Planning Solutions
The top-level container that holds entities, problem facts, value ranges, and the score.
Domain Modeling
Define planning solutions, entities, and problem facts using Rust derive macros.
SolverForge uses derive macros to turn your Rust structs into a planning domain. There are three key concepts:
| Concept | Macro | Purpose |
|---|---|---|
| Planning Solution | #[planning_solution] | The top-level container — holds entities, facts, and the score |
| Planning Entity | #[planning_entity] | Something the solver changes (assigns variables to) |
| Problem Fact | #[problem_fact] | Immutable input data the solver reads but doesn’t modify |
Planning Solution
├── problem_fact_collection → Vec<ProblemFact> (inputs)
├── planning_entity_collection → Vec<Entity> (solver changes these)
├── value_range_provider → Vec<Value> (possible values)
└── planning_score → Option<ScoreType> (current quality)
How It Works
- Annotate your structs with the appropriate derive macros
- Mark fields with attribute macros to tell the solver their role
- The macros generate trait implementations that the solver uses at runtime
The derive macros generate implementations of PlanningSolution, PlanningEntity, and ProblemFact traits automatically — you never implement these traits by hand.
Sections
- Planning Solutions — The top-level container struct
- Planning Entities — Structs the solver modifies
- Problem Facts — Immutable input data
- List Variables — Ordered sequence variables for routing/sequencing
See Also
- docs.rs/solverforge — Full API reference
- Employee Scheduling (Rust) — Complete worked example
Planning Entities
Structs with planning variables that the solver assigns during optimization.
Problem Facts
Immutable input data that constraints reference but the solver doesn’t modify.
List Variables
Ordered sequence variables for routing, sequencing, and scheduling problems.
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