To perform “global” optimization of the machining process in discrete manufacturing, a bi-objective flexible job-shop scheduling problem with tool allocation is proposed. Unlike traditional scheduling problems that treat resources independently, this paper addresses the strong coupling between machine routing, operation sequencing, and finite tool capacity. A mixed-integer programming model is constructed with the objectives of minimizing the tool wear cost and weighted sum of tardiness. Sophisticated constraints that fit actual manufacturing scenarios are considered, specifically the combination of tool magazine capacity, variant job releasing times, and machine/tool compatibility for operations. To address the computational challenge and the discrete nature of the solution space, a knowledge-driven teaching-learning-based optimization algorithm is designed. Specific strategies, including a topology-preserving discrete crossover and a critical-path-based neighborhood search, are developed to prevent premature convergence caused by complex constraints. Simulation experimental results show that the proposed algorithm significantly outperforms the traditional meta-heuristic algorithms in the aspects of quality, spread, and comprehensive metric, and the proposed multi-objective collaborative optimization method obtains better processing decisions than the traditional sequential scheduling methods.

To enhance the efficiency of locating dynamic missing persons in complex mountain terrain, this study introduces an innovative Slope Probability Search (SPS) algorithm based on a modified A* framework. The algorithm’s core is a dynamic global probability map, constructed by linking terrain slope to the behavioral tendencies of missing persons. This fundamentally shifts the unmanned aerial vehicle (UAV) search paradigm from conventional coverage patterns to intelligent, guided exploration. To ensure a realistic evaluation, we designed three representative dynamic models for the missing persons: Terrain Constrained, Path Following, and Random Walk. The SPS algorithm, through its unique heuristic function, achieves an optimal balance between exploiting high probability areas and exploring new regions to maximize search efficiency. Simulation experiments using real-world geographic data demonstrated that even under severe constraints of limited search duration and sensor range, the algorithm achieved a success rate of 88.9% achieving an average search time substantially lower than that of conventional methods. This research provides a solid theoretical basis and a practical algorithmic framework for developing next generation intelligent search and rescue systems.