Output Buffer Completion Model¶

JobShopLab has evolved to implement a more realistic completion model that better reflects real-world manufacturing systems. Instead of considering jobs complete when their operations finish, the system now requires jobs to be transported to designated output buffers for true completion.

Overview¶

The Output Buffer Completion Model represents a significant enhancement to JobShopLab’s job completion semantics. This change aligns the simulation with real manufacturing workflows where completed work-in-progress must be delivered to final storage or shipping areas.

Key Concepts¶

Traditional vs. Output Buffer Completion¶

Traditional Model (Previous): - Jobs were considered “done” when all operations reached DONE state - No consideration of material flow to final destinations - Incomplete simulation of manufacturing workflow

Output Buffer Model (Current): - Jobs are “done” only when both conditions are met:

1. All operations are in DONE state

2. Job has been transported to an output buffer

• Ensures complete material flow simulation

• Reflects real-world manufacturing closure

Output Buffer Configuration¶

Output buffers are defined in the instance configuration with the OUTPUT role:

buffers:
  - id: "output-buffer"
    type: "flex_buffer"
    capacity: 999999
    role: "output"  # Designates this as an output buffer
    description: "Final destination for completed jobs"

Job Completion Logic¶

The completion logic is implemented in multiple layers:

1. Core State Machine (core_utils.is_done()):

def is_done(state: State, instance: InstanceConfig) -> bool:
    """Check if all jobs are in output buffers."""
    output_buffer_ids = [b.id for b in get_output_buffers(instance)]
    for job in state.jobs:
        if job.location not in output_buffer_ids:
            return False
    return True

2. Individual Job Completion (job_type_utils.is_done()):

def is_done(job: JobState, instance: InstanceConfig) -> bool:
    """Check if a job is completely done."""
    output_buffer_ids = [b.id for b in instance.buffers
                        if b.role == BufferRoleConfig.OUTPUT]
    return (all_operations_done(job) and
            job.location in output_buffer_ids)

Transport Decision Logic¶

Enhanced Transport Routing¶

The transport system now makes intelligent routing decisions based on job completion status:

# Determine destination based on job's operation completion status
match job_type_utils.no_operation_idle(job_state):
    case True:  # All operations complete - transport to output buffer
        destination = next(iter(get_output_buffers(instance))).id
    case False:  # Operations remain - transport to next operation's machine
        destination = get_next_idle_operation(job_state).machine_id

Job Transportability Rules¶

The is_transportable() function implements a comprehensive decision tree:

1. Jobs at output buffers: Not transportable (fully complete)

2. All operations done, not at output: Transportable to output buffer

3. Operations remaining, not at next machine: Transportable to next machine

4. Operations remaining, at correct machine: Not transportable

Implementation Details¶

New Functions Added¶

Several new utility functions support the output buffer model:

Buffer Utilities:

def get_output_buffers(instance: InstanceConfig) -> tuple[BufferConfig, ...]:
    """Get all output buffer configurations."""
    return tuple(b for b in instance.buffers
                if b.role == BufferRoleConfig.OUTPUT)

Job Status Utilities:

def all_operations_done(job: JobState) -> bool:
    """Check if all operations in a job are in DONE state."""
    return all(op.operation_state_state == OperationStateState.DONE
              for op in job.operations)

def no_operation_idle(job: JobState) -> bool:
    """Check if no operations are in IDLE state."""
    return all(op.operation_state_state != OperationStateState.IDLE
              for op in job.operations)

Transport Logic:

def is_transportable(job_state: JobState, state: State,
                    instance: InstanceConfig) -> bool:
    """Determine if a job needs transportation."""
    # Comprehensive 4-case decision logic for transport needs

API Changes¶

Breaking Changes¶

The following function signatures have changed and require updates:

Before:

# Old signature - only state parameter
def is_done(state: State) -> bool:
    pass

After:

# New signature - requires instance parameter
def is_done(state: State, instance: InstanceConfig) -> bool:
    pass

Affected Functions:

• core_utils.is_done()

• job_type_utils.is_done()

• state.is_done() (in state machine module)

• JobShopLabEnv._is_terminated() (in environment)

Migration Guide¶

To update existing code:

1. Function Calls: Add instance parameter to is_done() calls:

# Old
if is_done(state):
    print("Simulation complete")

# New
if is_done(state, instance):
    print("Simulation complete")

2. Custom Handlers: Update any custom transition handlers that check completion

3. Testing: Update test fixtures to place completed jobs in output buffers:

# Old test fixture
job_state_done = JobState(id="j-1", operations=(done_op,), location="m-1")

# New test fixture
job_state_done = JobState(id="j-1", operations=(done_op,), location="output-buffer")

Benefits¶

Realistic Workflow Modeling¶

• Complete Material Flow: Simulates the entire manufacturing process from input to output

• Resource Utilization: AGVs must transport finished jobs, affecting system capacity

• Bottleneck Analysis: Output buffer capacity can become a constraint

• Logistics Integration: Transport resources are utilized for final delivery

Performance Considerations¶

• Simulation Accuracy: More realistic completion criteria

• Resource Contention: Transport resources compete for final delivery tasks

• Scheduling Complexity: Agents must consider final transport in their decisions

Example Usage¶

Complete Workflow Example¶

from jobshoplab import JobShopLabEnv, load_config

# Load configuration with output buffers
config = load_config("config_with_output_buffers.yaml")
env = JobShopLabEnv(config)

obs, info = env.reset()
done = False

while not done:
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    done = terminated or truncated

# All jobs are now in output buffers
print("All jobs successfully delivered to output buffers!")

Checking Job Completion Status¶

from jobshoplab.utils.state_machine_utils import job_type_utils

# Check if individual job is complete
if job_type_utils.is_done(job_state, instance):
    print(f"Job {job_state.id} is complete and at output buffer")
elif job_type_utils.all_operations_done(job_state):
    print(f"Job {job_state.id} operations done, needs transport to output")
else:
    print(f"Job {job_state.id} still has operations to complete")

Future Enhancements¶

The output buffer completion model enables several future enhancements:

• Multiple Output Types: Different output buffers for different product types

• Quality Control: Inspection stations before final output

• Shipping Integration: Connection to external logistics systems

• Performance Metrics: New KPIs based on complete workflow completion