⚡ OptimEngine — Operations Intelligence Solver

The first MCP Server for production scheduling, vehicle routing, and bin packing optimization.

An AI-native solver that assigns tasks to machines, deliveries to vehicles, and items to bins optimally using constraint programming. Built for the agentic economy: AI agents discover it, call it, and pay for it — autonomously.

What It Does

OptimEngine solves three families of NP-hard optimization problems that LLMs cannot compute:

1. Scheduling — Flexible Job Shop (FJSP)

Assign tasks to machines optimally with precedence, time windows, machine eligibility, setup times, priorities, and multiple objectives.

2. Routing — CVRPTW

Assign delivery locations to vehicles optimally with capacity constraints, time windows, service times, GPS coordinates, and multiple objectives.

3. Bin Packing

Assign items to bins/containers optimally with weight/volume constraints, item quantities, group constraints, and multiple objectives.

The core insight: LLMs understand optimization requests in natural language but cannot compute optimal solutions. These are NP-hard problems that require specialized solvers. OptimEngine is that solver, exposed as MCP tools that any AI agent can call.

MCP Tools

Scheduling Capabilities

Routing Capabilities

Packing Capabilities

Quick Start

1. Install & Run

git clone https://github.com/MicheleCampi/optim-engine.git
cd optim-engine
pip install -r requirements.txt
uvicorn api.server:app --host 0.0.0.0 --port 8000

Server starts at http://localhost:8000. Docs at /docs. MCP endpoint at /mcp.

2. Connect via MCP (Claude Desktop, Cursor, etc.)

{
  "mcpServers": {
    "optim-engine": {
      "command": "mcp-proxy",
      "args": ["https://optim-engine-production.up.railway.app/mcp"]
    }
  }
}

Example — Scheduling

curl -X POST https://optim-engine-production.up.railway.app/optimize_schedule \
  -H "Content-Type: application/json" \
  -d '{
    "jobs": [
      {"job_id": "J1", "tasks": [
        {"task_id": "cut", "duration": 3, "eligible_machines": ["M1", "M2"]},
        {"task_id": "weld", "duration": 2, "eligible_machines": ["M2"]}
      ], "due_date": 10},
      {"job_id": "J2", "tasks": [
        {"task_id": "cut", "duration": 4, "eligible_machines": ["M1"]},
        {"task_id": "weld", "duration": 3, "eligible_machines": ["M2"]}
      ], "due_date": 12}
    ],
    "machines": [{"machine_id": "M1"}, {"machine_id": "M2"}],
    "objective": "minimize_makespan"
  }'

Example — Routing

curl -X POST https://optim-engine-production.up.railway.app/optimize_routing \
  -H "Content-Type: application/json" \
  -d '{
    "depot_id": "warehouse",
    "locations": [
      {"location_id": "warehouse", "demand": 0},
      {"location_id": "customer_A", "demand": 20, "time_window_end": 3000, "service_time": 10},
      {"location_id": "customer_B", "demand": 15, "time_window_end": 4000, "service_time": 10},
      {"location_id": "customer_C", "demand": 25, "time_window_end": 5000, "service_time": 15}
    ],
    "vehicles": [
      {"vehicle_id": "truck_1", "capacity": 40},
      {"vehicle_id": "truck_2", "capacity": 40}
    ],
    "distance_matrix": [
      {"from_id": "warehouse", "to_id": "customer_A", "distance": 500, "travel_time": 500},
      {"from_id": "warehouse", "to_id": "customer_B", "distance": 800, "travel_time": 800},
      {"from_id": "warehouse", "to_id": "customer_C", "distance": 600, "travel_time": 600},
      {"from_id": "customer_A", "to_id": "warehouse", "distance": 500, "travel_time": 500},
      {"from_id": "customer_A", "to_id": "customer_B", "distance": 400, "travel_time": 400},
      {"from_id": "customer_A", "to_id": "customer_C", "distance": 700, "travel_time": 700},
      {"from_id": "customer_B", "to_id": "warehouse", "distance": 800, "travel_time": 800},
      {"from_id": "customer_B", "to_id": "customer_A", "distance": 400, "travel_time": 400},
      {"from_id": "customer_B", "to_id": "customer_C", "distance": 300, "travel_time": 300},
      {"from_id": "customer_C", "to_id": "warehouse", "distance": 600, "travel_time": 600},
      {"from_id": "customer_C", "to_id": "customer_A", "distance": 700, "travel_time": 700},
      {"from_id": "customer_C", "to_id": "customer_B", "distance": 300, "travel_time": 300}
    ],
    "objective": "minimize_total_distance"
  }'

Example — Bin Packing

curl -X POST https://optim-engine-production.up.railway.app/optimize_packing \
  -H "Content-Type: application/json" \
  -d '{
    "items": [
      {"item_id": "laptop", "weight": 3, "volume": 8, "value": 1200, "quantity": 10},
      {"item_id": "monitor", "weight": 8, "volume": 25, "value": 500, "quantity": 5},
      {"item_id": "keyboard", "weight": 1, "volume": 3, "value": 80, "quantity": 20}
    ],
    "bins": [
      {"bin_id": "small_box", "weight_capacity": 20, "volume_capacity": 50, "cost": 5, "quantity": 5},
      {"bin_id": "large_box", "weight_capacity": 50, "volume_capacity": 120, "cost": 12, "quantity": 3}
    ],
    "objective": "minimize_bins"
  }'

Use Cases

• Manufacturing: Production scheduling for contract manufacturing (cosmetics, pharma, food)
• Logistics: Last-mile delivery routing with time windows and capacity
• Warehouse: Bin packing for palletization, container loading, order fulfillment
• Cloud/IT: Resource allocation (VMs to servers, jobs to clusters)
• Food Delivery: Multi-driver route optimization
• Supply Chain: End-to-end scheduling + routing + packing

Architecture

AI Agent (Claude, GPT, Gemini, etc.)
    │
    ▼ MCP Protocol
┌────────────────────────────────────────┐
│  FastAPI + fastapi-mcp                  │  ← API layer
├────────────┬────────────┬──────────────┤
│ Scheduling │  Routing   │  Bin Packing │
│ CP-SAT     │  Routing   │  CP-SAT      │
│            │  Library   │              │  ← OR-Tools solvers
├────────────┴────────────┴──────────────┤
│  Pydantic Models                        │  ← Schema contract
└────────────────────────────────────────┘

Stack: Python 3.12 · FastAPI · OR-Tools (CP-SAT + Routing) · fastapi-mcp · Pydantic v2

Tests

pip install pytest
python -m pytest tests/ -v

97 tests covering: flexible job shop, time windows, due dates, machine availability, setup times, CVRPTW routing, capacity, GPS distances, bin packing, weight/volume constraints, group constraints, partial packing, and realistic manufacturing/delivery/warehouse scenarios.

Landing Page

🌐 optim-engine.vercel.app

Marketplace Listings

• MCPize — 6 MCP tools
• Apify Store — 130k+ users/month
• LobeHub — Top MCP directory

License

MIT

Built with Google OR-Tools — the optimization toolkit used by Google for fleet routing, scheduling, and resource allocation at scale.