Social life cycle assessment is an important method to assess products’ social impacts throughout their life cycles. There are already some indicators and software to assist conducting social life cycle assessment. However, it is hard for users to share or reuse assessment results because of different application data structures. To resolve this problem, a knowledge-based social life cycle assessment–aided design method is developed in this research. With this method, all elements in the social life cycle assessment process are analyzed and represented as classes, their relationships are described as object properties, and the data structure is represented as data properties to construct an ontology system for social life cycle assessment. Based on the ontology, a social life cycle assessment–aided product development web is developed. According to the data property structure, a bidirectional mapping between database and ontology is realized using JENA and ontology-based data access, which enables the result data to be automatically inputted into ontology individuals. Thus, the result data can be accumulated, shared, and reused among users. A case study with a floor product as well as a user test is carried out to prove the feasibility and usability of the web. The ontology-based social life cycle assessment–aided design method provides users with a new high-efficiency approach, setting the foundation for the intellectualization of life cycle assessment.

The main purpose of this paper is to identify the dynamic forces between the conical pick and the coal‐seam. According to the theory of time domain method, the dynamic force identification problem of the system is established. The direct problem is described by Green kernel function method. The dynamic force is expressed by a series of functions superposed by impulses, and the dynamic response of the structure is expressed as a convolution integral form between the input dynamic force and the response of Green kernel function. Because of the ill‐conditioned characteristics of the structure matrix and the influence of measurement noise in the process of dynamic force identification, it is difficult to deal with this problem by the usual numerical method. In present content, a novel improved Tikhonov regularization method is proposed to solve ill‐posed problems. An engineering example shows that the proposed method is effective and can obtain stable approximate solutions to meet the engineering requirements.

To improve the safety and effectiveness of autonomous towing aircraft aboard the carrier deck, this study proposes a velocity-restricted path planner algorithm named as kinodynamic safety optimal rapidly exploring random tree (KS-RRT) to plan a near time-optimal path. First, a speed map is introduced to assign different maximum allowable velocity for the sampling points in the workspace, and the traverse time is calculated along the kinodynamic connection of two sampling points. Then the near time-optimal path in the tree-structured search map can be obtained by the rewiring procedures, instead of a distance-optimal path in the original RRT algorithm. In order to enhance the planner’s performance, goal biasing scheme and fast collision checking technique are adopted in the algorithm. Since the sampling-based methods are sensitive to their parameters, simulation experiments are first conducted to determine the optimal input settings for the specific problem. The effectiveness of the proposed algorithm is validated in several common aircraft parking scenarios. Comparing with standard RRT and human heuristic driving, KS-RRT demonstrates a higher success rate, as well as shorter computation and trajectory time. In conclusion, KS-RRT algorithm is suitable to generate a near time-optimal safe path for autonomous high density parking in semistructured environment.

The ability of ensuring the safety and reliability of combat aircraft in noncombat processes is a must for aircraft sortie generation. How to guarantee the operation safety and reliability of combat aircraft while ensuring operation efficiency in the air is a matter requiring further exploration and research. For this reason, this paper carries out dynamic planning of aircraft sortie generation based on multiobjective optimization. First, this paper selected a few indicators for measuring the congestion of air field and constructed a state demarcation model; then, it constructed an operating state identification model for the sortie generation and established objective functions for the aircraft runway sequencing model based on the scheduling time span and takeoff and landing delay time loss of the aircraft. At last, this paper elaborated on the methods of landing aircraft parking space scheduling and runway optimization, and the effectiveness of the general model was verified by experimental results, which can be applied to both field-based and cargo-based aircraft sortie generation problems.

Aviation ordnance handling is critical to the firepower projection of the time-critical cyclic flight operation on aircraft carriers. The complexity of the problem depends on the supply and demand features of ordnance. This paper examines the scheduling of aviation ordnance handling of an operational aircraft carrier under the framework of hybrid flow shop scheduling (HFS) and derives a method based on the simulated annealing (SA) algorithm to get the HFS problems solution. The proposed method achieves the minimum possible flow time by optimizing the ordnance assignment through different stages. The traditional SA algorithm depends heavily on the heuristic scheme and consumes too much time to compute the optimal solution. To solve the problem, this paper improves the SA by embedding a task-based encoding method and a matrix perturbation method. The improved SA remains independent of the heuristic scheme and effectively propagates the local search process. Since the performance of SA is also influenced by its embedded parameters, orthogonal tests were carried out to carefully compare and select these parameters. Finally, different ordnance loading plans were simulated to reveal the advantage of the improved SA. The simulation results show that the improved SA (ISA) can generate better and faster solution than the traditional SA. This research provides a practical solution to stochastic HFS problems.

To ensure unmanned vehicles can perform a sortie mission quickly, efficiently, safely and reliably after receiving the command, it is necessary to calculate the sortie mission reliability of the shipborne unmanned vehicle group before loading. Aimed at the layout and sortie characteristics of an unmanned vehicle group, a sortie mission network model and a calculation method for sortie mission reliability are designed in this paper. Firstly, this paper uses space partition to parallel search for equal-length minimal paths based on the two-terminal network reliability. Secondly, this paper adopts the sum of disjoint products to process the equal-length minimal path set, innovatively proposing a calculation method for the sortie mission reliability of the shipborne unmanned vehicle group. Finally, the sortie mission reliability for three typical cases was calculated and compared with the Monte Carlo method. The comparative analysis indicates that the proposed method is both accurate and efficient, thereby corroborating its scientific validity and practical effectiveness. This study fills the gap in the field of sortie mission reliability and lays a theoretical foundation for subsequent research. Meanwhile, the method proposed in this paper can also be extended to the reliability calculation of a multiple-vehicle sortie mission in similar enclosed spaces.