⚡ OptimEngine

Operations Intelligence for AI Agents — L1 → L3 in one conversation.

11 MCP tools that optimize, quantify risk, forecast, and prescribe. From production scheduling to Monte Carlo simulation. From delivery routing to Pareto frontiers. Ask in natural language, get optimal decisions.

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🤖 Use with Claude (60 seconds)

No installation. No code. Works on claude.ai (Free, Pro, Max, Team, Enterprise).

1. Open claude.ai
2. Click + → Integrations
3. Add custom integration
4. Paste this URL:

https://optim-engine-production.up.railway.app/mcp/sse

Claude discovers all 11 tools automatically. Try asking:

"Schedule 5 production orders on 4 machines, minimize delays"

"Optimize delivery routes for 6 clients with 2 trucks"

"What happens if dosing time increases 30%? Run sensitivity analysis"

"I have historical data for the last 8 weeks. Forecast next month and recommend actions"

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🧠 What Is OptimEngine?

An Operations Intelligence Engine — not a wrapper, not a chatbot. A computational decision brain that solves NP-hard optimization problems and quantifies risk. Powered by Google OR-Tools CP-SAT and Routing solvers.

4 Intelligence Levels

Level	Capability	Question It Answers
L1 Deterministic	Scheduling, Routing, Packing, Validation	What's the optimal plan?
L2 Uncertainty	Sensitivity, Robust, Stochastic	How fragile is this plan?
L2.5 Multi-Objective	Pareto Frontier	What's the best trade-off?
L3 Prescriptive	Forecast → Optimize → Risk → Advise	What should I do and why?

11 MCP Tools

L1 — Deterministic Optimization

• optimize_schedule — Flexible Job Shop (FJSP) with precedence, setup times, priorities, 4 objectives
• optimize_routing — CVRPTW with capacity, time windows, distance matrix, drop visits
• optimize_packing — Multi-dimensional bin packing with weight, volume, groups
• validate_schedule — Find overlaps, precedence violations, eligibility errors

L2 — Optimization under Uncertainty

• analyze_sensitivity — Parametric perturbation, elasticity, risk ranking
• optimize_robust — Worst-case / percentile protection, price of robustness
• optimize_stochastic — Monte Carlo + CVaR with 4 distributions

L2.5 — Multi-Objective

• optimize_pareto — 2-4 competing objectives, trade-off analysis, correlation

L3 — Prescriptive Intelligence

• prescriptive_advise — 4 forecast methods, 3 risk appetites, confidence intervals, action items

Infrastructure

• health_check — System status
• root — Server info, capabilities, tool listing

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📊 Live Demo Results

Every number below comes from a real call to OptimEngine. Zero mock data.

Digital Twin Decisionale — NovaCosm (Cosmetics Manufacturer)

Full production-to-delivery chain: 6 lines, 8 orders, 5 brand clients.

Phase	Tool	Result
Plant diagnosis	optimize_schedule	575 min makespan, 2 late, Line 2 bottleneck (82.6%)
Cycle time forecast	prescriptive_advise	3 rising trends (+1.1-1.65%/week), +5.6% makespan in 4 weeks
What-if: cross-line	optimize_schedule	Move 1 product to Line 1 → tardiness -65%, lines balanced
Risk profile	optimize_stochastic	50 Monte Carlo, CV 4.5%, 100% feasible
Client doubles orders	optimize_schedule	Without investment: 530 min tardiness, 50% late
+ New line investment	optimize_schedule	With Line 2B: 0 tardiness, 478 min, 8/8 on-time
Manual schedule check	validate_schedule	4 violations found (overlaps + precedence)
Risk ranking	analyze_sensitivity	Serum dosing most critical (score 11.3, elasticity 0.227)
Delivery routing	optimize_routing	6 clients, 2 trucks, 70 km, all time windows met
Truck loading	optimize_packing	8/8 pallets, 0 excluded, route-constrained

Strategic decisions generated: cross-line authorization (saves 125 min/day), maintenance alert (Line 2 degrading), investment quantification (Line 2B enables client growth).

BevDistri (F&B HoReCa Distribution)

Tool	Result
optimize_routing	18 clients, 2/3 vehicles used, 132 km, all windows met
prescriptive_advise	+21% Modena demand in 4 weeks, decision deadline identified
optimize_packing	13 items, 2 bins, 0 drops, 97% utilization alert
analyze_sensitivity	Hotel demand can double without route split

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🔧 For Developers

MCP Configuration (Claude Desktop, Cursor)

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

Direct API

curl -X POST https://optim-engine-production.up.railway.app/optimize_schedule \
  -H "Content-Type: application/json" \
  -d '{
    "jobs": [
      {
        "job_id": "ORD-001",
        "priority": 8,
        "due_date": 480,
        "tasks": [
          {"task_id": "dosing", "duration": 90, "eligible_machines": ["line_A", "line_B"], "setup_time": 15},
          {"task_id": "filling", "duration": 60, "eligible_machines": ["line_A", "line_D"], "setup_time": 10}
        ]
      }
    ],
    "machines": [{"machine_id": "line_A"}, {"machine_id": "line_B"}, {"machine_id": "line_D"}],
    "objective": "minimize_makespan"
  }'

Orchestration Pattern: Routing → Packing

When combining routing and packing (e.g., delivery logistics), use this pattern:

1. Call optimize_routing → get routes with vehicle-to-client assignments
2. Partition items by route → each vehicle's items based on routing output
3. Call optimize_packing per vehicle → separate packing per truck/van

This ensures pallet assignments match delivery routes. See the NovaCosm demo for a complete example.

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🌐 Available On

Platform	Link
Claude.ai	Add as custom integration (instructions above)
MCPize	mcpize.com/mcp/optim-engine
Apify Store	apify.com/hearty_indentation/optim-engine
LobeHub	lobehub.com/mcp/michelecampi-optim-engine
mcp.so	mcp.so/server/optim-engine
Railway	optim-engine-production.up.railway.app
ERC-8004	Agent #22518 on Base L2
Landing Page	optim-engine-landing.vercel.app

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📈 Numbers

Metric	Value
Solver modules	9
MCP tools	11
Tests passing	121
Intelligence levels	4 (L1, L2, L2.5, L3)
Forecast methods	4
Stochastic distributions	4
Scheduling objectives	4
Routing objectives	4
Risk appetites	3
ERC-8004 Agent	#22518 (Base L2)
Capital invested	€0

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🏗️ Architecture

                    ┌─────────────────────────────────┐
                    │         Claude / AI Agent        │
                    │    (natural language interface)   │
                    └──────────────┬──────────────────┘
                                   │ MCP Protocol
                    ┌──────────────▼──────────────────┐
                    │      OptimEngine v8.0.0          │
                    │      FastAPI + MCP Server        │
                    ├─────────────────────────────────┤
                    │  L1 Deterministic                │
                    │  ├─ Scheduling (CP-SAT FJSP)     │
                    │  ├─ Routing (OR-Tools CVRPTW)    │
                    │  ├─ Packing (CP-SAT)             │
                    │  └─ Validator                    │
                    ├─────────────────────────────────┤
                    │  L2 Uncertainty                  │
                    │  ├─ Sensitivity Analysis          │
                    │  ├─ Robust Optimization           │
                    │  └─ Stochastic (Monte Carlo)     │
                    ├─────────────────────────────────┤
                    │  L2.5 Multi-Objective             │
                    │  └─ Pareto Frontier               │
                    ├─────────────────────────────────┤
                    │  L3 Prescriptive Intelligence     │
                    │  └─ Forecast → Optimize → Advise  │
                    └─────────────────────────────────┘
                         Google OR-Tools · Python
                         Railway · ERC-8004 Base L2

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🤝 Consulting & Custom Integration

Need OptimEngine configured for your specific operations? Production scheduling, logistics optimization, risk analysis for your plant?

I build Digital Twin Decisional systems — from scheduling diagnosis to strategic what-if analysis. The solver runs in seconds; the domain expertise makes it useful.

Michele Campi — Operations Intelligence Engineer

• GitHub: @MicheleCampi
• 7+ years operations controlling in cosmetics contract manufacturing
• Built OptimEngine solo: 11 tools, 4 intelligence levels, 121 tests, zero capital

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License

MIT — use it freely. The code is open; the intelligence design is the moat.