to maximize the total revenue of workers at a certain level of fairness guarantee and that is proved to be NP-hard. To solve this problem, we propose an efficient game-theory based approach for task assignment, which makes use of the best-response framework to iteratively select the best strategy for each worker until a Nash equilibrium is reached. Inspired by the observation that tasks with similar spatial and temporal features can be assigned together to a worker, we propose a spatial-temporal grouping based optimization to further improve the efficiency of task assignment. Furthermore, to improve the quality of Nash equilibrium, we present an effective large neighborhood search based optimization that trains a DQN decision model as destroy operator to accelerate the convergence of optimal task assignment. Finally, extensive experiments conducted on two real-world datasets demonstrate that our proposed approaches achieve better effectiveness and efficiency than the state-of-the-arts.