spec_version: 1
name: berth_planning
type: space
runtime: fastapi
app: berth_openenv.server:app
port: 8000
description: Dock planning at the Port of Barcelona on real 2024 container calls - re-plan one to three weeks at a quay with real crane fleets, movement limits, gales under the port's wind rules, crane outages, closures, late and bunched ships, diverted traffic, emergencies and priority cargo; graded once on submit against the CP-SAT optimum. 1,050 train and 50 eval tasks (dock-v1), plus the first 100-task pack (berth-v1).
version: "0.1.0"
variables:
  BERTH_TASKS_DIR: ""        # colon-separated pack directories; defaults to tasks/dock-v1-eval:tasks/dock-v1-train
  BERTH_RUNS_DIR: ""         # rollout runs shown in the viewer
  BERTH_MAX_CHECKS: "10"
  BERTH_MAX_TOOL_CALLS: "24"
  MAX_CONCURRENT_ENVS: "64"
validation:
  reward:
    range: [0.0, 1.0]
    # oracle/solve.py submits the CP-SAT optimum and scores exactly 1.0; no plan scores 0, any rule broken <= 0.2
    oracle_tolerance: 0.001
    floor_margin: 0.5
  resources:
    # pure Python: the grader re-checks a plan in milliseconds, CP-SAT never runs on the server
    cpu: 2.0
    memory_mb: 4096
    disk_mb: 2048
    episode_timeout_s: 3600.0
  network:
    mode: no-network
  capabilities:
    verifier:
      kind: reward_channel
    oracle:
      form: script
      location: oracle/solve.py
    rubric_tree: true
    task_api: true
    declared_tools: [get_situation, check_plan, submit_plan]
    declared_task_count:
      eval: 50
      train: 1050
  types:
    tags: [simulation, logistics, scheduling, operations-research, mcp, real-world-data]