Optimize sequence dependent roadmap

What it's for

Finds the value-preserving roadmap order when dependencies, context/setup costs, and decaying opportunity make simple priority sorting wrong.

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.

{
  "initiatives": [
    {
      "category": "platform",
      "duration": 2,
      "execution_cost": 10,
      "id": "foundation",
      "predecessor_ids": [],
      "value_decay_per_period": 0.02,
      "value_scenarios": [
        100,
        120
      ]
    },
    {
      "category": "product",
      "duration": 2,
      "execution_cost": 10,
      "id": "growth",
      "predecessor_ids": [
        "foundation"
      ],
      "value_decay_per_period": 0.2,
      "value_scenarios": [
        500,
        600
      ]
    },
    {
      "category": "platform",
      "duration": 2,
      "execution_cost": 10,
      "id": "cleanup",
      "predecessor_ids": [],
      "value_decay_per_period": 0.01,
      "value_scenarios": [
        80,
        100
      ]
    }
  ],
  "scenario_probabilities": [
    0.5,
    0.5

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": [
    "One aggregate execution stream performs initiatives sequentially, dependencies are complete and acyclic, duration and setup time are deterministic planning inputs, and scenario columns align value uncertainty across initiatives.",
    "Completion value decays exponentially and execution cost is complete and incremental; transition setup depends only on adjacent categories and is not a claim that people with different roles are interchangeable.",
    "Exact mode certifies only the represented sequences within the declared complete-sequence limit; heuristic mode has no optimality certificate and must be stress-tested against alternate durations, transitions, dependencies, and value-decay definitions."
  ],
  "configuration": {
    "enumerated_complete_sequences": 3,
    "exact_limit_exceeded": false,
    "initiative_count": 3,
    "maximum_exact_sequences": 100000,
    "scenario_count": 2,
    "solver_mode": "exact_enumeration",
    "starting_category": "platform",
    "tail_probability": 0.1,
    "transition_count": 2
  },
  "decision": "sequence_dependent_roadmap_optimized_exact",
  "method": "dependency_setup_time_decay_roadmap_sequence_v1",
  "schedule": [
    {
      "completion_time": 2,
      "expected_discounted_net_value": 95.6868,
      "initiative_id": "foundation",
      "position": 1,
      "setup_time": 0
    },
    {
      "completion_time": 5,
      "expected_discounted_net_value": 192.3337,
      "initiative_id": "growth",
      "position": 2,
      "setup_time": 1
    },
    {
      "completion_time": 12,
      "expected_discounted_net_value": 69.8228,
      "initiative_id": "cleanup",
      "position": 3,
      "setup_time": 5
    }
  ],
  "summary": {
    "cvar_discounted_net_value_loss": -320.9723,

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 Freeze the initiative DAG, aggregate sequential execution model, categories, pairwise setup times, durations, costs, aligned value scenarios, and time-decay rates.
2. 2 Enumerate every feasible complete sequence within the governed limit and choose maximum expected discounted net value; if the limit is exceeded, rebuild the entire order with a dependency-feasible value-density heuristic.
3. 3 Report the selected schedule, expected and tail value, exact sequence count, and fallback status; heuristic results abstain from any optimality claim and require sensitivity analysis.

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 a dependencyfeasible roadmap sequence under" }
  → finds "optimize_sequence_dependent_roadmap"

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

gitrevio_capability_run
  { "capability_id": "optimize_sequence_dependent_roadmap", "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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