Astronomical observation Scheduling Problem: a comprehensive study and novel metaheuristic solutions
[eng] As the complexity of large-scale astronomical surveys increases, the need for intelligent and adaptive scheduling systems has become critical to maximizing scientific return. The Astronomical Observation Scheduling (AOS) problem represents a complex and highly constrained case of combinatorial...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/227624 |
| Acceso en línea: | https://hdl.handle.net/2445/227624 http://hdl.handle.net/10803/696837 |
| Access Level: | acceso abierto |
| Palabra clave: | Intel·ligència artificial Metaheurística Optimització combinatòria Observacions astronòmiques Artificial intelligence Metaheuristics Combinatorial optimization Astronomical observations |
| Sumario: | [eng] As the complexity of large-scale astronomical surveys increases, the need for intelligent and adaptive scheduling systems has become critical to maximizing scientific return. The Astronomical Observation Scheduling (AOS) problem represents a complex and highly constrained case of combinatorial optimization, characterized by strict computational time limits and frequent changes. Its unique structure and challenges motivate focused research to develop flexible and scalable scheduling solutions. This research is organized into two main phases, which together comprise its four core contributions. The first phase focuses on heuristic research with the aim of developing efficient heuristic strategies that effectively address the specific constraints and structure of the AOS problem. The first core contribution is the introduction of the Conflict Resolution Unit (CRU) heuristic algorithm and its variants, designed to fulfill the objectives of this phase. The second and principal phase focuses on metaheuristic research, aiming to design algorithms that are both flexible and scalable, and capable of addressing the diverse level of complexities and changes in AOS. To ensure that these metaheuristics are well-adapted to the problem, they incorporate the heuristics developed in the first phase as core components. The second contribution is the Accumulative Planner (AP) algorithm, which integrates the CRU heuristic with a greedy strategy to form a fast, primarily local optimization algorithm for AOS, with competent results. These results were used as a baseline for further improvements. The third core contribution is the Hybrid Accumulative Planner (HAP) algorithm, developed to overcome the limitations of AP. HAP uses a modified version of CRU and a multi-start strategy to enable a broader and more robust search process. The fourth and final core contribution is the Forgetful Swarm Optimization (FSO) algorithm. It combines another CRU variant with a Destroy-and-Repair strategy and a Swarm Intelligence framework to deliver a capable global optimization method. FSO is designed to balance the search in exploration and exploitation, achieving high-quality results within a reasonable computational time, while preserving the precision of domain-specific heuristics. The core contributions are adapted to a real-world example of AOS problems and evaluated using its available datasets. These datasets present a variety of test scenarios with diverse characteristics, highlighting the challenges that the algorithms must address. Besides the core contributions, the evaluation includes other adapted algorithms for this real-world problem to provide a better perspective on relative performance. These include an Evolutionary Algorithm, an Iterated Local Search, and a Hill-Climbing Greedy. The results show the effectiveness of the proposed heuristic, CRU, and its variants in handling different tasks and constraints of AOS. Furthermore, all metaheuristics that leverage CRU as a core component produce high-quality solutions. The mostly local optimization algorithm of AP competes with global approaches in terms of solution quality, even surpassing them in some cases, while operating at a fraction of their computational cost. On the other hand, FSO consistently outperforms all other algorithms across the evaluated datasets, with a significantly lower computational cost than other global optimization algorithms, such as the evaluated Evolutionary Algorithm. The HAP algorithm performs between the two other proposed metaheuristics in terms of both solution quality and computational cost. Additionally, this thesis presents an algorithm design framework that formalizes the development process leading to these solutions. This work advances the state of the art in AOS research. The novel proposals, in particular the FSO algorithm, aim to set a benchmark for future studies. |
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