In the context of manufacturing platformization, a growing number of businesses are adopting cloud manufacturing platforms (CMPs) across various scales. To boost competitiveness and network externalities, CMPs offer value-added services to both suppliers and demanders, often necessitating the sharing of proprietary information. It is crucial for CMP operations to encourage information sharing and select service strategies that maximize benefits for all participants within the platform. This paper develops a tripartite evolutionary game theory model that simulates interactions among numerous participants and describes dynamic game processes more comprehensively than traditional game theories. It is used to analyze strategic decisions of cloud manufacturing platforms (CMPs) that utilize proprietary information to provide value-added services. The study analyzes evolutionary stability and conducts numerical simulations based on real-world scenarios. Findings suggest that the platform and suppliers play dominant roles in this tripartite evolution, with their cooperation encouraging demanders to share more information. These insights are valuable for the future management of CMPs.

In the V2G (Vehicle to Grid) interaction scenario, there is a learning process for obtaining optimal strategies due to the gradual rationality of electric vehicles and micro-grids. This paper compares four evolutionary algorithms-replicator dynamics, reinforcement learning, belief learning, and experience-weighted attraction (EWA)-in V2G interactions according to different interaction scenarios. By constructing a V2G game model and performing simulations, we evaluate these algorithms in terms of information processing, learning speed, and equilibrium results. The EWA algorithm demonstrates superior efficiency and stability, making it a promising tool for V2G strategy optimization. This study provides insights into the application of learning algorithms in V2G games, offering guidance for future IoV (Internet of Vehicles) developments, especially in grid load management and energy dispatch.

The Internet-of-Things (IoT) technology realizes deep integration of the physical process and production information in a manufacturing workshop through the real-time acquisition of data and seamless interaction of equipment, showing valuable academic and applicable potential in upgrading the workshop environment and eliminating the effect of dynamics. This brings a new opportunity to increase workshop productivity. However, how to realize active perception, dynamic optimization, and real-time regulation of the manufacturing process based on IoT technology is a research hotspot facing technical and theoretical challenges. To address these issues, this study constructs a multiagent-based dynamic scheduling (MADS) workshop IoT architecture by embedding advanced ultrawide band (UWB) and 5th-generation (5G) communication technologies. Differing from conventional scheduling models, the proposed model optimally assigns processes to machines by designing a resource scheduling agency (RS Agency). The$NP$-complete of the multiobjective optimization dynamic job-shop scheduling problem is proved and reduced to a cooperative bargaining game negotiation model (CBGNM) to rationalize the allocation of workshop resources. Then, a multiobjective weights tuning scheme enables effective and efficient responses to workshop exceptions. The performance comparisons with public data of the Kacem$8\times 8$benchmark verify that the CBGNM has better results than the conventional scheduling methods and reaches maximum optimization of 41.7%, 39.3%, 47.2%, 32.9%, 54.5%, and 38.4% regarding makespan, critical machine workload, total processing time, fairness index, total energy consumption, and comprehensive index, respectively. The numerical experiments show that the game players have no significant coupling with their joint payoff and validate that their bargaining power varies with abnormal events.

This paper studies a traffic control algorithm for multiple Automated Guided Vehicle (multi-AGV) path planning based on system-level scheduling. This paper analyzes the multi-AGV path planning, combined with the collusion in the process of multi-AGV transportation and the causes of AGV scheduling deadlock, designed a rotational anti-deadlock algorithm based on time-slice, and built an AGV scheduling experiment platform based on ROS system for verification. This algorithm not only provides theoretical support for finding the optimal path, collision-free and anti-deadlock for multi-AGV scheduling in terminal transportation tasks, but also has great significance for further improving the overall operation efficiency of fully automated terminals, reducing the overall operation cost and promoting the construction and popularization of fully automated container terminals.