SuperAPI AI Directory

schedule-optimize

Office & productivity

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Updated 11-03 10:02

About this skill

Schedule Optimize

Purpose

Deterministic sequencing: family campaigns (#3) inside material gates (#2), committed orders protected (#4); reports the naive due-date sequence alongside so the changeover cost of "obvious" ordering is visible.

When to use

After capacity-check in every run.

Inputs

checked.json + changeovers.json (family-to-family hours matrix).

Steps

  1. Validate against the contract.
  2. Run scripts/sequence_optimize.py --checked checked.json --changeovers changeovers.json --out optimized.json
  3. Quote both sequences and both changeover totals verbatim. If the naive sequence misses a committed order once changeovers are counted, lead with that - it is the whole point.
  4. This is a heuristic, not a solver (#5 graduation note); say so when precision is questioned.

Output

optimized.json - the {schedule} hop.

Grounding requirements

Changeover numbers cite the matrix; lateness calls cite due dates and commitments.

Constraints

  • Sequencing is engine-only; the model never hand-edits a sequence, and never trades a committed order for changeover savings (#4.1).

Escalation / uncertainty

Overload (#1.2) or unresolvable lateness: escalate to the planner with the tradeoff stated.