SolverForge

Native Rust constraint solver — production-ready at v0.6.0.

SolverForge is a native Rust constraint solver for planning and scheduling problems. It uses derive macros for domain modeling, constraint streams for declarative rule definition, and metaheuristic algorithms for optimization.

Installation

cargo add solverforge

Minimal Example

use solverforge::prelude::*;

// 1. Define your domain
#[problem_fact]
#[derive(Clone, Debug)]
pub struct Employee {
    #[planning_id]
    pub id: i64,
    pub name: String,
}

#[planning_entity]
#[derive(Clone, Debug)]
pub struct Shift {
    #[planning_id]
    pub id: i64,
    pub required_skill: String,
    #[planning_variable(allows_unassigned = true)]
    pub employee: Option<Employee>,
}

#[planning_solution]
#[derive(Clone, Debug)]
pub struct Schedule {
    #[problem_fact_collection]
    #[value_range_provider]
    pub employees: Vec<Employee>,
    #[planning_entity_collection]
    pub shifts: Vec<Shift>,
    #[planning_score]
    pub score: Option<HardSoftScore>,
}

// 2. Define constraints
fn define_constraints() -> impl ConstraintSet<Schedule, HardSoftScore> {
    let factory = ConstraintFactory::<Schedule, HardSoftScore>::new();

    let unassigned = factory
        .for_each(|s: &Schedule| s.shifts.as_slice())
        .filter(|s: &Shift| s.employee.is_none())
        .penalize(HardSoftScore::ONE_HARD)
        .named("Unassigned shift");

    (unassigned,)
}

// 3. Solve
fn main() {
    let problem = Schedule {
        employees: vec![/* ... */],
        shifts: vec![/* ... */],
        score: None,
    };

    let mut director = ScoreDirector::new(problem, define_constraints());
    let score = director.calculate_score();
    println!("Score: {:?}", score);
}