SolverForge (Legacy)

Every organization faces planning problems: providing products or services with a limited set of constrained resources (employees, assets, time, and money). SolverForge’s Planning AI optimizes these problems to do more business with fewer resources using Constraint Satisfaction Programming.

SolverForge is a lightweight, embeddable constraint satisfaction engine which optimizes planning problems. Example use cases include:

• Vehicle Routing - Optimize delivery routes for fleets of vehicles
• Employee Scheduling - Assign shifts to employees based on skills and availability
• School Timetabling - Schedule lessons to timeslots and rooms
• Meeting Scheduling - Find optimal times and rooms for meetings
• Bin Packing - Efficiently pack items into containers
• Task Assignment - Assign tasks to resources optimally

Python-First Design

SolverForge provides a Pythonic API using:

• Decorators for domain modeling (@planning_entity, @planning_solution)
• Type annotations with Annotated for constraint and property marking
• Dataclasses for clean, readable domain models
• Fluent constraint stream API for intuitive constraint definition

from dataclasses import dataclass, field
from typing import Annotated
from solverforge_legacy.solver.domain import (
    planning_entity, planning_solution,
    PlanningId, PlanningVariable, PlanningEntityCollectionProperty,
    ProblemFactCollectionProperty, ValueRangeProvider, PlanningScore
)
from solverforge_legacy.solver.score import HardSoftScore

@planning_entity
@dataclass
class Lesson:
    id: Annotated[str, PlanningId]
    subject: str
    teacher: str
    timeslot: Annotated[Timeslot | None, PlanningVariable] = field(default=None)
    room: Annotated[Room | None, PlanningVariable] = field(default=None)

@planning_solution
@dataclass
class Timetable:
    timeslots: Annotated[list[Timeslot], ProblemFactCollectionProperty, ValueRangeProvider]
    rooms: Annotated[list[Room], ProblemFactCollectionProperty, ValueRangeProvider]
    lessons: Annotated[list[Lesson], PlanningEntityCollectionProperty]
    score: Annotated[HardSoftScore, PlanningScore] = field(default=None)

Requirements

• Python 3.10+ (3.11 or 3.12 recommended)
• JDK 17+ (for the optimization engine backend)

Next Steps

• Follow the Getting Started guides to solve your first planning problem
• Learn about Planning AI concepts