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.

Collaborative spatial crowdsourcing leverages distributed workers' collective intelligence to accomplish spatial tasks. A central challenge is to efficiently assign suitable workers to collaborate on these tasks. Although mainstream reinforcement learning (RL) methods have proven effective in task allocation, they face two key obstacles: delayed reward feedback and non-stationary data distributions, both hindering optimal allocation and collaborative efficiency. To address these limitations, we propose CAFE (credit assignment and fine-tuning enhanced), a novel multi-agent RL framework for spatial crowdsourcing. CAFE introduces a credit assignment mechanism that distributes rewards based on workers' contributions and spatiotemporal constraints, coupled with bi-level meta-optimization to jointly optimize credit assignment and RL policy. To handle non-stationary spatial task distributions, CAFE employs an adaptive fine-tuning procedure that efficiently adjusts credit assignment parameters while preserving collaborative knowledge. Experiments on two real-world datasets validate the effectiveness of our framework, demonstrating superior performance in terms of task completion and equitable reward redistribution.

With the widespread adoption of mobile internet and GPS-enabled smartphones, spatial crowdsourcing has emerged as a prevalent computing paradigm. In this paradigm, the human-machine collaborative task assignment mode, which empowers workers to select tasks based on their preferences, has become a preferred approach for various applications such as ridesharing and takeaways. Generally, the platform continuously presents a set of top-$k$tasks to individual workers by taking into account factors like travel distance, and allows workers to select tasks from this set. This decision approach is beneficial to both platform and workers. However, it still faces significant challenges in large-scale dynamic results maintenance, which incurs considerable computational costs. In this paper, we propose a novel solution framework with an adaptive two-layer cache structure to efficiently address the problem of updating dynamic top-$k$results. Additionally, we propose two effective learning-based methods which greatly improve the efficiency of result maintenance. Furthermore, we present a novel approach to identify and process caches that trigger intensive updates within a tight time limit, greatly reducing the peak demand for updating caches. Finally, extensive experimental results on real datasets demonstrate that our proposed algorithms 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.

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.

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, namelyFairness-GuaranteedTaskAssignment (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.

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 calledPrediction-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 powerfulBilateral 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 aDeep 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.

Huge warship material transfer support operation is one of the critical factors affecting the comprehensive combat effectiveness of warships, which need to deliver essential combat materials to the target position quickly and safely. In this paper, considering the multi-stage and high dynamic characteristics of huge warship material transfer support, we model the operation scheduling optimization problem as a special flexible flow shop scheduling problem with some constraints. A greedy genetic algorithm based on dual-layer integer-coded is proposed to solve the transfer support model with the objective function of minimizing the maximum transfer completion time. Benchmark computation examples and a simulation environment are used in experiments, which verify the effectiveness of the algorithm in the actual transfer support operation.