To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is proposed. When a new job is inserted, the new job and the operations that have not started processing will be rescheduled. Finally, through solving the Brandimarte instances, it is proved that the convergence accuracy of the SLABC algorithm is higher than that of other optimization algorithms, and the effectiveness of the DSLABC algorithm is demonstrated by solving a specific example with a new job inserted.

The preform is covered by a flexible vacuum bag in vacuum-assisted resin infusion process, and the preform compaction state is time and space dependent during the mould-filling stage, there is no practical tool for the filling simulation. In this paper, the resin flow governing equations for vacuum-assisted resin infusion are built based on the analysis of the preform compaction behaviour and the fibre tow impregnation. For obtaining the numerical solution conveniently and accurately, an alternative approach is proposed in which the primary flow is computed from the normal Darcy’s law, two-slave elements are added to each master element, named sink slave element and deformation slave element, to calculate the resin mass variation caused by fibre tow infiltration and preform deformation. Three examples are conducted to verify the precision and broad applicability of the approach. The influence of the preform deformation and the unsaturated effect on the mould filling is analysed.

Agricultural machinery rental is a new service form that uses big data in agriculture to improve the utilization rate of agricultural machinery and promote the development of the agricultural economy. To realize agricultural machinery scheduling optimization in cloud services, a dynamic artificial bee-ant colony algorithm (DABAA) is proposed to solve the above problem. First, to improve the practicability of the mathematical model in agricultural production, a dynamic coefficient is proposed. Then the mutation operation is combined with the artificial bee colony (ABC) algorithm to improve the algorithm. Then, iterative threshold adjustment and optimal fusion point evaluation are used to combine the ABC algorithm with the ant colony optimization (ACO) algorithm, which not only improves the search precision but also improves the running speed. Finally, two groups of comparison experiments are carried out, and the results show that the DABAA can obviously improve the running speed and accuracy of cloud services in agricultural machinery rental.

As the complexity of mission planning increases, relying on the subjective experience of planners is no longer sufficient to meet the needs of modern mission planning. Knowledge mapping, as a structured knowledge management technique, provides an effective solution for systematically integrating knowledge in the task-planning domain. The mission-planning business model is able to systematically capture and portray domain knowledge in mission planning through a formal representation of mission planning processes, rules, and constraints. Thus, it becomes an important source of knowledge for mission-planning knowledge mapping. This paper proposes a business-model-driven knowledge graph construction method for mission planning. First, under the support of conceptual business knowledge, the multidimensional task-planning ontology network expression method is utilized to construct the task-planning ontology network, and then the data-based business knowledge is structured to transform it into business data mapping to complete the acquisition of business knowledge. Then, the task-planning ontology network is constructed using the multidimensional task-planning ontology network representation method under the support of conceptual knowledge. Subsequently, a domain knowledge categorization algorithm based on Ullman subgraph matching is used to realize the matching mapping between the ontology network and business data mapping to complete the categorization of task-planning domain knowledge. Finally, the generated task-planning domain knowledge graph is stored in the Neo4j graph database. In order to ensure the completeness of the knowledge graph, an adaptive adjustment method based on its actual effectiveness is conceived, which is able to detect and adjust the completeness of the knowledge graph. The effectiveness of the proposed methodology is validated by constructing a space-station mission-planning knowledge graph driven by a space-station mission-planning business model.