Dear Editor, This letter presents a multi-automated guided vehicles (AGV) routing planning method based on deep reinforcement learning (DRL) and recurrent neural network (RNN), specifically utilizing proximal policy optimization (PPO) and long short-term memory (LSTM). Compared to traditional AGV pathing planning methods using genetic algorithm, ant colony optimization algorithm, etc., our proposed method has a higher degree of adaptability to deal with temporary changes of tasks or sudden failures of AGVs. Furthermore, our novel routing method, which uses LSTM to take into account temporal step information, provides a more optimized performance in terms of rewards and convergence speed as compared to existing PPO-based routing methods for AGVs.

In the realm of online learning, where resources are abundant, it is essential to customize recommendations and plans to meet individual learning needs. This involves not only identifying and addressing areas of weakness but also aligning the learning journey with each learner’s cognitive preferences. However, existing methods for suggesting and structuring learning paths have notable limitations. To address these challenges, this paper introduces a knowledge graph-based personalized learning path planning method (KG-PLPPM). By leveraging a knowledge graph and refining cognitive diagnosis models, the proposed method tailors learning paths to individual needs. It evaluates knowledge concept similarity and learner mastery, and employs an algorithm for path planning. In the experiments, two metrics—the concept sequence degree and learning efficiency—are used to assess our work. Experimental results demonstrate that the method presented enhances the coherence and relevance of recommended learning paths, and achieves a higher concept sequence degree, indicating that knowledge concepts are arranged in a manner consistent with the learning sequence, which aligns more closely with learners’ cognitive preferences. Moreover, across various learning progresses and path lengths, it effectively addresses weak knowledge areas, significantly enhancing learning efficiency.

The last decade has brought the blooming of shuttle-based automated storage and retrieval system (SBS/RS) in many industrial applications thanks to its high throughput and expandability in handling mini-load commodity. In the paper, we focus on a double-deep tier-captive SBS/RS. Specifically, we propose a simulation model to support analyzing the performance related data of a double-deep tier-captive SBS/RS with both single-elevator and double-elevator patterns. The data includes shuttle carrier waiting time, elevator idle time, and total outbound time. Then, the impact of system performance due to two different elevator-patterns has been analyzed quantitatively. Based on the model, an optimization model and an improved NSGAII algorithm for task scheduling are designed, which can make the SBS/RS achieve the shortest shuttle carrier waiting time, rearrangement time and total outbound time. Experiments are conducted considering diverse task sizes and load factors, and the two elevator-patterns in a SBS/RS. The results show that our work can optimize the performance of the SBS/RS and assist stakeholders to decide more economical elevator-pattern.

Graph partitioning is an NP-hard combinatorial optimization problem, and is a fundamental step in distributing workloads on parallel compute systems, circuit placement, and sparse matrix reordering. The proposed heuristic algorithms such as streaming graph partitioning provide solutions to large-scale graph in a reasonable amount of time. However, the ability of breaking out of local minima in existing these methods is very limited as they are simple in reflecting the connectivity between vertices in real graphs with power-law distribution characteristic. As hill climbing algorithm is a local search method, it can be adopted to improve the result of graph partitioning. However, directly adopting the existing hill climbing algorithm to graph partitioning will result in local minima and poor convergence speed during the iterative process. In this paper, we propose an improved hill climbing graph partitioning algorithm based on clustering. Instead of taking a single vertex as a basic unit, the proposed method considers a cluster consisting of a series of vertices as a hill to move during each iteration. The method uses a new metric that considers both balance and edgecuts to look for the most beneficial cluster as the hill. With these improvements, the method provides a strong power to break out of local minima and achieve an adaptive tradeoff between balance and edgecuts. Experimental results on real-world graphs show that the proposed algorithm substantially reduces edgecuts within a controlled imbalance range.