A green supply chain, as one of the most critical low-carbon strategies, has been widely studied along with evolutionary game theory, for which governmental policies, such as a carbon tax, subsidies, or penalties, have been frequently applied; however, green sensitivity has been little explored. In this study, a general evolutionary game model composed of green-sensitive governments, enterprises, and consumers is built by considering the policy factors, and the evolutionary stable strategies of this model are solved. The solution results reveal that the green sensitivities significantly affect the evolutionary dynamics and stable strategies of the model. Green supply chains can evolve from low to advanced stages as the green sensitivities increase. At low development stages, governments must take the lead in enhancing their green sensitivity, resort to two key composite policy factor solutions, and encourage enterprises and consumers to increase their green sensitivities and drive them to adopt green strategies. As the green sensitivities of both enterprises and consumers have progressively increased to a certain high level, governments can adopt a nonsupervision strategy, reaching an advanced evolutionary stable strategy with the enterprises and consumers having complete autonomy. Therefore, green sensitivity has been proven to be a substitute for policy factors for green supply chain development. Moreover, the advanced strategy cannot be reached by any policy factor composition without green sensitivity. The findings above clarify the significance of green sensitivity, indicating that specific government policies based on the specific green sensitivities of the three parties can improve green supply chain management.

Smart manufacturing is the core of the modern production and manufacturing industry as it moves towards digitalization. The successful implementation of smart manufacturing needs the support of information technology, data technology, and operational technology, among which edge computing, blockchain technology and artificial intelligence can play significant roles facilitating the development of smart manufacturing. In this research, a smart manufacturing system is proposed considering the integration of edge computing and blockchain technology. The introduction of edge computing can balance the computational workload and provide a more timely response for terminal devices. Blockchain technology can be utilized to promote both the device-level data transmission and the manufacturing service transaction. Moreover, regarding the computational task assignment in smart manufacturing systems, a mathematical model is proposed, and further solved using a swarm intelligence-based approach. Numerical experiments show that the introduction of the edge computing mechanism in smart manufacturing can significantly improve the processing time, especially with a large number of tasks.

Air cargo transportation is an essential component in the freight transportation market, primarily due to the transportation requirements of time-sensitive products. Air cargo transportation plays an increasingly important role alongside economic development. Cargo flight network design and fleet routing selection significantly affect the performance of the air cargo transportation. In this research, we propose an integrated model simultaneously considering cargo flight network design and the fleet routing selection for the air cargo transportation. Two transportation modes, the direct transportation mode in point-to-point networks and the transshipment mode in hub-and-spoke networks, are compared. In order to solve the proposed optimization problem, a swarm-intelligence-based algorithm is adapted. Numerical experiments were conducted to examine and validate the effectiveness and efficiency of the proposed model and algorithm. The computational results suggest that the proper settings of hub and transshipment route selection in an air cargo transportation network can significantly reduce the transportation cost, which can provide practical managerial insights for the air cargo transportation industry.

The design of steel logistics parks acts as fundamental infrastructure supporting the operations of storage, allocation, and distribution of steel products in the steel logistics industry, which actually lags behind the development of other logistics industries, such as e-commerce logistics, due to its large lot bulk storage, low turnover rate, and costly transportation and operations. This research proposes a data-driven approach for a specific steel logistics park, aiming to improve its operational efficiency in terms of product layout and allocation in multiple yards. The entry and delivery order data are analyzed comprehensively so as to determine the products with high operational frequency and the corresponding relevancy among them. Experimental results show that, among the 69 steel specifications, 14 high-frequency products are identified, and the correlation among the 14 identified high-frequency products possesses evident distribution characteristics concerning their brands and specifications. The identified frequency and correlation among various products can not only facilitate the product layout and allocation in steel logistics parks, but also advance the vehicle scheduling efficiency for product pick-up and delivery. Moreover, the research methodology and framework can provide managerial insights for other industries with mass data processing requirements.

The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.