Task scheduling (TS) and multi-agent-path-finding (MAPF) are two cruxes of pickup-and-delivery in automated warehouses. In this paper, the two cruxes are optimized simultaneously. Firstly, the system model, task model, and path model are established, respectively. Then, a task scheduling algorithm based on enhanced HEFT, a heuristic MAPF algorithm and a TS- MAPF algorithm are proposed to solve this combinatorial optimization problem. In EHEFT, a novel rank priority rule is used to determine task sequencing and task allocation. In MAPF algorithm, a CBS algorithm with priority rules is designed for path search. Subsequently, the TS-MAPF algorithm which combines EHEFT and MAPF is proposed. Finally, the proposed algorithms are tested separately against relevant typical algorithms at different scales. The experimental results indicate that the proposed algorithms exhibited good performance.

In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

Robotic Mobile Fulfillment System (RMFS) affects the traditional scheduling problems heavily while operating a warehouse. This paper focuses on storage assignment optimization for Fishbone Robotic Mobile Fulfilment Systems (FRMFS). Based on analyzing operation characteristics of FRMFS, a storage assignment optimization model is proposed with the objectives of maximizing operation efficiency and balancing aisle workload. Adaptive Genetic Algorithm (AGA) is designed to solve the proposed model. To validate the effectiveness of AGA in terms of iteration and optimization rate, this paper designs a variety of scenarios with different task sizes and storage cells. AGA outperforms other four algorithm in terms of fitness value and convergence and has better convergence rate and stability. The experimental results also show the advancement of AGA in large size FRMFS. In conclusion, this paper proposes a storage assignment model for FRMFS to reduce goods movement and travel distance and improve the order picking efficiency.

End-to-end data-driven prediction methods are essential for effective prognostics and health management, enabling accurate and timely predictions of remaining useful life (RUL) in complex systems. These methods reduce reliance on manual features and expert knowledge, making them crucial for industries like aerospace and manufacturing. Existing deep learning-based RUL prediction methods demonstrate strong predictive performance by integrating spatiotemporal features from time-series data. However, as the depth of these models increases, their complexity and computational demands grow rapidly, and these models have their own limitations. Moreover, for RUL prediction tasks, these models may not always be necessary, especially when simpler architectures can deliver competitive results. Models based on the simple architecture of multilayer perceptrons (MLPs) often perform poorly in RUL prediction tasks due to limitations, such as their inability to capture spatial features. In light of these challenges, we propose the recurrent mixing MLP (RMixMLP), a fully MLP-based block structure designed to capture both channel and temporal information while addressing the limitations of deeper models. The RMixMLP comprises two components: (1) the mixing MLP (MixMLP), which integrates a series-MixMLP to extract temporal features and a channel-MixMLP to combine channel features, and (2) a self-recurrent framework that allows parameter sharing within the MixMLP block, ensuring data integrity and controlling model complexity. Data processed through stacked RMixMLP blocks are fed into a fully connected layer for RUL prediction, eliminating the need for other deep learning methods. The proposed technique was validated on the company-modular aero-propulsion system simulation aircraft turbofan engine dataset, with experimental results demonstrating that RMixMLP improves prediction accuracy by 9.7% compared to recent state-of-the-art models. At the same time, compared with the traditional MLP-based model, RMixMLP not only reduces the number of model parameters, but also improves the prediction performance by 20%.