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‎.github/workflows/docker-publish.yml‎

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name: CI/CD Pipeline
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on:
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push:
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branches:
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- main
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- dev
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- 'release/**'
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paths:
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- 'rust/employee-scheduling/**'
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- 'legacy/employee-scheduling-fast/**'
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- 'legacy/maintenance-scheduling-fast/**'
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- 'legacy/meeting-scheduling-fast/**'
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- 'legacy/order-picking-fast/**'
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- 'legacy/vehicle-routing-fast/**'
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- 'legacy/vm-placement-fast/**'
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- '.github/workflows/docker-publish.yml'
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workflow_dispatch:
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inputs:
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apps:
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description: 'Apps to build (comma-separated, or "all")'
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required: false
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default: 'all'
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env:
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REGISTRY: ghcr.io
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jobs:
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# Rust tests
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test-rust:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up Rust
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uses: dtolnay/rust-toolchain@stable
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- name: Cache Rust dependencies
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uses: Swatinem/rust-cache@v2
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with:
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workspaces: rust/employee-scheduling
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- name: Run tests
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working-directory: rust/employee-scheduling
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run: cargo test --verbose
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- name: Build
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working-directory: rust/employee-scheduling
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run: cargo build --release --verbose
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# Python legacy tests
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test-python:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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app:
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- name: employee-scheduling-fast
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path: legacy/employee-scheduling-fast
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- name: maintenance-scheduling-fast
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path: legacy/maintenance-scheduling-fast
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- name: meeting-scheduling-fast
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path: legacy/meeting-scheduling-fast
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- name: order-picking-fast
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path: legacy/order-picking-fast
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- name: vehicle-routing-fast
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path: legacy/vehicle-routing-fast
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- name: vm-placement-fast
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path: legacy/vm-placement-fast
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.12'
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- name: Set up JDK 21
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uses: actions/setup-java@v4
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with:
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java-version: '21'
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distribution: 'temurin'
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- name: Install dependencies
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working-directory: ${{ matrix.app.path }}
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run: |
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python -m pip install --upgrade pip
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pip install -e .
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pip install pytest
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- name: Run tests
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working-directory: ${{ matrix.app.path }}
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run: pytest -v
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# Build and push on main and dev
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build-and-push:
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needs: [test-rust, test-python]
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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strategy:
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fail-fast: false
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matrix:
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app:
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- name: employee-scheduling
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context: rust/employee-scheduling
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- name: employee-scheduling-fast
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context: legacy/employee-scheduling-fast
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- name: maintenance-scheduling-fast
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context: legacy/maintenance-scheduling-fast
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- name: meeting-scheduling-fast
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context: legacy/meeting-scheduling-fast
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- name: order-picking-fast
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context: legacy/order-picking-fast
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- name: vehicle-routing-fast
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context: legacy/vehicle-routing-fast
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- name: vm-placement-fast
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context: legacy/vm-placement-fast
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Check if app should be built
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id: check
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run: |
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if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
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apps="${{ github.event.inputs.apps }}"
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if [[ "$apps" == "all" || "$apps" == *"${{ matrix.app.name }}"* ]]; then
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echo "build=true" >> $GITHUB_OUTPUT
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else
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echo "build=false" >> $GITHUB_OUTPUT
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fi
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else
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echo "build=true" >> $GITHUB_OUTPUT
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fi
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- name: Set up Docker Buildx
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if: steps.check.outputs.build == 'true'
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uses: docker/setup-buildx-action@v3
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- name: Log in to Container Registry
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if: steps.check.outputs.build == 'true'
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata (tags, labels)
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if: steps.check.outputs.build == 'true'
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ github.repository_owner }}/${{ matrix.app.name }}
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tags: |
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type=ref,event=branch
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type=sha,prefix=
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type=raw,value=latest
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- name: Build and push Docker image
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if: steps.check.outputs.build == 'true'
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uses: docker/build-push-action@v5
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with:
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context: ${{ matrix.app.context }}
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha
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cache-to: type=gha,mode=max

‎README.md‎

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# SolverForge Quickstarts
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This repository contains quickstarts for [SolverForge](https://github.com/SolverForge/solverforge-legacy), an AI constraint solver and framework for Rust and Python.
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It shows different use cases and basic implementations of constraint solving. The legacy (Timefold-based) quicktarts have been moved to [legacy](legacy/).
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This repository contains quickstarts for [SolverForge](https://github.com/SolverForge/solverforge), an AI constraint solver and framework for Rust and Python.
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It shows different use cases and basic implementations of constraint solving.
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## Overview
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| Use Case <img width="341" height="1"> | Notable Solver Concepts <img width="541" height="1"> |
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|-----------------------------------------------------------------------|----------------------------------------------------------|
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| 🚚 <a href="#-vehicle-routing">Vehicle Routing</a> | Chained Through Time, Shadow Variables |
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| 🧑‍💼 <a href="#-employee-scheduling">Employee Scheduling</a> | Load Balancing |
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| 🛠️ <a href="#-maintenance-scheduling">Maintenance Scheduling</a> | TimeGrain, Shadow Variable, Variable Listener |
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| 🛒 <a href="#-order-picking">Order Picking</a> | Chained Planning Variable, Shadow Variables |
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| 👥 <a href="#-meeting-scheduling">Meeting Scheduling</a> | TimeGrain |
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| Use Case <img width="341" height="1"> | Rust | Python (Legacy) | Notable Solver Concepts <img width="541" height="1"> |
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|-----------------------------------------------------------------------|------|-----------------|-----------------------------------------------------|
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| 👋 <a href="#-hello-world">Hello World</a> | 🚧 | ✅ | Basic Planning Problem |
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| 🧑‍💼 <a href="#-employee-scheduling">Employee Scheduling</a> | ✅ | ✅ | Load Balancing |
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| 🚚 <a href="#-vehicle-routing">Vehicle Routing</a> | 🚧 | ✅ | Chained Through Time, Shadow Variables |
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| 🛠️ <a href="#-maintenance-scheduling">Maintenance Scheduling</a> | 🚧 | ✅ | TimeGrain, Shadow Variable, Variable Listener |
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| 🛒 <a href="#-order-picking">Order Picking</a> | 🚧 | ✅ | Chained Planning Variable, Shadow Variables |
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| 👥 <a href="#-meeting-scheduling">Meeting Scheduling</a> | 🚧 | ✅ | TimeGrain |
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| 📈 <a href="#-portfolio-optimization">Portfolio Optimization</a> | 🚧 | ✅ | Financial Constraints |
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| 🖥️ <a href="#-vm-placement">VM Placement</a> | 🚧 | ✅ | Bin Packing, Resource Allocation |
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> [!NOTE]
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> The implementations in this repository serve as a starting point and/or inspiration when creating your own application.
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> SolverForge is a library and does not include a UI. To illustrate these use cases a rudimentary UI is included in these quickstarts.
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>
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> **Rust implementations** are native SolverForge applications showcasing zero-erasure architecture.
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> **Python (Legacy)** implementations use the Timefold-based legacy solver and are located in the [legacy/](legacy/) directory.
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## Use cases
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### 🚚 Vehicle Routing
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Find the most efficient routes for vehicles to reach visits, considering vehicle capacity and time windows when visits are available. Sometimes also called "CVRPTW".
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### 👋 Hello World
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![Vehicle Routing Screenshot](legacy/vehicle-routing/vehicle-routing-screenshot.png)
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A minimal example demonstrating the basics of constraint solving with SolverForge.
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- [Run vehicle-routing](legacy/vehicle-routing/README.MD) (Python, FastAPI)
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- [Run vehicle-routing (fast)](fast/vehicle-routing-fast/README.MD) (Python, FastAPI)
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> [!TIP]
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> <img src="https://docs.timefold.ai/_/img/models/field-service-routing.svg" align="right" width="50px" /> [Check out our off-the-shelf model for Field Service Routing](https://app.timefold.ai/models/field-service-routing). This model goes beyond basic Vehicle Routing and supports additional constraints such as priorities, skills, fairness and more.
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- **Python (Legacy)**: [legacy/hello-world-fast](legacy/hello-world-fast/README.md)
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---
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### 🧑‍💼 Employee Scheduling
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Schedule shifts to employees, accounting for employee availability and shift skill requirements.
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![Employee Scheduling Screenshot](java/employee-scheduling/employee-scheduling-screenshot.png)
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- **Rust**: [rust/employee-scheduling](rust/employee-scheduling/README.md)
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- **Python (Legacy)**: [legacy/employee-scheduling-fast](legacy/employee-scheduling-fast/README.md)
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---
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- [Run employee-scheduling](legacy/employee-scheduling/README.MD) (Python, FastAPI)
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- [Run employee-scheduling (fast)](fast/employee-scheduling-fast/README.MD) (Python, FastAPI)
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### 🚚 Vehicle Routing
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> [!TIP]
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> <img src="https://docs.timefold.ai/_/img/models/employee-shift-scheduling.svg" align="right" width="50px" /> [Check out our off-the-shelf model for Employee Shift Scheduling](https://app.timefold.ai/models/employee-scheduling). This model supports many additional constraints such as skills, pairing employees, fairness and more.
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Find the most efficient routes for vehicles to reach visits, considering vehicle capacity and time windows when visits are available. Sometimes also called "CVRPTW".
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- **Python (Legacy)**: [legacy/vehicle-routing-fast](legacy/vehicle-routing-fast/README.md)
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---
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### 🛠️ Maintenance Scheduling
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Schedule maintenance jobs to crews over time to reduce both premature and overdue maintenance.
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![Maintenance Scheduling Screenshot](legacy/maintenance-scheduling/maintenance-scheduling-screenshot.png)
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- [Run maintenance-scheduling](legacy/maintenance-scheduling/README.adoc) (Python, FastAPI)
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- **Python (Legacy)**: [legacy/maintenance-scheduling-fast](legacy/maintenance-scheduling-fast/README.md)
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---
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### 🛒 Order Picking
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Generate an optimal picking plan for completing a set of orders.
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![Order Picking Screenshot](legacy/order-picking/order-picking-screenshot.png)
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- [Run order-picking](legacy/order-picking/README.adoc) (Python, FastAPI)
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- **Python (Legacy)**: [legacy/order-picking-fast](legacy/order-picking-fast/README.md)
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### 👥 Meeting Scheduling
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Assign timeslots and rooms for meetings to produce a better schedule.
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![Meeting Scheduling Screenshot](legacy/meeting-scheduling/meeting-scheduling-screenshot.png)
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- **Python (Legacy)**: [legacy/meeting-scheduling-fast](legacy/meeting-scheduling-fast/README.md)
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---
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### 📈 Portfolio Optimization
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Optimize investment portfolios to balance risk and return while satisfying various financial constraints.
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- **Python (Legacy)**: [legacy/portfolio-optimization-fast](legacy/portfolio-optimization-fast/README.md)
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---
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### 🖥️ VM Placement
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Optimize the placement of virtual machines across physical servers to maximize resource utilization and minimize costs.
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- [Run meeting-scheduling](legacy/meeting-scheduling/README.adoc) (Python, FastAPI)
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- [Run meeting-scheduling (fast)](fast/meeting-scheduling-fast/README.adoc) (Python, FastAPI)
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- **Python (Legacy)**: [legacy/vm-placement-fast](legacy/vm-placement-fast/README.md)
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---
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‎legacy/deploy/argocd/applicationset.yaml‎

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apiVersion: argoproj.io/v1alpha1
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kind: ApplicationSet
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metadata:
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name: solverforge-legacy-apps
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namespace: argocd
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spec:
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generators:
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- list:
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elements:
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- appName: employee-scheduling-fast
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chartPath: legacy/employee-scheduling-fast/helm/employee-scheduling-fast
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- appName: maintenance-scheduling-fast
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chartPath: legacy/maintenance-scheduling-fast/helm/maintenance-scheduling-fast
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- appName: meeting-scheduling-fast
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chartPath: legacy/meeting-scheduling-fast/helm/meeting-scheduling-fast
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- appName: order-picking-fast
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chartPath: legacy/order-picking-fast/helm/order-picking-fast
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- appName: vehicle-routing-fast
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chartPath: legacy/vehicle-routing-fast/helm/vehicle-routing-fast
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- appName: vm-placement-fast
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chartPath: legacy/vm-placement-fast/helm/vm-placement-fast
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template:
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metadata:
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name: '{{appName}}'
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namespace: argocd
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labels:
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app.kubernetes.io/part-of: solverforge-legacy
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finalizers:
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- resources-finalizer.argocd.argoproj.io
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spec:
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project: default
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source:
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repoURL: https://github.com/SolverForge/solverforge-quickstarts.git
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targetRevision: HEAD
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path: '{{chartPath}}'
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helm:
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releaseName: '{{appName}}'
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valueFiles:
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- values.yaml
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destination:
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server: https://kubernetes.default.svc
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namespace: solverforge-benchmark
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syncPolicy:
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automated:
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prune: true
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selfHeal: true
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syncOptions:
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- CreateNamespace=true
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- PruneLast=true
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retry:
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limit: 5
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backoff:
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duration: 5s
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factor: 2
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maxDuration: 3m

‎legacy/employee-scheduling-fast/helm/employee-scheduling-fast/.helmignore‎

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.DS_Store
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.git/
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.gitignore
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.bzr/
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.bzrignore
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.hg/
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.hgignore
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.svn/
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*.swp
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*.bak
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*.tmp
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*.orig
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*~
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.project
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.idea/
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*.tmproj
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.vscode/

‎legacy/employee-scheduling-fast/helm/employee-scheduling-fast/Chart.yaml‎

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apiVersion: v2
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name: employee-scheduling-fast
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description: Employee Scheduling optimization using Timefold Solver (Python/FastAPI)
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type: application
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version: 0.1.0
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appVersion: "1.0.0"
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keywords:
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- timefold
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- optimization
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- scheduling
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- python
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- fastapi

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