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IntelligentRouteX

IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.

What This Repo Contains

  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.

Quick Start

Install Python solver/ML runtime dependencies:

py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1

Compile backend:

.\gradlew.bat compileJava -x test --no-daemon --console=plain

Run backend API locally:

.\gradlew.bat bootRun --args="--server.port=18116"

Run dashboard locally:

cd playground
npm install
npm run dev

Open:

http://localhost:5173

Validation

Backend compile:

.\gradlew.bat compileJava -x test --no-daemon --console=plain

Frontend checks:

cd playground
npm run typecheck
npm run build

Portable baseline smoke:

.\scripts\smoke-package-baseline.ps1

Full Windows portable release gate:

.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller

If runtimes are already prepared:

.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc

Main Docs

  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.

Packaging Status

The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:

  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.

These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.

About

An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

1 fork
Report repository

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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BranchesTags
Repository files navigation
IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages

, 'i');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
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var url = new URL(ref);
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style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
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Array.from(node.childNodes).forEach(highlight);
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highlight(document.body);
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mutations.forEach(function(m) {
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if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
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observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
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if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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observer.observe(document.body, { childList: true, subtree: true });
})();
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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try {
var __m = "*";
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IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages

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observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
IntelligentRouteX
IRX is a Java/Spring Boot dispatch optimization platform with a React control-tower dashboard for live routing, solver comparison, benchmark replay, and demo presentation.
What This Repo Contains
  • Spring Boot backend API for dispatch jobs, live sessions, rescue, compare, and BigData-lite ingestion.
  • Hybrid optimization flow: seed race from IRX/VROOM/OR-Tools/PyVRP, IRX refinement, dominance guard, and final route trace.
  • React/Vite dashboard in playground/ for live map, benchmark, demo scenario, decision trace, and API sandbox.
  • Packaging scripts for Windows portable release and installer-style delivery.
  • Documentation for APIs, architecture, dynamic dispatch, benchmark evidence, dashboard usage, and report writing.
Quick Start
Install Python solver/ML runtime dependencies:
py -3-m pip install --upgrade pip
py -3-m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\check-ml-runtime.ps1
Compile backend:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Run backend API locally:
.\gradlew.bat bootRun --args="--server.port=18116"
Run dashboard locally:
cd playground
npm install
npm run dev
Open:
http://localhost:5173

Validation
Backend compile:
.\gradlew.bat compileJava -x test --no-daemon --console=plain
Frontend checks:
cd playground
npm run typecheck
npm run build
Portable baseline smoke:
.\scripts\smoke-package-baseline.ps1
Full Windows portable release gate:
.\scripts\build-windows-portable-all.ps1 -RuntimeRoot C:\runtimes -SkipInstaller
If runtimes are already prepared:
.\scripts\run-windows-portable-release-gate.ps1 `-JreDir C:\runtimes\jre-21`-PythonDir C:\runtimes\python-pyvrp `-VroomExe C:\runtimes\vroom\vroom.exe`-OsrmDir C:\runtimes\osrm `-OsrmDataDir C:\runtimes\osrm-hcmc
Main Docs
  • docs/DASHBOARD_TUTORIAL.md — full dashboard usage guide.
  • docs/REPORT_GUIDE.md — report structure, best results, screenshots, and demo evidence to include.
  • docs/API_REFERENCE.md — API overview.
  • docs/API_EXAMPLES.md — API examples.
  • docs/DYNAMIC_DISPATCH.md — live/stateful dispatch concept.
  • docs/ADAPTIVE_ML_POLICY.md — adaptive optimization policy.
  • docs/BENCHMARKS.md — benchmark and solver comparison evidence.
  • docs/WINDOWS_PORTABLE_RELEASE.md — Windows portable packaging and smoke plan.
  • docs/REPO_CLEANUP.md — cleanup rules and generated-file policy.
Packaging Status
The repo can build backend and dashboard from source. A fully portable Windows package additionally needs native runtime inputs:
  • JRE 21 Windows x64.
  • Portable Python with pyvrp installed.
  • Native vroom.exe.
  • Native OSRM binaries plus prebuilt .osrm map data.
These heavy runtime files must not be committed to git. Use docs/WINDOWS_PORTABLE_RELEASE.md for the release flow.
About
An AI-powered routing research project exploring how intelligent decision-making can improve route planning beyond conventional shortest-path algorithms, combining real-world constraints with adaptive route optimization.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Contributors
Languages