Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.