Optimize stochastic flow control MPC

What it's for

Moves workflow optimization beyond bottleneck reporting and static what-if simulation into a governed closed-loop decision: what capacity or WIP control should change now, given tail delivery risk?

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "actions": [
    {
      "capacity_delta": {},
      "id": "baseline",
      "operating_cost": 0
    },
    {
      "capacity_delta": {
        "review": 3
      },
      "id": "review-swarm",
      "operating_cost": 2
    },
    {
      "capacity_delta": {},
      "first_stage_release_limit": 3,
      "id": "wip-control",
      "operating_cost": 0.5
    }
  ],
  "baseline_action_id": "baseline",
  "exact_sequence_limit": 100,
  "horizon_periods": 4,
  "maximum_action_changes": 3,
  "scenarios": [
    {
      "arrivals_by_period": [
        4,
        4,
        4,
        4
      ],
      "capacity_multipliers": {
        "implementation": [
          1,
          1,
          1,
          1
        ],
        "review": [
          1,
          1,
          1,

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "The ordered stages form a serial workflow and work advances at most one stage per decision period.",
    "Arrival probabilities, capacity multipliers, costs, and control effects are calibrated local scenarios.",
    "Only the first action is deployable; the remaining sequence is an open-loop explanation that must be re-optimized after the next state observation."
  ],
  "baseline_comparison": {
    "action_id": "baseline",
    "cvar_cost": 419.2,
    "expected_cost": 367.96,
    "objective": 393.58
  },
  "configuration": {
    "cvar_level": 0.9,
    "discount_rate_per_period": 0,
    "horizon_periods": 4,
    "minimum_objective_improvement": 0,
    "risk_aversion": 0.5
  },
  "decision": "deploy_first_action_and_reoptimize",
  "executive_summary": {
    "baseline_objective": 393.58,
    "expected_delivered_items": 21.5,
    "expected_ending_backlog": 19.7,
    "objective_improvement": 116.42,
    "selected_cvar_cost": 314.4,
    "selected_expected_cost": 239.92,
    "selected_objective": 277.16
  },
  "illustrative_open_loop_sequence": [
    "review-swarm",
    "review-swarm",
    "review-swarm",
    "review-swarm"
  ],
  "interpretation": "This is risk-averse model-predictive control, not a fixed multi-period commitment. Beam-search results disclose that global optimality is not guaranteed.",
  "method": "risk_averse_stochastic_receding_horizon_flow_mpc_v1",
  "optimization": {
    "candidate_sequences_retained": 81,
    "globally_optimal_over_declared_sequences": true,
    "maximum_action_changes": 3,
    "potential_sequences": 81,
    "search_method": "exact_enumeration",
    "sequence_evaluations": 82

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Propagate each calibrated scenario through an ordered workflow where arrivals join the first stage and completed work advances at most one stage per decision period.
2. 2 Evaluate control sequences using discounted holding, operating, switching, and terminal backlog costs under both probability-weighted expectation and tail CVaR.
3. 3 Search all feasible sequences when the declared action/horizon space is bounded; otherwise retain a disclosed beam of best partial sequences without claiming global optimality.
4. 4 Recommend only the first control, observe the next workflow state, and solve the horizon again; later actions are an explanation rather than a commitment.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "optimize the next deliveryflow control with" }
  → finds "optimize_stochastic_flow_control_mpc"

gitrevio_capability_describe
  { "capability_id": "optimize_stochastic_flow_control_mpc" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "optimize_stochastic_flow_control_mpc", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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