Micronaut Optimizer

Rationale

This repository contains the Micronaut Optimizer project, which aims to optimize various computational problems using the Micronaut framework. The project leverages the power of Micronaut for building lightweight, modular, and efficient applications.

Architecture

The architecture of the Micronaut Optimizer is visualized using PlantUML. Below is the updated PlantUML diagram representing the high-level architecture of the project.

@startuml
package "io.github.seehiong" {
    package "controller" {
        class ProgressController
        class ProblemController
    }
    
    package "factory" {
        class SolverServiceFactory
    }
    
    package "service" {
        class SolverService
        class BaseSolverService
        class FLPService
    }
    
    package "utils" {
        class DisposableUtils << (U,orchid) utility >>
        class CoordUtils << (U,orchid) utility >>
        class FileUtils << (U,orchid) utility >>
    }

    package "model" {
        abstract class Input
        class FLPInput
        abstract class Output
        class FLPOutput
        interface Constraint
        interface Objective
        interface Metric
        interface Metadata
    }   

    package "solver" {
        interface Solver
        class FLPSolver
        class TSPSolver
    }    
}

SolverService <|.. BaseSolverService
BaseSolverService <|-- FLPService
FLPService --> DisposableUtils

ProgressController --> Output
ProblemController --> SolverService
SolverServiceFactory -> SolverService
FLPSolver --|> Solver
TSPSolver --|> Solver

Input <|-- FLPInput
Output <|-- FLPOutput
Input --> Constraint
Input --> Objective
Output --> Metric
Output --> Metadata

FLPSolver --> CoordUtils
FLPSolver --> FileUtils
@enduml

optimizer-architecture

Key Features

• Real-time Progress Monitoring: View optimization progress through SSE (Server-Sent Events)
• Multiple Solver Support:
  • Traveling Salesman Problem (TSP)
  • Facility Location Problem (FLP)
• Interactive Visualization: Dynamic visualization of solution progress
• Factory Pattern: Extensible solver framework

Folder Structure

The folder structure of the project is as follows:

micronaut-optimizer/
├── src/main/
│   ├── java/io/github/seehiong/
│   │   ├── controller/   # REST endpoints
│   │   ├── factory/      # Solver factory implementation
│   │   ├── micronaut/    # Application configuration
│   │   ├── model/        # Data models and DTOs
│   │   ├── service/      # Business logic layer
│   │   ├── solver/       # Optimization algorithms
│   │   └── utils/        # Utility classes
│   └── resources/
│       ├── application.yml  # Micronaut configuration
│       ├── logback.yml      # Logging configuration
│       └── static/          # Static resources (HTML, CSS, JS)
│           ├── flp-progress.html
│           ├── tsp-progress.html
│           └── cvrp-progress.html
├── build.gradle
├── settings.gradle
├── README.md

Prerequisites

• Java 21 or higher
• Gradle 8.11 or higher
• Web browser with SSE support

Installation

1. Clone the repository:

git clone https://github.com/seehiong/micronaut-optimizer.git
cd micronaut-optimizer

2. Build the project:

./gradlew clean build

3. Run the application:

./gradlew run

4. Access the visualization:

• TSP Progress: http://localhost:8080/tsp-progress.html
• FLP Progress: http://localhost:8080/flp-progress.html

API Endpoints

POST /solve/{problem}           # Solve a problem with raw input
POST /solve/{problem}/upload    # Solve a problem with file upload
GET /progress/latest/{solverId} # Get the latest output for a solver
GET /progress/{solverId}        # Stream optimization progress

Sample Inputs and Progress

Facility Location Problem (FLP)

Input Json

• Post to http://localhost:8080/solve/flp
• Navigate to http://localhost:8080/flp-progress.html?xScale=1500&yScale=1500&solverId=<SOLVER_ID> for progess:

{
    "facilityCostConstraint": {
        "costs": [100, 100, 100]
    },
    "facilityCapacityConstraint": {
        "capacities": [100, 100, 500]
    },    
    "facilityCoordinateConstraint": {
        "coordinates": [
            {"x": "1065", "y": "1065"},
            {"x": "1062", "y": "1062"},
            {"x": "0",    "y": "0"}
        ]
    },
    "customerCoordinateConstraint": {
        "coordinates": [
            {"x": "1397", "y": "1397"},
            {"x": "1398", "y": "1398"},
            {"x": "1399", "y": "1399"},
            {"x": "586",  "y": "586"},
            {"x": "900",  "y": "900"},
            {"x": "910",  "y": "910"},
            {"x": "1200", "y": "1200"},
            {"x": "1210", "y": "1210"}
        ]
    },
    "customerDemandConstraint": {
        "demands": [50, 50, 75, 75, 80, 80, 90, 90]
    }
}

Progress

Traveling Salesman Problem (TSP)

Input Json

• Post to http://localhost:8080/solve/tsp
• Navigate to http://localhost:8080/tsp-progress.html?xScale=5&yScale=5&solverId=<SOLVER_ID> for progess:

{
    "distanceMatrixConstraint": {
        "distances": [
            [ 0, 10, 15, 20, 25 ],
            [10,  0, 35, 25, 20 ],
            [15, 35,  0, 30, 10 ],
            [20, 25, 30,  0, 15 ],
            [25, 20, 10, 15,  0 ]
        ]
    },
    "solveTimeConstraint": {
        "solveTime": "30s"
    },
    "minMaxObjective": {
        "minMaxEnum": "MINIMIZE"
    }
}

Progress

Capacitated Vehicle Routing Problem (CVRP)

Input Json

• Post to http://localhost:8080/solve/cvrp
• Navigate to http://localhost:8080/cvrp-progress.html?xScale=20&yScale=20&solverId=<SOLVER_ID> for progess:

{
    "vehicleConstraint": {
        "vehicleNumber": 4,
        "capacity": 15
    },
    "customerDemandConstraint": {
        "demands": [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]
    },
    "distanceMatrixConstraint": {
        "distances": [
            [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],
            [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],
            [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],
            [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],
            [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],
            [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],
            [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],
            [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],
            [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],
            [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],
            [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],
            [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],
            [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],
            [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],
            [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],
            [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],
            [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0]
        ]
    },
    "solveTimeConstraint": {
        "timeInSeconds": "10"
    }
}

Progress

Bin Packing Problem (BPP)

Input Json

• Post to http://localhost:8080/solve/bpp
• Navigate to http://localhost:8080/bpp-progress.html?solverId=<SOLVER_ID> for progess:

{
    "itemWeightConstraint": {
        "weights": [48, 30, 19, 36, 36, 27, 42, 42, 36, 24, 30]
    },
    "binCapacityConstraint": {
        "capacity": 100
    }
}

Progress

Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

License

MIT License