Aug 2026· Aerospace· Vol 13, pp. 757· 0 citations· 27 references
TL;DR
Results show that Staged-FT produces solutions close to traditional algorithms, RCRP achieves full hard constraint satisfaction in the emergency re-planning and logistics planning cases, and LGEV reduces the convergence generations of NSGA-III while improving Pareto-front quality.
Abstract
Space Station Operation Mission Planning (SSOMP) requires coordinated decisions across long-term activity allocation, mid-term logistics optimization, and short-term execution scheduling and is a key component of autonomous mission operations for high-precision space missions. Existing optimization methods have achieved substantial progress at individual planning levels, but their dependence on problem-specific models, limited support for semantic review of decision rationale, and computational cost restrict their adaptability to multi-level planning scenarios. This paper proposes a Large Language Model (LLM)-assisted framework for multi-level SSOMP. The framework combines Staged Fine-Tuning (Staged-FT), Reflective Constraint–Repair Prompting (RCRP), and LLM-Guided Evolutionary Variation (LGEV). Staged-FT uses a Cognitive-Load-Theory-informed curriculum with Low-Rank Adaptation to adapt general-purpose LLMs to SSOMP domain knowledge. RCRP couples a Deterministic Rule Engine with LLM-based semantic repair to improve hard constraint satisfaction. LGEV embeds the fine-tuned LLM into NSGA-III as a fitness-aware variation operator for multi-objective activity allocation. Three case studies are conducted on literature-derived benchmark scenarios of logistics optimization, emergency re-planning, and activity allocation with logistics design, corresponding to Flight Increment Planning, Short-Term Execution Planning, and Overall Operation Planning, respectively. Results show that Staged-FT produces solutions close to traditional algorithms, RCRP achieves full hard constraint satisfaction in the emergency re-planning and logistics planning cases, and LGEV reduces the convergence generations of NSGA-III while improving Pareto-front quality. The framework provides a constraint-aware approach with explicit reasoning traces that can support expert review of AI-assisted planning for autonomous space mission operations.
Post-disaster emergency communication recovery is not merely a link-repair task but a high-level planning problem constrained by service priorities, inter-object dependencies, resource budgets, and time windows. Existing restoration optimization methods generally rely on fully structured inputs, whereas direct large language model (LLM) planning may produce fluent candidates that violate encoded prerequisites, stage-order relations, budget limits, or temporal constraints. To address this challenge, we propose ICG-Restore, an intent-constrained, graph-enhanced LLM planning framework with rule-consistent minimal-edit repair. ICG-Restore transforms mixed restoration requests and structured network observations into task packages that can be checked for validator-level feasibility under an encoded high-level constraint model and evaluated by downstream abstract executors or schedulers. The framework compiles natural-language requests, structured observations, and operational rules into a task-intent object; retrieves task-relevant context from a heterogeneous scenario graph and a restoration knowledge graph; generates stage-wise restoration candidates; and applies bounded local corrections to candidates that violate encoded constraints. In this paper, “minimal-edit” is a descriptive label for a bounded local repair principle that prioritizes less disruptive corrections. Candidates accepted by the validators are evaluated and ranked by a safety-aware agent executor operating in an abstract restoration action space. Experiments on controlled abstract topologies covering three scales, four restoration tasks, and five environmental evolution modes show that ICG-Restore improves validator-level constraint satisfaction and benchmark-estimated recovery utility. Compared with Direct-LLM, it improves CSR and CRS by 1.99% and 24.56%, respectively; benchmark-specific WCTC@5 structural-alignment diagnostic increases by 38.87%.
Jinyin Bai, Wei Zhu, Xiangchen Wang et al.· Applied Informatics· 0 citations
A multiagent large language model (LLM)–based system for early-stage building layout planning, which enables flexible design requirement inputs and robust spatial reasoning and demonstrated significant improvements in both geometric quality and semantic alignment over a baseline LLM-only system.
Haolan Zhang, Rui-Chuan Zhang· Journal of computing in civi...· 0 citations
Constructing simulation scenarios manually is time-consuming and often depends on platform-specific modeling experience. Existing large-language-model (LLM) methods are promising for interpreting operational documents, but they still struggle with long-document parsing, incomplete platform interfaces, auditable task execution, and cross-lingual equipment-name normalization. This paper proposes a dual-channel LLM-agent framework for intelligent generation of simulation scenarios. The method standardizes Word-based scenario inputs, extracts scenario elements into a schema-constrained JSON intermediate representation, and decomposes the generation process into planning and execution. A DLL/Lua dual-channel Plan Agent assigns basic object and scenario-property operations to a C# object model, while allocating fine-grained unit, mission, and environment operations to Lua-based scripting interfaces. An Exec Agent further integrates two-stage hot-pluggable tool loading, side-effect-aware read/write-separated scheduling, and a Smart-Matcher module that combines BM25 retrieval, multilingual vector retrieval, reciprocal rank fusion, and low-confidence LLM reranking. Experiments on representative red-blue simulation scenarios show an average end-to-end generation time of 168 s, a first-round planning success rate of 92.0%, a post-Replan success rate of 100.0%, and Top-1/Top-5 equipment matching performance of 95.6% and 98.4%, respectively.
Lei Wang, Zhiqiang Fan, Yikang Song et al.· 2026 IEEE 27th China Confere...· 0 citations
This work transfers knowledge in the reverse direction, using knowledge extracted from high-quality GP rules to guide an online LLM decision maker, and injects it through Feature Selection, Feature Hint, Rule Reference, and Rule Follow.
ProgRouter is presented, an online progress-guided routing framework that adaptively selects LLM agents across workflow steps to preserve task-solving quality while adhering to time and cost budgets and reduces the operating cost relative to key baselines while maintaining strong task-solving performance.
Songyuan Li, Ahmed M. Abdelmoniem, Shi-Qiang Wang· 0 citations
Coordinating a team of robots in aircraft skin fabrication requires allocating and sequencing tightly coupled subtasks under spatio-temporal constraints, while the fleet must react to runtime disturbances such as robot failures and urgent task arrivals. Mixed-Integer Linear Programming (MILP) yields provably optimal coordination, but a disturbance often introduces new constraint logic rather than a mere parameter change, leaving the existing formulation structurally inadequate and requiring expert-led reformulation to accommodate the new logic. This letter proposes a Large Language Model (LLM)-based framework that automates the path from natural-language scheduling requirements to MILP formulation, executable solver code, and event-driven rescheduling, so that the optimization model can be restructured online without expert intervention. Two compact LLMs are specialized for complementary roles: a modeling LLM, empowered via knowledge augmentation, supervised fine-tuning on industrial constraint descriptions, and direct preference optimization on self-generated negatives; and a code LLM, trained via sandbox-validated knowledge distillation. On ten industrial constraint classes, the 8B modeling LLM reaches 100% constraint-level accuracy, exceeding cloud-scale general-purpose LLMs equipped with retrieval over the same knowledge base, while the 8B code LLM attains 86% under fully on-premises deployment. When a disturbance occurs, an event-triggered mechanism regenerates only the affected constraints. A case study on multi-robot scheduling for aircraft skin fabrication shows that the framework matches the schedule quality of baselines while shifting adaptation cost from offline expert labour to a bounded online inference budget.
Zhen-Dong Chen, Mingming Peng, Hao Zhang et al.· IEEE Robotics and Automation...· 0 citations
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