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.
Sun et al. (Thu,) studied this question.