Concepts
Understand the fundamental concepts of planning optimization and constraint solving.
Tag: Python
Getting Started
Quickstart Guides — repository layout, prerequisites, and how to run examples locally.
Categories:
Employee Scheduling
A comprehensive quickstart guide to understanding and building intelligent employee scheduling with SolverForge
Categories:
API Summary
Quick reference for SolverForge Python API.
Solver Configuration
Configure the solver with SolverConfig and related classes.
Constraint Streams
Build constraints using the fluent Constraint Streams API.
Construction Heuristics
Build an initial solution quickly with construction heuristics.
FastAPI Integration
Build REST APIs for your solver with FastAPI.
Installation
Set up Python, JDK, and install SolverForge.
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.
Portfolio Optimization
A comprehensive quickstart guide to understanding and building intelligent stock portfolio optimization with SolverForge
Categories:
VM Placement
A comprehensive quickstart guide to understanding and building intelligent virtual machine placement optimization with SolverForge
Categories:
Meeting Scheduling
A comprehensive quickstart guide to understanding and building intelligent meeting scheduling with SolverForge
Categories:
Continuous Planning
Rolling horizon and replanning strategies.
FAQ
Frequently asked questions about SolverForge.
Getting Started
Install SolverForge and solve your first planning problem.
Hello World
Build a school timetabling solver from scratch.
Joiners
Efficiently filter and match entities in constraint streams.
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.
Running the Solver
Execute the solver synchronously with Solver.solve().
Serialization
JSON serialization with dataclasses and Pydantic.
Vehicle Routing
A comprehensive quickstart guide to understanding and building intelligent vehicle routing with SolverForge
Categories:
Collectors
Aggregate data in constraint streams using collectors.
Exhaustive Search
Find optimal solutions with exhaustive search (for small problems).
Hello World with FastAPI
Add a REST API to your school timetabling solver.
Logging
Configure logging for debugging and monitoring.
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.
SolverManager
Manage concurrent and asynchronous solving jobs.
Terminology
Glossary of terms used in SolverForge documentation.
Constraints
Define constraints using the fluent Constraint Streams API.
Move Selectors
Reference for move types available in local search.
Score Types
Choose the right score type for your constraints.
Shadow Variables
Define calculated variables that update automatically.
SolutionManager
Analyze and explain solutions with SolutionManager.
Benchmarking
Compare solver configurations and tune performance.
Pinning
Lock specific assignments to prevent the solver from changing them.
Score Analysis
Understand why a solution has its score.
Solver
Configure and run the solver to find optimal solutions.
Optimization Algorithms
Understand the algorithms that power SolverForge’s optimization.
Constraint Performance
Optimize constraint evaluation for faster solving.
Testing Constraints
Test constraints in isolation for correctness.
Design Patterns
Common patterns for handling real-world planning scenarios.
Integration
Integrate SolverForge with web frameworks and other systems.
SolverForge (Legacy)
Technical documentation for SolverForge Legacy — the pure Python constraint solver using the Timefold backend.
Reference
API reference and frequently asked questions.
Why Java Interop is Difficult in SolverForge Core
Reflections on the challenges of bridging Rust and Java in constraint solving, and the hard lessons learned along the way.
Order Picking Quickstart: JPype Bridge Overhead in Constraint Solving
Building an order picking quickstart with real-time 3D visualization deepened our understanding of JPype’s performance characteristics in constraint-heavy workloads.
Dataclasses vs Pydantic in Constraint Solvers
Architectural guidance for Python constraint solvers: when to use dataclasses vs Pydantic for optimal performance.