VELORA - KRITI Optimization

Welcome to the KRITI Optimization System, a robust full-stack application built to solve advanced routing, vehicle scheduling, and dynamic allocation problems utilizing state-of-the-art C++ heuristic algorithms combined with an interactive Web Application frontend.

---

🏛️ Repository Architecture

This codebase has been heavily engineered and structured according to modern C++ conventions and standard web architecture:

KRITI-Optimization/
├── frontend/                 # Next.js React Web Application
├── include/
│   └── third_party/          # Header-only dependencies (crow_all.h, json.hpp)
├── src/
│   ├── algorithms/           # Isolated Routing & Optimization Solvers
│   │   ├── alns/             # Adaptive Large Neighborhood Search
│   │   ├── branch_and_cut/   # Exact Branch and Cut Logic
│   │   ├── crdp/             # Clustering-Routing-DP Solver
│   │   ├── god_vns/          # Generic Optimization & Variable Neighborhood Search
│   │   ├── heterogeneous_darp/# Heterogeneous DARP logic
│   │   ├── memetic/          # Memetic Algorithm logic
│   │   └── vns/              # Additional VNS implementations
│   └── server/               
│       └── main.cpp          # The Crow Backend API Server
├── build/
│   └── bin/                  # Centralized output registry for all compiled executables
├── Makefile                  # Root automated compilation logic
└── package.json              # Node dependencies (e.g. clang-format hooks)

---

🧠 The Engine: main.cpp

The backend orchestrator (src/server/main.cpp) acts as the synchronization and translation pipeline bridging user-supplied inputs and complex C++ solvers. The major operations configured in main.cpp include:

1. Crow REST API: It establishes a lightning-fast HTTP server to interpret massive multi-part HTTP POST data forms housing employee/vehicle CSV configurations.
2. Ephemeral Space Generation: For data integrity, it constructs isolated, zero-collision /tmp/req_<UUID> sandbox environments ensuring multiple simultaneous API requests never overlap or corrupt shared directories.
3. Automated Distance Matrixing: Utilizes system curl pipelines to dynamically interact with the OSRM Networking Project to compile legitimate traffic-distance calculation matrices for nodes. Automatically engages mathematical fallback Haversine routing locally if API networks fail.
4. Concurrent Execution Engine: Leveraging std::thread, the backend simultaneously boots execution threads mapping directly to build/bin/main_alns, build/bin/main_bac, and others. These binaries read the sandbox CSV matrices and output algorithmic payload routing solutions.
5. JSON Aggregation: Translates parsed algorithm CSV responses identically through the single framework and pipes structured .json payloads back directly to the frontend.

---

🚀 How to Run the Application

The project requires both a C++ building infrastructure and a Node infrastructure.

Prerequisites

Ensure your local environment contains the following applications:

• g++ (Must support C++17)
• make (GNU Make to handle compilation)
• curl (System tool required by the C++ backend for external Matrix API fetching)
• node & npm (Required to run the Frontend interface)

Step 1: Compile the Backend Algorithms

We have condensed the complexities of the various C++ solvers into a single command-line step. Run the following command exactly in your root directory:

make clean all

This command mechanically sets up environment bindings, compiles the complex ALNS, Branch/Cut, VNS structures, and builds the Crow server—depositing all native executables safely in build/bin/.

Step 2: Start the Backend Server

Once compiled, you can launch the backend environment. By default, Crow exposes the primary interface on Port 5555.

./build/bin/server_app

Step 3: Run the Web Frontend

Open a new terminal window and navigate into your React server directory. Boot the development frontend seamlessly:

cd frontend
npm install
npm run dev

You can now interact with the entire dashboard UI conventionally by navigating your browser to http://localhost:3000.

---

🛠️ Code Maintenance

This repository utilizes rigorous indentation formatting to ensure the ecosystem scales optimally.

• Use clang-format exclusively for formatting any novel changes inserted into the C++ domain using the root .clang-format profile.
• Variable names standardize toward snake_case definitions for data handling and function declarations. Class structures default strictly to CamelCase.