الدروس التعليمية

from dispatchatlas.bench import smoke_benchmark_provider
 
provider = smoke_benchmark_provider(root_seed=20260527)
for problem_id in provider.list_problem_ids():
    print(problem_id.value)

smoke-cloud-edge-0
smoke-cloud-edge-1
smoke-workflow-0
smoke-workflow-1
smoke-machine-scheduling-unrelated-0
smoke-machine-scheduling-unrelated-1
smoke-job-shop-0
smoke-job-shop-1
smoke-flexible-job-shop-0
smoke-flexible-job-shop-1
smoke-permutation-flow-shop-0
smoke-permutation-flow-shop-1
smoke-setup-flow-shop-0
smoke-setup-flow-shop-1
smoke-rcpsp-renewable-0
smoke-rcpsp-renewable-1
smoke-open-shop-0
smoke-open-shop-1
smoke-hybrid-flow-shop-0
smoke-hybrid-flow-shop-1
smoke-distributed-permutation-flow-shop-0
smoke-distributed-permutation-flow-shop-1
smoke-no-wait-flow-shop-0
smoke-no-wait-flow-shop-1
smoke-blocking-flow-shop-0
smoke-blocking-flow-shop-1
smoke-distributed-assembly-flow-shop-0
smoke-distributed-assembly-flow-shop-1
smoke-multi-objective-pfsp-0
smoke-multi-objective-pfsp-1
smoke-rcpsp-max-0
smoke-rcpsp-max-1
smoke-rcpsp-multi-mode-0
smoke-rcpsp-multi-mode-1
smoke-multi-project-rcpsp-0
smoke-multi-project-rcpsp-1
smoke-unrelated-parallel-setup-0
smoke-unrelated-parallel-setup-1
smoke-reentrant-fab-0
smoke-reentrant-fab-1
smoke-distributed-flexible-job-shop-0
smoke-distributed-flexible-job-shop-1
smoke-facility-assignment-0
smoke-facility-assignment-1

from dispatchatlas.bench import smoke_benchmark_provider
from dispatchatlas.core import ProblemId, TerminationPolicy, derive_seed
from dispatchatlas.solve import default_solver_registry
 
provider = smoke_benchmark_provider(root_seed=20260527)
validated = provider.get_problem(ProblemId("smoke-job-shop-0"))
spec = validated.spec
 
print(f"problem: {spec.id.value}")
print(f"tasks: {len(spec.tasks)}  resources: {len(spec.resources)}")
 
registry = default_solver_registry()
for solver_id in ("earliest-start", "shortest-processing-time"):
    metadata = registry.get_metadata(solver_id)
    solver = registry.create(solver_id)
    run = solver.solve(
        problem=validated,
        stop=TerminationPolicy(max_iterations=metadata.default_stop.max_iterations),
        seed=derive_seed(20260610, "docs.tutorial.compare", 0),
    )
    makespan = run.result.objective_values[0]
    print(
        f"{solver_id}: feasible={run.result.feasible} "
        f"{makespan.objective_name}={makespan.value:.1f}"
    )

problem: smoke-job-shop-0
tasks: 9  resources: 3
earliest-start: feasible=True makespan=353.0
shortest-processing-time: feasible=True makespan=433.0

from dispatchatlas.solve import default_solver_registry
 
registry = default_solver_registry()
metadata = registry.get_metadata("shortest-processing-time")
print(f"solver: {metadata.solver_id}")
print(f"stochasticity: {metadata.stochasticity}")
print(f"citation: {metadata.citation.reference}")
print("capabilities:", ", ".join(c.value for c in metadata.capabilities))

solver: shortest-processing-time
stochasticity: deterministic
citation: Smith, W. E. (1956). Various optimizers for single-stage production. Naval Research Logistics Quarterly, 3(1-2), 59-66.
capabilities: single-objective, capacity-aware, precedence-aware, constructive, dispatching, deterministic

from pathlib import Path
 
from dispatchatlas.core import TerminationPolicy
from dispatchatlas.lab import (
    CampaignConfig,
    CampaignKind,
    CampaignStage,
    ExecutionMode,
    OutputPolicy,
    ResourceBudget,
    default_campaign_runner,
)
 
workspace = Path("my-campaigns")
 
config = CampaignConfig(
    campaign_id="first-campaign",
    benchmark_ids=("smoke-job-shop-0", "smoke-workflow-0"),
    solver_ids=("earliest-start", "shortest-processing-time"),
    objectives=("makespan",),
    root_seed=20260610,
    seed_namespace="docs.tutorial.first-campaign",
    stop=TerminationPolicy(max_iterations=5),
    output=OutputPolicy(root_dir=str(workspace)),
    stage=CampaignStage.SMOKE,
    kind=CampaignKind.PILOT,
    resources=ResourceBudget(
        max_workers=2,
        max_concurrent_runs=2,
        estimated_seconds_per_run=0.5,
    ),
    execution_mode=ExecutionMode.SEQUENTIAL,
)
 
runner = default_campaign_runner(workspace)
plan = runner.validate(config)
budget = runner.dry_run(plan)
print(f"planned runs: {len(plan.runs)}")
print(f"estimated wall time: {budget.estimated_wall_time_seconds:.1f}s")
 
index = runner.run(plan)
print(f"completed runs: {index.run_count}")
print(f"failed attempts: {len(index.failures)}")

planned runs: 4
estimated wall time: 2.0s
completed runs: 4
failed attempts: 0

my-campaigns/
  .checkpoints/first-campaign.json
  ENVIRONMENT.md
  logs/first-campaign_<timestamp>.log
  results/first-campaign/
    plan.json
    environment.json
    earliest-start/smoke-job-shop-0/run_0.json
    earliest-start/smoke-workflow-0/run_0.json
    shortest-processing-time/smoke-job-shop-0/run_0.json
    shortest-processing-time/smoke-workflow-0/run_0.json

from pathlib import Path
 
from dispatchatlas.analytica import load_result_dataset, summarize_dataset
 
dataset = load_result_dataset(Path("my-campaigns"), "first-campaign")
print(f"campaign: {dataset.campaign_id}")
print(f"completed runs: {len(dataset.completed)}")
 
summary = summarize_dataset(dataset)
for solver in summary.solver_summaries:
    print(
        f"{solver.solver_id}: runs={solver.count} "
        f"feasible={solver.feasible_count} "
        f"mean {solver.objective_name}={solver.mean:.1f}"
    )

campaign: first-campaign
completed runs: 4
earliest-start: runs=2 feasible=2 mean makespan=182.1
shortest-processing-time: runs=2 feasible=2 mean makespan=222.1

uv run dispatchatlas export `
  --campaign-dir .\my-campaigns\results\first-campaign `
  --target-dir .\exports\first-campaign `
  --authorized-output-root .\exports `
  --tier core