Cusched: An Intelligent Timetable Scheduling System Framework Based on Large Language Models

Cusched is an innovative university timetable scheduling framework that skillfully integrates the reasoning capabilities of large language models (LLMs) with constraint satisfaction problem (CSP) solving techniques, offering an intelligent solution for complex university scheduling scenarios. Its core innovation lies in using LLMs to parse constraint conditions described in natural language, lowering the threshold for using scheduling systems. This allows academic administrators to flexibly define and adjust rules without programming or modeling knowledge, transforming scheduling from a manual task to a strategic decision-making process.

University timetable scheduling is a highly challenging optimization task that requires satisfying multiple constraints: hard constraints (e.g., a teacher/class cannot have classes at the same time, classroom capacity matching, special equipment requirements) and soft constraints (e.g., teacher time preferences, logical course sequence, student transition time). As universities expand, manual scheduling becomes increasingly difficult, and traditional algorithms (such as genetic algorithms and simulated annealing) have limitations in handling complex constraints and flexible adjustments.

Cusched proposes a "constraint-aware" intelligent scheduling framework that combines LLM reasoning capabilities with CSP solving. Traditional scheduling systems require hard-coded constraint rules, but Cusched allows users to describe requirements in natural language, which LLMs parse and convert into formal constraint expressions. This significantly lowers the usage threshold, enabling academic administrators to flexibly define and adjust scheduling rules.

Natural Language Constraint Parsing

LLMs convert natural language into machine-understandable constraints (e.g., "Professor Zhang does not have classes on Wednesday afternoons" is transformed into constraints on teacher, time, and action).

Constraint Hierarchy Management

Distinguish priorities: hard constraints (must be satisfied), soft constraints (try to satisfy), and advisory constraints (for reference). LLMs evaluate constraint conflicts and provide trade-off suggestions.

Iterative Optimization Process

1. Initial solving to generate a preliminary timetable;
2. Conflict detection to identify issues;
3. Feedback issues to LLMs to generate adjustment suggestions;
4. Re-solve to optimize the result.

Lowering Technical Thresholds

The natural language interface allows academic administrators to interact directly, quickly responding to emergencies (e.g., teacher leave, classroom maintenance) without requiring a professional team to modify code.

Enhancing Flexibility

Quickly incorporate changes (e.g., new courses, teacher transfers), explain reasons for infeasible arrangements, and provide alternative solutions.

Supporting Complex Scenarios

Handle complex scenarios such as cross-campus teaching, bilingual courses, and experimental grouping. LLMs' semantic understanding capabilities address rich contextual information.

Cusched demonstrates the application potential of LLMs in the field of operations research and optimization, serving as an "intelligent front-end" to enhance the usability of traditional algorithms. This "LLM + traditional algorithm" hybrid architecture can be applied to fields such as medical resource scheduling, logistics route planning, and project management. In the future, multimodal large models may support extracting constraints from PDFs/Excel files, further simplifying operations.

Cusched represents a new paradigm: using LLM semantic understanding to lower the threshold for complex optimization problems, transforming "constraint awareness" into a user-friendly experience, and providing a reference for educational informatization. For academic administrators, scheduling shifts from a manual task to strategic decision-making, allowing human resources to be invested in improving educational quality.