This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm (DJS). Emphasizing on-demand resource sharing, typical to Cloud Service Providers (CSPs), the research focuses on minimizing job completion delays through efficient task allocation. Utilizing Johnson’s rule from operations research, the study addresses the challenge of resource availability post-task completion. It advocates for queuing models with multiple servers and finite capacity to improve job scheduling models, subsequently reducing wait times and queue lengths. The Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences, showcasing their efficacy through comparative analysis. The research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators, ultimately positioning the proposed technique as superior to existing approaches, offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

This paper presents a dynamic closed-loop supply chain (CLSC) model, incorporating a manufacturer, a retailer, and an internet recycling platform (IRP), utilizing differential game theory while considering the forgetting effect of consumers. The model encompasses factors such as the quality level of used products and Big Data marketing (BDM), comparing optimal equilibriums under decentralized and cooperative decision scenarios. To effectively coordinate the dynamic CLSC at each time point, we propose a revenue-sharing and cost-sharing (RSCS) combined contract. In addition to ensuring reasonable sharing of revenues and costs, this contract allows the manufacturer to flexibly adjust wholesale prices for final products and transfer prices for used products in order to distribute profits appropriately and achieve Pareto optimality within the CLSC system. Furthermore, our results indicate that there exists a threshold for Big Data marketing efficiency; high-efficiency BDM not only facilitates increased recycling on Internet platforms but also reduces unit recycling costs for enterprises. Interestingly, when implementing the combined contract, Big Data marketing efficiency does not impact the transfer price paid by manufacturers to Internet recycling platforms.

Using Lean Manufacturing (LM) tools in production processes is crucial for companies’ economic, environmental, and social sustainability success. This study shows a structural equation model (SEM) that shows the relationship between LM Tools like Kaizen (KAI), Gemba (GEM), Value Stream Mapping (VSM) and Key Performance Indicator (KPI) with Economic Sustainability (ECS). Seven hypotheses were evaluated with data from 179 responses to a questionnaire about the Mexican maquiladora industry, showing that these variables are linked. At a 95% confidence level, the model was evaluated using the partial least squares method. The findings indicate that the relationships between KAI and GEM and KAI and VSM have the strongest relationship, followed by VSM and ECS; however, VSM has the strongest effect on ECS. Based on these findings, it is recommended that managers adopt a continuous improvement (KAI) approach based on working directly on the shop floor (GEM and VSM) to support their decisions regarding economic growth (ECS).

Preference prediction is the building block of personalized services, and its implementation at the group level helps enterprises identify their target customers effectively. Existing methods for preference prediction mainly focus on behavioral interactions to extract the associations between groups and products, ignoring the importance of other auxiliary records (e.g., online reviews and social tags) in association detection. This paper proposes a novel method named GMAT for group preference prediction, aiming to collectively detect the sophisticated association patterns from user generated content (UGC) and behavioral interactions. In doing so, we construct a tripartite graph to collaborate these two types of data, and design a deep-learning algorithm with mutual attention module for generating the contextualized representations of groups and products. Extensive experiments on two real-world datasets show that GMAT is superior to other baselines in terms of group preference prediction. Additionally, GMAT is able to improve prediction accuracy compared with its different variants, further verifying the proposed method’s effectiveness on association pattern detection.

Aiming at the design problem of aviation swarm combat course of action (COA), considering the influence of stochastic parameters in the causal relationship model and optimization problem model, according to the dynamic influence net (DIN) theory, stochastic simulation technique, feedforward neural network (FNN) function approximation technique and multi-objective artificial fish school algorithm (MOAFSA), this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm combat. First, on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling, the static and dynamic causal relationship modeling and optimization problem modeling were carried out respectively. Second, the probability propagation mechanism of DIN was established, which mainly included two aspects, i.e., the overall process and the specific algorithm. Then, input and output data were generated based on stochastic simulation. According to these data, FNN was adopted for function approximation, and MOAFSA was adopted for iterative optimization. Finally, the rationality of the model, and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases.