The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

Data sharing helps to make full use of each other’s data and enable the digital economy. With the gradual rise of corporate data sharing, the frequent occurrence of data leakage events highlights the dilemma of data sharing, leakage and supervision, which infringes on the data assets of the leaked party. Our paper aims to design an effective data supervision mechanism and achieve the stability of data sharing alliance. Therefore, this paper considers the data technology capabilities of both parties of the data sharing alliance and the benefits and loss of data leakage, establishes the game model and supervision mechanism of data sharing, leakage and supervision under the condition of complete information, and analyzes the game equilibrium and the influence of different supervision levels on the company’s data sharing and leakage behavior. The results show that the company sharing and leaking behavior is affected by both the level of data supervision fines and the relative technical level. Our model can make up for the weakness of the low-tech company, control the company’s choice of leaking behaviors, and ensure the stability of data sharing alliance by designing reasonable data supervision mechanism, especially the severe data supervision strategy.

In recent years, the collection, mining, and utilization of data have become a new profit growth point for enterprises, and these events have also accelerated the pace of enterprises to collect users’ data. However, the relevance of personal data privacy and the frequent occurrence of data leakage events have increased users’ privacy awareness. The purpose of our study is to enhance the effective flow of data while protecting users’ data privacy. The data supply chain consists of the end user, data provider, and service provider, and involves the flow of the value-added process of data. Our study focuses on the pricing strategy of data products considering data incentive and data protection levels. We propose three models—centralized pricing, decentralized pricing, and revenue-sharing pricing—and solve them, and then we analyze the impact of users’ privacy awareness on data incentives, protection, and pricing of data products in the three models. We also analyze which pricing method works best for participants.

Based on the emergent colonization and food-chain phenomena, behavioral ecology, a new kind of improved artificial-life algorithm is proposed. It is referred as food-chain algorithm that achieves optimization computation by mimicking the evolution of natural ecosystem and the information processing mechanism of natural organisms. Artificial food-chain system is firstly built in artificial world, and food-chain algorithm is designed in detail. Then the key parameters of the algorithm are secondly analyzed in detail. According to the simulation results, food-chain algorithm has outstanding performance in function optimization. Furthermore, food-chain algorithm has lifelike traits, for example, autonomy, evolution, adaptive, and robustness, and it adapts to solve the optimization problems of lifelike systems, such as, supply chain planning, distribution network designing, and etc. However, it is felt that more research must be done in order to reach a better understanding of how to design the parameters and rules of food chain algorithm