Overview
What SolverForge is, how it differs from mathematical solvers, and the project roadmap.
Tag: Concepts
Concepts
Understand the fundamental concepts of planning optimization and constraint solving.
Construction Heuristics
Build an initial solution quickly with construction heuristics.
Planning Entities
Define planning entities that the solver will optimize.
Real-Time Planning
Handle changes while the solver is running.
What is Planning Optimization?
Introduction to planning optimization and constraint satisfaction.
Continuous Planning
Rolling horizon and replanning strategies.
Local Search
Improve solutions iteratively with local search algorithms.
Planning Variables
Define what the solver assigns: simple variables and list variables.
Problem Types
Common categories of planning and scheduling problems.
Exhaustive Search
Find optimal solutions with exhaustive search (for small problems).
Domain Modeling
Model your planning problem with entities, variables, and solutions.
Planning Solutions
Define the container for problem data and solution score.
Repeated Planning
Batch optimization and periodic replanning.
Terminology
Glossary of terms used in SolverForge documentation.
Move Selectors
Reference for move types available in local search.
Shadow Variables
Define calculated variables that update automatically.
Pinning
Lock specific assignments to prevent the solver from changing them.
Optimization Algorithms
Understand the algorithms that power SolverForge’s optimization.
Design Patterns
Common patterns for handling real-world planning scenarios.
Dataclasses vs Pydantic in Constraint Solvers
Architectural guidance for Python constraint solvers: when to use dataclasses vs Pydantic for optimal performance.