Jianhua Sun

The timeliness of maritime rescue critically depends on the efficient generation of solutions and the execution of missions. Therefore, this study aims to implement maritime rescue task allocation and sequencing (MRTAS) while ensuring solution generation and mission execution efficiencies. First, a mathematical model minimizing mission completion time and resource consumption for MRTAS is established. Second, adaptive operators considering iteration progress and population objective distribution status and an idle-time-aware decoding strategy based on an in-degree embedded gap insertion are proposed. The adaptive operators and idle-time-aware decoding strategy are employed to enhance the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for efficiency improvement in both solution generation and mission execution. The enhanced MOEA/D is then employed to identify Pareto-optimal MRTAS schemes. Validation using two case studies (Case 1–18 task, Case 2–100 task) confirms the practicality and feasibility of the enhanced MOEA/D. Furthermore, ablation studies, sensitivity analyses, and comprehensive comparisons against fixed operators, state-of-the-art algorithms, and traditional decoding strategies all demonstrate that the enhanced MOEA/D can accelerate convergence while maintaining converged solution quality and reduce mission completion time.

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user’s experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.

Purpose: In situations where the crew is reduced, the optimization of crew task allocation and sequencing (CTAS) can significantly enhance the operational efficiency of the man-machine system by rationally distributing workload and minimizing task completion time. Existing related studies exhibit a limited consideration of workload distribution and involve the violation of precedence constraints in the solution process. This study proposes a CTAS method to address these issues. Design/methodology/approach: The method defines visual, auditory, cognitive and psychomotor (VACP) load balancing objectives and integrates them with workload balancing and minimum task completion time to ensure equitable workload distribution and task execution efficiency, and then a multi-objective optimization model for CTAS is constructed. Subsequently, it designs a population initialization strategy and a repair mechanism to maintain sequence feasibility, and utilizes them to improve the non-dominated sorting genetic algorithm III (NSGA-III) for solving the CTAS model. Findings: The CTAS method is validated through a numerical example involving a mission with a specific type of armored vehicle. The results demonstrate that the method achieves equitable workload distribution by integrating VACP load balancing and workload balancing. Moreover, the improved NSGA-III maintains sequence feasibility and thus reduces computation time. Originality/value: The study can achieve equitable workload distribution and enhance the search efficiency of the optimal CTAS scheme. It provides a novel perspective for task planners in objective determination and solution methodologies for CTAS.