Optimizing research in Antarctica: a novel approach to project selection and scheduling across multiple stations
针对南极多站点研究项目选择与调度问题，提出混合整数规划模型和三种元启发式算法，通过480个测试实例验证了迭代局部搜索在解质量和可扩展性上的优势，为研究管理者提供决策工具。
Antarctica’s unique research environment necessitates innovative project selection and scheduling strategies to maximize scientific output while addressing logistical, environmental, and resource constraints. This study introduces a novel framework for the Research Project Selection and Scheduling in Multiple Antarctic Stations Problem (RPSAP), formulated as a mixed-integer programming model. The model incorporates resource-sharing constraints, station-specific capacities, transportation delays, and sustainability considerations. Given the problem’s NP-hard complexity, three metaheuristic methods—Iterated Local Search (ILS), Variable Neighborhood Search (VNS), and Simulated Annealing (SA)—were developed to efficiently solve large-scale instances. Metaheuristics demonstrated robust performance through extensive computational experiments involving 480 test instances across 60 classes. The ILS consistently outperformed others in solution quality and scalability, while SA offered competitive results in execution time. Results reveal that the metaheuristics are superior to exact methods in handling large problem sizes, with optimality achieved only for small instances using the proposed exact model. This research bridges the gap between theoretical optimization models and practical applications, offering decision-making tools for research managers to enhance resource utilization, minimize ecological impacts, and prioritize high-impact projects.