Queue Draining

Controlled Drainage

notes: Useful near-name for a governed backlog drain, especially from the pilot deferred candidate.

Backlog Draining

notes: Emphasizes accumulated work rather than the queue mechanism.

Controlled Queue Drain

notes: Plain-language wording for the same intervention.

Drain Mode

notes: Operational mode that may instantiate the archetype but should not be drafted separately.

Queue Flush

notes: Only an alias when flushing means governed completion or disposition, not blind deletion.

related_primes: boundedness constraint flow feedback threshold observability order scheduling

slug: flow

components:
Drain Trigger
Backlog Inventory
Admission Pause
Drain Policy
Drain Order Rule
Service Allocation Plan
Validity and Staleness Rule
Disposition Path
Progress Visibility Signal
Completion Criteria
Reentry or Transition Boundary
Exception Path
Stakeholder Notification Policy
Residual Queue Handoff
Safety Cutoff
Post-Drain Review

mechanisms:
Graceful Queue Shutdown
Message Queue Drain
Connection Draining
Backlog Burn-Down
Maintenance Drain
Incident Backlog Cleanup
Appointment Waitlist Clearing
TTL Expiration Sweep
Dead-Letter Queue Processing
Surge Worker Pool
Drain Dashboard

essence_one_line: Reduce accumulated backlog in a controlled order before shutdown, transition, recovery, or normal operation resumes.

compression_statement: When accumulated queued work threatens transition, recovery, or service stability, drain the queue in a controlled order to preserve integrity and reduce backlog without creating new overload.

core_intuition: A queue that has accumulated enough work is not just a line waiting for normal service. It becomes a transitional state that can block shutdown, distort recovery, hide obligations, or overload the system when normal operation resumes. Queue Draining converts that state into a governed process: pause or shape inflow, decide what existing items deserve which treatment, allocate capacity, track progress, and leave drain mode only when the residual state is safe.

canonical_formula: accumulated_queue + inflow_control + drain_policy + disposition_path + completion_criteria -> safe_residual_or_empty_queue

structural_problem: A queue has accumulated enough work that ordinary operation, shutdown, recovery, or transition would be unsafe or misleading without controlled drainage.

trigger_conditions: queued work has accumulated beyond what ordinary service can safely or honestly absorb

trigger_conditions: a shutdown, migration, maintenance window, policy transition, or recovery event requires knowing what will happen to existing queued work

trigger_conditions: new arrivals continue to obscure the existing backlog or make it impossible to reduce

trigger_conditions: the queue contains a mixture of valid, stale, duplicate, risky, urgent, or dependency-constrained items

trigger_conditions: resuming normal operation would release delayed overload downstream

trigger_conditions: stakeholders need an explicit answer about which queued obligations will be completed, deferred, transferred, expired, or rejected

symptoms: queue length falls only temporarily because new arrivals refill it

symptoms: old items remain unresolved even after the apparent incident, surge, or transition has ended

symptoms: operators cannot say which backlog items are still valid, high-risk, duplicated, or abandoned

symptoms: shutdown or migration is delayed because the residual queue state is unknown

symptoms: normal service appears restored while a hidden recovery backlog continues to consume capacity

symptoms: teams repeatedly run informal cleanup pushes without criteria for completion or acceptable residual risk

symptoms: affected actors receive silence, ambiguous status, or abrupt closure instead of governed disposition

root_tension: The system needs to change state or regain control, but existing queued work still represents obligations, dependencies, risks, and claims on capacity. Treating the backlog as ordinary future work lets delayed overload persist; discarding it too casually violates integrity. Queue Draining resolves this by making the backlog itself the object of a controlled transition.

anti_signatures: the queue is operating normally and only needs a standing service-order rule

anti_signatures: the central issue is preventing future accumulation through backlog bounds rather than reducing a current backlog

anti_signatures: incoming demand rate is the only problem and existing backlog does not need special disposition

anti_signatures: work can be safely abandoned with no obligation, risk, stakeholder harm, or transition consequence

anti_signatures: the relevant intervention is restarting flow after an interruption and the backlog has already been cleared or transferred

structural_intervention: Define drain order, admission pause, service allocation, discard/expiration rules, and completion criteria.

logic: Declare the drain context: shutdown, transition, recovery, surge cleanup, migration, waitlist clearing, or backlog stabilization.

logic: Inventory the queued work by count, age, class, validity, risk, dependency, ownership, and service commitment.

logic: Control inflow with an admission pause, intake freeze, routing change, deferral path, or class-specific throttle so the drain can make net progress.

logic: Define the drain policy: what will be completed, skipped, expired, transferred, rejected, escalated, or preserved for later service.

logic: Select the drain order and allocate capacity in a way that fits the drain objective and preserves safety, fairness, legality, and dependency constraints.

logic: Process the backlog while tracking progress, oldest items, exceptions, staleness, residual risk, and downstream effects.

logic: Exit drain mode only when completion criteria are met or a residual queue handoff is explicitly accepted.

logic: Review why the backlog accumulated and whether upstream controls such as bounded backlog, WIP limits, admission policy, or visibility need adjustment.

condition: The drain target cannot shrink while new arrivals continue.

rule: Pause, throttle, redirect, or cleanly defer new arrivals before increasing drain effort.

condition: Queued items may be stale, duplicated, invalid, unsafe, or abandoned.

rule: Revalidate or classify them before processing; do not assume that age alone means they should be served.

condition: The queue must be drained before shutdown, migration, or cutover.

rule: Define a completion threshold and residual handoff before changing state.

condition: Old backlog and urgent new work compete for the same capacity.

rule: Use a service allocation plan that explicitly protects essential ongoing service while dedicating enough capacity to reduce the backlog.

condition: Drain order is disputed.

rule: Choose the order based on the purpose of the drain: safety, dependency, age, deadlines, fairness, ease of completion, or transition-criticality.

condition: Some items cannot be completed during the drain.

rule: Use a visible disposition path such as transfer, deferral, expiration, rejection, reclassification, or escalation.

condition: Drain metrics improve but hard cases remain.

rule: Do not declare completion solely on average queue length; check oldest items, high-risk residuals, neglected classes, and exception queues.

invariant: The queue is not refilled faster than the drain can reduce it.

invariant: Drain order remains compatible with safety, legality, fairness, and dependency constraints.

invariant: Completion criteria are explicit before the drain begins.

invariant: Stale or invalid work is handled through a visible rule.

why_it_matters: Old work often contains obsolete assumptions; serving it blindly can waste capacity or create harm.

outcome: Controlled backlog reduction

description: The queue moves from unsafe accumulation toward a smaller, known, and governable state.

outcome: Safer shutdown, transition, migration, or recovery

outcome: More honest service commitments

description: Queued items receive explicit treatment rather than being allowed to remain indefinitely pending.

outcome: Reduced delayed overload

outcome: Clear residual-risk ownership

description: Any remaining backlog has explicit ownership, next treatment, and visibility.

prose_essence:
Queue Draining is the deliberate reduction and disposition of accumulated queued work. It applies when a queue has become more than an ordinary waiting line: it now blocks shutdown, complicates migration, delays recovery, hides obligations, or threatens to overwhelm normal service when operations resume.

The archetype works by treating the backlog as a transitional object. Instead of hoping ordinary service will eventually absorb it, the system declares a drain context, controls new inflow, classifies the backlog, chooses a drain order, allocates capacity, and exits only when the queue is empty enough or safely handed off.

prose_when_to_use:
Use Queue Draining when accumulated waiting work needs explicit treatment before a state change or safe recovery. The trigger may be a technical deployment, a maintenance window, an incident backlog, a waitlist clearance, an agency cutover, a production shutdown, or a service backlog that has become too stale or large for ordinary handling.

It is especially useful when simply serving the next item is not enough. The backlog may include duplicates, expired requests, high-risk cases, dependency-blocked items, or work that belongs in a new system. A drain makes those distinctions visible and governable.

prose_structural_problem:
A queue can accumulate to the point where normal service assumptions no longer hold. The system may still have work waiting, but it may also need to shut down, migrate, recover, reopen admission, or protect downstream capacity. The backlog creates delayed overload: the problem seems postponed, but it will reappear when the queued work is released or rediscovered.

The structural failure is ambiguity. Which items are still valid? Which must be served first? Which can be expired or transferred? How much new demand can enter while old work is cleared? When is the system safe to change state? Queue Draining answers these questions with a temporary governance structure around the backlog.

prose_intervention_logic:
The intervention begins by declaring a drain mode and deciding what queue or backlog is in scope. The system then inventories queued work, pauses or reshapes inflow, defines a drain policy, allocates service capacity, and processes the backlog according to an explicit order and disposition rule.

A successful drain does not merely reduce a visible number. It ensures that every relevant queued item is completed, transferred, expired, rejected, reclassified, escalated, or preserved under named ownership. The drain ends only when explicit completion criteria are satisfied or when the remaining queue has a safe residual handoff.

prose_key_components:
### Drain Trigger

Defines the condition that moves the queue from ordinary service into a controlled draining mode. The trigger may be shutdown, migration, incident recovery, accumulated backlog age, approaching capacity exhaustion, or a governance decision that normal flow is no longer adequate.

### Backlog Inventory

Makes the queued population legible by count, age, class, owner, dependency, validity, and risk before deciding how to drain it. A drain plan cannot be safe if it treats all accumulated work as interchangeable. Inventory exposes what must be completed, what can wait, what is stale, and what must be rejected or reclassified.

### Admission Pause

Stops, slows, or redirects new arrivals so the drain can reduce the existing queue rather than being refilled faster than it empties. The pause can be complete, class-specific, threshold-based, or replaced by a clean deferral path. Without inflow control, draining becomes ordinary overloaded operation.

### Drain Policy

Specifies the queue-specific rules for what will be served, skipped, expired, deferred, escalated, or preserved during the drain. This is the central component of the archetype. It turns an accumulated backlog into a governed transition rather than an improvised scramble.

### Drain Order Rule

Chooses the sequence in which queued items are handled during the drain. The order can be FIFO, risk-first, oldest-first, deadline-first, dependency-first, smallest-first, class-weighted, or a hybrid. It is a temporary or context-specific queue discipline used for the drain objective.

### Service Allocation Plan

Assigns capacity, workers, windows, automation, or service lanes to the drain while protecting essential ongoing operations. Queue draining often needs dedicated capacity or a scheduled burn-down period. Otherwise the drain competes invisibly with normal work and may never finish.

### Validity and Staleness Rule

Determines whether old queued items are still actionable, valuable, safe, or authorized to process. Not every old item should be served. Some must be revalidated, expired, canceled, or turned into a clean rejection because time has changed the underlying assumptions.

### Disposition Path

Provides explicit outcomes for items that will not be completed during the drain. A backlog is not drained merely by hiding or deleting work. Items need a governed disposition such as complete, defer, expire, reject, reclassify, escalate, or transfer.

### Progress Visibility Signal

Shows drain progress, remaining backlog, age distribution, throughput, exception count, and completion forecast. Visibility prevents false confidence and lets operators know whether the drain is reducing risk, merely moving work around, or creating new hidden queues.

### Completion Criteria

Defines when the drain is finished enough to shut down, transition, reopen admission, resume normal operation, or hand off the remaining work. Without an exit criterion, a drain can become a permanent special mode or can end too early while dangerous residue remains.

### Reentry or Transition Boundary

Connects the drained queue state to the next operating state: shutdown, migration, normal service, recovery, or controlled reentry. The boundary prevents confusion between clearing existing backlog and governing the return of new flow. Queue Draining may precede Controlled Reentry, but it is not the same intervention.

### Exception Path

Allows specific items to bypass the ordinary drain order when risk, dependency, legality, or safety requires it. Exceptions should be visible and auditable so the drain does not become a covert priority contest.

### Stakeholder Notification Policy

Communicates expected delay, disposition, pause, cutoff, or service changes to people affected by the drain. This component matters when queued actors interpret silence as neglect or when rejected/deferred work must be redirected safely.

### Residual Queue Handoff

Transfers remaining valid work to another queue, team, system, schedule, or governance process when full drainage is not necessary or possible. A drain can legitimately end with a governed residual queue if ownership, expectations, and next treatment are explicit.

### Safety Cutoff

Stops the drain when continuing would create unacceptable risk, quality loss, unfairness, or resource depletion. The cutoff protects against the temptation to clear numbers by pushing unsafe throughput or silently discarding valuable work.

### Post-Drain Review

Examines why the backlog accumulated and whether upstream controls should change. A drain treats accumulated work; review prevents the same queue from refilling because the original capacity, admission, or visibility problem remains unresolved.

prose_common_mechanisms:
### Graceful Queue Shutdown

Stop accepting new work, allow selected queued or in-flight items to finish, then close the queue once completion criteria are met. Common in computing, service operations, and maintenance contexts where abrupt shutdown would lose or corrupt queued work. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Message Queue Drain

Let workers consume pending messages under a defined policy before deployment, scaling, migration, or retirement of a processing path. This is a domain mechanism; the archetype is the broader controlled reduction of accumulated queued work. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Connection Draining

Remove a server or service endpoint from new assignment while existing connections or requests finish or time out. It implements Queue Draining at a boundary between traffic assignment and service completion. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Backlog Burn-Down

Dedicate a period, team, cadence, or capacity block to reducing a known backlog toward a target level. The roadmap explicitly marks backlog burn-down as a mechanism or subcase under Queue Draining rather than a separate archetype. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Maintenance Drain

Clear or reduce queued work before maintenance, migration, shutdown, or a service-window transition. Its distinctive feature is the transition trigger; it still relies on the parent drain policy, pause, and completion criteria. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Incident Backlog Cleanup

Classify, prioritize, resolve, expire, or transfer work accumulated during an outage, disruption, surge, or recovery period. The cleanup should not only restore appearances; it should prevent stale or duplicated recovery work from consuming normal capacity. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Appointment Waitlist Clearing

Process a waitlist after capacity changes by confirming validity, sequencing offers, filling slots, and removing stale or unreachable entries. This mechanism appears in healthcare, education, public services, events, and other scheduled-access domains. This is an implementation mechanism for Queue Draining, not the archetype itself.

### TTL Expiration Sweep

Expire or revalidate queued items whose age exceeds a defined time-to-live or validity threshold. Used carefully, it prevents obsolete work from dominating the drain; used carelessly, it becomes disguised load shedding. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Dead-Letter Queue Processing

Move failed or unprocessable queued items into a separate path for inspection, retry, correction, or discard. This supports disposition and exception handling during a drain, especially when ordinary processing repeatedly fails. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Surge Worker Pool

Temporarily add capacity dedicated to the drain so backlog reduction does not starve ongoing essential work. Capacity bursts should be governed by quality and safety constraints rather than by the desire to make the queue count fall. This is an implementation mechanism for Queue Draining, not the archetype itself.

### Drain Dashboard

Tracks queue size, oldest item, drain rate, exceptions, staleness, completion forecast, and residual risk while the drain is active. The dashboard is a supporting mechanism, not the intervention itself; it informs the drain policy and completion decision. This is an implementation mechanism for Queue Draining, not the archetype itself.

prose_failure_modes:
### Drain without inflow control

**Cause:** New arrivals continue entering faster than existing work can be completed or disposed of.

**Mitigation:** Add an admission pause, intake freeze, clean deferral path, or class-specific inflow throttle before declaring a drain.

### Count-clearing disguised as service

**Cause:** Operators delete, close, expire, or reclassify items to make queue size fall without legitimate disposition.

**Mitigation:** Require visible disposition states, audit samples, stakeholder notification where appropriate, and post-drain review.

### Unsafe speedup

**Cause:** Backlog pressure encourages rushed processing that violates quality, legal, clinical, financial, or safety requirements.

**Mitigation:** Define safety cutoffs, minimum quality checks, escalation rules, and capacity limits that override the burn-down target.

### Drain starvation of normal operations

**Cause:** All capacity is diverted to old backlog, leaving urgent new work, essential service, or maintenance unattended.

**Mitigation:** Use protected service lanes, service allocation plans, and explicit tradeoff decisions for ongoing work.

### Wrong drain order

**Cause:** The drain uses FIFO, easiest-first, or visible-complaint order when dependency, risk, age, or fairness should dominate.

**Mitigation:** Choose a drain order rule tied to the purpose of the drain, then monitor exceptions and neglected classes.

### Transition before residue is safe

**Cause:** The system resumes, shuts down, migrates, or reopens admission before remaining queued work is understood.

**Mitigation:** Use completion criteria, residual queue handoff, and a reentry or transition boundary.

### Permanent special mode

**Cause:** Drain mode continues indefinitely because no exit criteria or upstream correction exists.

**Mitigation:** Set a completion threshold, review cadence, and post-drain upstream controls such as bounded backlog, WIP limits, or admission changes.

### Stale-work harm

**Cause:** Old queued items are processed even though their assumptions, permissions, need, or safety status have expired.

**Mitigation:** Apply validity and staleness rules before service; revalidate or expire rather than blindly completing stale items.

prose_neighbor_distinctions:
### Controlled Reentry

Controlled Reentry regulates how flow resumes after interruption; Queue Draining handles existing accumulated backlog before, during, or after the state transition.

### Buffering

Buffering temporarily holds work to absorb mismatch. Queue Draining deliberately empties, transfers, expires, or disposes of accumulated work under a drain policy.

### Load Shedding

Load Shedding discards or rejects demand to protect the system. Queue Draining may shed some stale or unsafe work, but its main pattern is governed backlog reduction.

### Load Leveling / Demand Smoothing

Load leveling reshapes demand over time to prevent peaks. Queue Draining responds to a backlog that already accumulated and needs controlled treatment.

### Bounded Backlog

Bounded Backlog prevents impossible accumulation by setting limits and overflow rules. Queue Draining handles a queue that must be reduced or safely transitioned.

### Queue Discipline Design

Queue Discipline Design chooses a general service-order rule. Queue Draining uses service order as one component of a temporary backlog reduction and exit policy.

### Work-in-Progress Limiting

Work-in-Progress Limiting caps active work to preserve completion. Queue Draining reduces waiting backlog and may use WIP limits to keep the drain from overloading active service.

### Queue Aging and Starvation Prevention

Queue aging prevents indefinite neglect during ordinary queue operation. Queue Draining addresses an accumulated backlog that threatens recovery, shutdown, or transition.

prose_cross_domain_examples:
- **Distributed computing and messaging** — Before retiring a worker pool, operators stop accepting new messages on that path, let workers consume valid queued messages, route failed messages to a dead-letter queue, and exit when no migration-critical messages remain. The intervention is not ordinary buffering; it is controlled reduction of an accumulated queue before a state transition.
- **Cloud infrastructure and load balancers** — A server is removed from new assignment while existing connections finish or time out before the instance is patched or shut down. Connection draining uses admission pause plus completion criteria to avoid dropping in-flight or queued service.
- **Customer support after an outage** — A support organization freezes low-priority intake for a day, segments outage-related tickets, resolves duplicated reports in batches, escalates aged severe cases, and publishes disposition rules for remaining tickets. The backlog is a delayed-overload artifact of the incident and needs a governed cleanup path before normal operation resumes.
- **Public service or permitting backlog** — An agency creates a temporary backlog unit to inventory old applications, request missing information, expire abandoned requests, and clear high-impact cases before switching to a new intake system. The drain links accumulated waiting work to a transition boundary and explicit disposition states.
- **Healthcare scheduling** — A clinic clearing a deferred appointment waitlist confirms which patients still need service, sequences offers by clinical need and waiting age, removes unreachable entries after notification, and measures residual risk. The waitlist cannot simply be served in raw order; stale entries, urgency, and communication shape the drain policy.
- **Manufacturing and maintenance** — Before scheduled downtime, a production line stops releasing new jobs into a constrained station and processes or reroutes queued work until the station can be safely taken offline. The goal is a safe transition from active production into maintenance, not merely higher throughput.

prose_non_examples:
- **A normal FIFO queue with no special backlog condition** — Ordinary service order is Queue Discipline Design. Queue Draining applies when accumulated backlog must be deliberately reduced for recovery, shutdown, transition, or safe resumption.
- **A buffer that temporarily absorbs bursts** — Buffering holds work to decouple mismatch; draining governs how accumulated work is cleared or disposed of.
- **A rate limiter that slows new arrivals** — Rate limiting controls arrival rate. It may support draining, but it does not by itself define how existing backlog is handled.
- **A load-shedding rule that drops excess work immediately** — Load shedding protects capacity by rejecting or discarding work. Queue Draining may include rejection, but only as part of a broader backlog disposition and transition policy.
- **Controlled Reentry after a queue has already been cleared** — Controlled Reentry governs the return of new flow after interruption. Queue Draining governs the accumulated queued work that may need to be reduced before reentry.

prose_tradeoffs:
- **Backlog reduction versus new-demand responsiveness** — Pausing or slowing intake helps clear old work but may disappoint or exclude new demand.
- **Speed versus quality and fairness** — Aggressive draining can clear numbers quickly while increasing errors, missed constraints, or unequal treatment.
- **Completion versus disposition** — Serving every item may be impossible; expiring, transferring, or rejecting some work can be more honest but politically or emotionally difficult.
- **Local drain success versus upstream cause correction** — A successful drain can hide the fact that admission, capacity, priority, or visibility problems caused the backlog.
- **Simple FIFO drain versus risk-sensitive drain** — Simple order is understandable, but complex order may better respect dependencies, deadlines, rights, and safety.

prose_invariants_to_preserve:
- **Existing queued work is explicitly completed, transferred, expired, rejected, or preserved; it is not merely hidden.** A drain that reduces visible counts without accountable disposition creates silent loss and false recovery.
- **The queue is not refilled faster than the drain can reduce it.** Without inflow control, the intervention becomes ordinary overloaded operation with a different name.
- **Drain order remains compatible with safety, legality, fairness, and dependency constraints.** Backlog pressure can tempt systems to choose the easiest items while abandoning urgent, constrained, or high-rights cases.
- **Completion criteria are explicit before the drain begins.** Exit criteria prevent premature reopening, endless special mode, or disagreement about whether transition is safe.
- **Stale or invalid work is handled through a visible rule.** Old work often contains obsolete assumptions; serving it blindly can waste capacity or create harm.

prose_target_outcomes:
- **Controlled backlog reduction** — The queue moves from unsafe accumulation toward a smaller, known, and governable state.
- **Safer shutdown, transition, migration, or recovery** — The system changes state without abandoning, duplicating, corrupting, or misrepresenting queued work.
- **More honest service commitments** — Queued items receive explicit treatment rather than being allowed to remain indefinitely pending.
- **Reduced delayed overload** — Work accumulated during a surge, outage, pause, or transition does not suddenly flood normal operations.
- **Clear residual-risk ownership** — Any remaining backlog has explicit ownership, next treatment, and visibility.

prose_parameter_dimensions:
### Drain Scope

**Tuning question:** Which queue, classes, time window, owners, items, or service states are included in the drain?

**Typical range:** single queue, class-specific subset, whole backlog, dependency-linked subset, or cross-system backlog.

### Inflow Control Level

**Tuning question:** How strongly should new arrivals be paused, throttled, redirected, or admitted while the drain is underway?

**Typical range:** no pause, soft pause, class-specific pause, intake freeze, clean rejection, or alternate-routing path.

### Drain Order Basis

**Tuning question:** What ordering principle should govern the backlog during the drain?

**Typical range:** oldest-first, risk-first, dependency-first, deadline-first, smallest-first, fairness-weighted, or hybrid.

### Capacity Allocation

**Tuning question:** How much capacity is dedicated to the drain versus normal work, safety work, and new demand?

**Typical range:** background trickle, scheduled blocks, dedicated team, surge capacity, automation burst, or full stop-the-line drain.

### Staleness Threshold

**Tuning question:** When does queued work need revalidation, expiration, cancellation, or different treatment because time has changed its value or validity?

**Typical range:** none, age threshold, domain validity window, legal cutoff, dependency expiration, or risk-based revalidation.

### Disposition Granularity

**Tuning question:** How many final states can a queued item receive during the drain?

**Typical range:** complete only, complete/defer, complete/expire/reject, or multi-state disposition with transfer and escalation.

### Completion Threshold

**Tuning question:** What condition is sufficient to leave drain mode?

**Typical range:** zero backlog, safe residual level, no high-risk aged items, all migration-critical items cleared, or explicit residual handoff.

### Exception Tolerance

**Tuning question:** How much bypass, escalation, or manual override is permitted during the drain?

**Typical range:** none, safety-only, dependency-based, leadership-approved, or explicitly quota-limited.

prose_variants_and_near_names:
### Graceful Shutdown Drain

Drain queued or in-flight work before shutting down a service, line, process, or organizational queue. It differs from the parent because the drain is organized around a closure event and an exit condition for safe shutdown. It remains under Queue Draining because it still uses the same components: admission pause, drain policy, service order, disposition, and completion criteria.

### Recovery Backlog Drain

Drain work accumulated during an outage, surge, interruption, or degraded-service period so normal operation is not overwhelmed by delayed load. It differs from the parent because the backlog is a delayed consequence of failure or disruption, so cleanup must account for duplicates, residual risk, and recovery priorities. It remains under Queue Draining because the causal intervention remains controlled treatment of existing queued work.

### Migration or Cutover Drain

Drain, transfer, or explicitly dispose of queued work before moving to a new system, policy, version, team, or processing path. It differs from the parent because the drain is tied to compatibility between old and new states, not only to queue size. It remains under Queue Draining because it still reduces or governs the existing queue through drain policy, disposition path, and completion criteria.

### Stale Backlog Cleanup

Drain a backlog by identifying items whose age has made them invalid, obsolete, unsafe, or in need of revalidation. It differs from the parent because the critical drain decision is whether aged work still deserves service, not merely what order to serve it in. It remains under Queue Draining because the variant remains a way to reduce and dispose of existing backlog during a drain.

### Waitlist Clearance Drain

Clear a human-service waitlist by confirming demand, sequencing offers, filling capacity, and removing stale or unreachable entries. It differs from the parent because the queued entities are people or access claims, so notification, confirmation, and fairness are central. It remains under Queue Draining because it still reduces a waiting set through a governed drain order and disposition path.

Near names that should point to this archetype or one of its variants include: Controlled Drainage, Queue Draining or Controlled Drainage, Backlog Draining, Drain Mode, Queue Flush. The names are useful for retrieval but should not automatically become separate archetypes.

Graceful Shutdown Drain

distinctive_feature: The drain is organized around a closure event and an exit condition for safe shutdown.

distinct_from_parent: The parent can drain for recovery or ongoing backlog reduction; this variant specifically drains to close or retire a service path.

still_same_parent_because: It still uses the same components: admission pause, drain policy, service order, disposition, and completion criteria.

use_when: abrupt shutdown would drop, corrupt, duplicate, or strand queued work

use_when: the system can stop or redirect new arrivals while existing work completes

use_when: there is a clear shutdown, retirement, or maintenance boundary

example_domains:
distributed systems
manufacturing maintenance
service operations

failure_modes: ending service before residual queued work is safe

Recovery Backlog Drain

distinctive_feature: The backlog is a delayed consequence of failure or disruption, so cleanup must account for duplicates, residual risk, and recovery priorities.

distinct_from_parent: The parent includes all controlled drains; this variant is incident- and recovery-centered.

still_same_parent_because: The causal intervention remains controlled treatment of existing queued work.

source_primes: queueing feedback resource_management

use_when: a disruption created a backlog that normal service cannot absorb safely

use_when: some queued items may be duplicated, stale, or misclassified after the incident

use_when: recovery requires both backlog reduction and restoration of normal controls

example_domains:
customer support
public services
IT operations
claims processing

failure_modes: declaring recovery while aged or high-risk residual cases remain

failure_modes: closing duplicate reports without preserving the real underlying issue

Migration or Cutover Drain

distinctive_feature: The drain is tied to compatibility between old and new states, not only to queue size.

distinct_from_parent: It emphasizes migration boundary and residual handoff while the parent covers any controlled backlog reduction.

still_same_parent_because: It still reduces or governs the existing queue through drain policy, disposition path, and completion criteria.

use_when: queued work exists in an old regime that will not remain fully operational

use_when: items cannot simply be copied forward without duplication, invalid assumptions, or loss of context

use_when: transition success depends on knowing what backlog remains

example_domains:
software migration
agency intake reform
workflow redesign
records processing

failure_modes: duplicating work across old and new systems

failure_modes: losing queued obligations during cutover

Stale Backlog Cleanup

distinctive_feature: The critical drain decision is whether aged work still deserves service, not merely what order to serve it in.

distinct_from_parent: The parent drains any accumulated queue; this variant focuses on staleness and validity as the main decision basis.

still_same_parent_because: The variant remains a way to reduce and dispose of existing backlog during a drain.

source_primes: queueing threshold

use_when: old queued items may no longer represent real demand or valid work

use_when: processing stale items would waste capacity or create harm

use_when: expiration or revalidation must be governed rather than arbitrary

example_domains:
ticketing systems
appointments
permits
queued data processing

failure_modes: expiring valid obligations because they are inconvenient

failure_modes: serving obsolete work without revalidation

Waitlist Clearance Drain

distinctive_feature: The queued entities are people or access claims, so notification, confirmation, and fairness are central.

distinct_from_parent: It is a domain-shaped variant of backlog draining for reserved or access-mediated service.

still_same_parent_because: It still reduces a waiting set through a governed drain order and disposition path.

source_primes: queueing scheduling resource_management

use_when: actors hold positions in a waitlist rather than merely submitting work items

use_when: capacity opens after a delay, cancellation, expansion, or policy change

use_when: fairness, communication, and no-show handling shape the drain

example_domains:
healthcare
education
events
public services

failure_modes: filling slots quickly while violating priority or eligibility rules

near_names:
Controlled Drainage
Queue Draining or Controlled Drainage
Backlog Draining
Drain Mode
Queue Flush


Queue Partitioning

type: archetype

source: generated

batch: First Iteration/Batch 06/Wave 2

aliases:
queue splitting
lane partitioning
multi-class queue design
separate service queues
service lane partitioning

related_primes: boundary order blocking flow modularity constraint accountability

slug: queueing

slug: stratification

slug: boundary

slug: order

slug: blocking

slug: modularity

slug: constraint

slug: accountability

components:
Partition Basis
Classification Rule
Queue Partition Rule
Lane Service Policy
Capacity Allocation Model
Cross-Partition Fairness Policy
Partition Visibility
Overflow and Transfer Rule
Shared Invariants
Partition Review Cadence
Specialist Lane Owner
Self-Selection Guardrail
Merge-Back Rule
Partition Simulation or Load Test

mechanisms:
Multi-Class Queue
Priority Lane
Express Lane
Specialist Queue
Exception Queue
Service-Type Queue
Tenant or Segment Queue
Dedicated Worker Pool
Overflow Lane
Triage Router

essence_one_line: Split a shared queue into governed lanes so different classes of waiting work receive appropriate service without blocking or distorting one another.

compression_statement: When unlike items wait in one queue despite different service needs, risks, durations, or capacity paths, partition the queue into distinct lanes with explicit classification, service, capacity, visibility, and fairness rules; this reduces interference and service mismatch at the cost of fragmentation, routing burden, and cross-lane fairness complexity.

core_intuition: A single line is fair and simple only when waiting items are similar enough to be served by the same rule and capacity path. When the queue mixes emergencies with routine cases, quick requests with long cases, specialist work with general work, or blocked exceptions with normal flow, the line becomes a source of interference. Partitioning makes relevant differences operationally visible, but it must preserve shared accountability so the separated lanes do not become hidden backlogs or privileged shortcuts.

canonical_formula: shared_waiting_set + meaningful_difference_in_service_need -> classified_lanes + lane_service_policies + cross_lane_invariants

structural_problem: Different classes of work, actors, or requests wait in the same queue even though they require different service paths, durations, priorities, risks, capabilities, or fairness treatment. The shared queue causes avoidable delay, head-of-line blocking, misrouting, overloaded generalists, hidden class-specific backlog, or unjust cross-class interference.

trigger_conditions: a single queue contains items with materially different service requirements or service times

trigger_conditions: one class of item repeatedly delays or crowds out another class

trigger_conditions: urgent or safety-sensitive items can be buried in routine backlog

trigger_conditions: simple work waits behind complex work even though it could be completed quickly

trigger_conditions: specialized work is repeatedly misrouted to general service paths

trigger_conditions: exceptions, blocked items, or incomplete cases stall routine flow

trigger_conditions: aggregate queue metrics hide class-specific service failures

symptoms: long waits for items that require short service time

symptoms: specialist queues emerging informally without ownership or visibility

symptoms: frontline workers manually cherry-picking items because the shared queue is too heterogeneous

symptoms: repeated reclassification or handoff after items have already waited

symptoms: routine work and urgent work both receiving inappropriate treatment

symptoms: some groups moving quickly while other groups wait invisibly

symptoms: backlog reports that look acceptable overall while one work class is failing

root_tension: Simplicity and apparent equality of one shared queue conflict with the need to account for meaningful differences in service need, risk, duration, ownership, and capacity path.

anti_signatures: all queued items are materially similar and can be served well by one queue discipline

anti_signatures: the problem is only too much total demand, not interference among work classes

anti_signatures: the system lacks reliable information to classify items without severe misrouting

anti_signatures: partitioning would create discriminatory or stigmatizing lanes without a defensible service reason

anti_signatures: capacity is fully interchangeable and load balancing would solve the issue without creating class lanes

anti_signatures: the main need is to decide who may enter at all rather than how admitted/waiting work is separated

structural_intervention: Define the meaningful classes inside a shared waiting set, split the queue into governed partitions, route items into the right lane, assign lane-specific service policies and capacity, preserve lane-level visibility, and maintain shared invariants across the partitioned system.

action_logic: identify where a shared queue is mixing unlike work in ways that create delay, blocking, misrouting, unfairness, or service mismatch

action_logic: choose a partition basis that corresponds to real differences in service need, risk, duration, capacity path, or accountability

action_logic: define classification rules for assigning items to lanes and correcting misclassification

action_logic: create distinct queues or lanes with explicit service policies, ownership, and capacity allocation

action_logic: instrument each lane so backlog size, age, priority mix, and service risk remain visible

action_logic: define overflow, transfer, merge-back, and exception rules so lanes do not become rigid silos

action_logic: review cross-lane fairness and adjust partitions when demand, capacity, or service obligations change

decision_rules: partition only when the benefits of service fit or reduced interference exceed the cost of added routing complexity

decision_rules: do not create a lane unless it has a clear service policy, owner, capacity path, and visibility rule

decision_rules: allow lane differences only when they are tied to defensible differences in need, risk, service standard, or resource path

decision_rules: monitor both lane-level performance and system-level invariants; neither aggregate success nor local success is sufficient alone

decision_rules: use transfer or overflow rules when a partition is overloaded, empty, misclassified, or unable to meet minimum service guarantees

decision_rules: retire or merge lanes that no longer correspond to real differences in demand or service need

target_invariants: no queue partition becomes invisible, ownerless, or exempt from service accountability

target_invariants: classification criteria remain explicit, contestable, and auditable

target_invariants: lane differences do not produce unjustified exclusion, stigma, or indefinite waiting

target_invariants: every item has a valid lane, transfer path, or clean rejection path

target_invariants: urgent or safety-sensitive items are not buried in routine flow

target_invariants: routine or low-status lanes are not starved by protected or privileged lanes

target_invariants: the partition structure remains simpler than the problem it solves

expected_outcomes: reduced interference among unlike work classes

expected_outcomes: less head-of-line blocking from slow, blocked, or complex items

expected_outcomes: better matching between work type and service capability

expected_outcomes: more accurate visibility into lane-specific backlog, wait, and service risk

expected_outcomes: clearer ownership for specialized or exception work

expected_outcomes: improved perceived and substantive fairness when lane differences are justified and transparent to the appropriate audience

expected_outcomes: faster completion for work classes that were previously delayed by inappropriate mixing

prose_essence:
Queue Partitioning is the design move of splitting one shared waiting line into multiple governed queues because the items in that line are not actually interchangeable. The archetype applies when the shared queue mixes work with different service needs, risks, durations, capabilities, or ownership paths, and that mixture creates interference.

The point is not simply to create more lines. The point is to make service-relevant differences operational: each lane has a reason to exist, a classification rule, a service policy, a capacity path, lane-level visibility, and fairness safeguards across lanes.

prose_when_to_use:
Use Queue Partitioning when a common queue is creating avoidable harm because unlike work is forced through the same waiting structure. Typical signs include urgent work buried behind routine work, simple work stuck behind complex work, specialist work bouncing through generalist queues, blocked exceptions holding up normal flow, or aggregate backlog numbers hiding a failing work class.

It is especially useful when one queue discipline cannot satisfy all classes at once. A FIFO rule may be fair among similar items, but unfair or unsafe when emergencies, routine cases, quick tasks, and complex tasks all share the same line. Partitioning lets those classes receive different service policies while preserving system-wide accountability.

prose_structural_problem:
The structural problem is mixed waiting under scarce or specialized capacity. A single queue treats items as if they can be served by the same rule and the same capacity path, even when they cannot. The result is service mismatch: some work waits behind the wrong kind of work, some handlers receive items they are not equipped to serve, and some categories of need become invisible inside the aggregate backlog.

This can produce head-of-line blocking, misrouting, fairness disputes, manual cherry-picking, hidden starvation, and poor service-level control. The queue appears simple, but its simplicity is false because it conceals meaningful differences.

prose_intervention_logic:
The intervention begins by identifying the relevant difference inside the queue: class, urgency, risk, duration, complexity, resource path, tenant, region, service type, or exception status. The system then creates explicit lanes around that difference and routes items into them through a classification rule.

Each lane needs more than a label. It needs a service policy, capacity allocation, ownership, backlog visibility, overflow paths, and shared invariants. Otherwise partitioning just turns one unmanaged queue into several unmanaged queues.

The archetype works by reducing cross-class interference. Routine items no longer hide urgent items. Quick items no longer wait behind long cases. Specialist work no longer cycles through general queues. Exceptions no longer stall routine flow. But the system must also monitor whether the new lanes are creating unfair fast paths, neglected slow paths, or obsolete fragments.

prose_key_components:
### Partition Basis

**Role:** Defines the criterion used to split one shared waiting set into multiple queues, such as service type, risk, urgency, resource path, customer class, tenant, geography, or complexity.

The partition basis is the causal heart of this archetype. A queue split is useful only when the chosen categories correspond to different service needs, blocking risks, capabilities, fairness constraints, or capacity paths. Arbitrary partitioning fragments visibility without solving interference.

### Classification Rule

**Role:** Assigns each arriving or waiting item to the correct queue according to the partition basis.

The rule may be manual, algorithmic, self-selected, triaged, or derived from metadata. It should be auditable because misclassification can create unfair delay, gaming, safety risk, or a disguised form of exclusion.

### Queue Partition Rule

**Role:** Specifies how many queues exist, where the split occurs, whether items may move between partitions, and how partitions are created, merged, or retired.

The roadmap identifies queue_partition_rule as a likely component. It turns the abstract split into a governed structure: distinct queues, lane boundaries, cross-over rules, consolidation rules, and conditions for changing the partition design.

### Lane Service Policy

**Role:** Defines how each partitioned queue is served, including its discipline, staffing, capacity share, service standard, escalation logic, and exception handling.

Partitioning alone does not solve interference unless each lane receives service rules suited to its class. The service policy prevents split queues from becoming labels with no operational consequence.

### Capacity Allocation Model

**Role:** Determines how service capacity, staff time, worker pools, servers, appointment slots, or processing attention are allocated across partitions.

A partition can starve if it has a dedicated queue but no capacity. The allocation model may reserve capacity, share capacity dynamically, dedicate specialized workers, or rebalance lanes when demand changes.

### Cross-Partition Fairness Policy

**Role:** Preserves fair treatment across queues by defining acceptable differences in wait, access, service order, burden, and outcome among partitions.

Queue partitioning often creates visible inequality because some lanes move faster than others. The fairness policy distinguishes justified service fit from unjustified privilege, neglect, or discriminatory segmentation.

### Partition Visibility

**Role:** Makes each queue visible by size, age, mix, capacity, and service-level risk so partitioning does not hide backlog inside smaller lanes.

A partitioned queue system can look healthy in aggregate while one lane collapses. Visibility must preserve lane-level facts while still supporting system-wide decisions.

### Overflow and Transfer Rule

**Role:** Defines what happens when a partition exceeds capacity, becomes empty, receives misrouted items, or requires help from another queue.

This component protects the system from rigid lanes. It may allow temporary pooling, escalation, rerouting, overflow to a generalist lane, controlled bypass, or return to intake for reclassification.

### Shared Invariants

**Role:** States the system-wide promises that remain true despite partitioning, such as no indefinite waiting, no unowned items, minimum safety checks, auditability, and eventual service or clean rejection.

Partitioning should not let every lane become its own unaccountable world. Shared invariants keep the split compatible with overall service integrity and organizational responsibility.

### Partition Review Cadence

**Role:** Schedules review of whether the current partition structure still matches demand, capacity, service needs, and fairness constraints.

Queue partitions can become obsolete as demand changes. A review cadence catches lanes that have become empty, overloaded, gamed, stigmatizing, redundant, or too complex to operate.

Optional components include Specialist Lane Owner, Self-Selection Guardrail, Merge-Back Rule, and Partition Simulation or Load Test. These are useful when lanes require special expertise, user choice can be gamed, temporary partitions must recombine, or the partition structure should be tested before adoption.

prose_common_mechanisms:
Mechanisms implement Queue Partitioning, but they are not the archetype by themselves. The archetype is the full design pattern: partition basis, classification, lane service policy, capacity allocation, visibility, fairness, overflow, and review.

### Multi-Class Queue

**Mechanism type:** queue_structure

**Role:** Maintains separate waiting lines for different classes of work or actors, with class-specific rules and service targets.

A multi-class queue is a common implementation mechanism. It becomes Queue Partitioning only when the class split is intentionally used to reduce interference, align service fit, or preserve cross-class fairness.

### Priority Lane

**Mechanism type:** lane_design

**Role:** Creates a distinct queue for urgent, high-risk, high-value, or time-sensitive work that should not wait behind routine items.

Priority lanes are merge-sensitive with Priority-Based Admission. In this archetype the lane is about separating waiting structures and service paths, not merely deciding who is admitted or ranked.

### Express Lane

**Mechanism type:** service_lane

**Role:** Separates quick, simple, or low-complexity items so they can be served without waiting behind long or complex items.

Express lanes can relieve mixed-service interference, but can also create unfairness if complex cases are neglected or if the simplicity criterion is gamed.

### Specialist Queue

**Mechanism type:** specialized_queue

**Role:** Routes items requiring specialized skill, authorization, equipment, or risk review into a distinct service path.

Specialist queues implement partitioning when the service path is materially different from the general queue. They can fail when specialist capacity is too scarce or ownership is unclear.

### Exception Queue

**Mechanism type:** exception_handling_structure

**Role:** Separates blocked, anomalous, incomplete, stale, or disputed items so routine flow can continue while exceptions receive appropriate handling.

Exception queues support partitioning and head-of-line blocking relief, but they should not become dumping grounds for difficult work.

### Service-Type Queue

**Mechanism type:** classification_queue

**Role:** Splits waiting work by the type of service required, such as billing, technical support, clinical triage, permit review, or repair category.

This mechanism aligns queue membership with staff, tools, procedures, and service standards rather than treating all waiting work as interchangeable.

### Tenant or Segment Queue

**Mechanism type:** segment_queue

**Role:** Creates separate queues for tenants, accounts, regions, populations, or actor groups with distinct service obligations or constraints.

Segment queues can preserve accountability or contractual commitments, but they require strong fairness and anti-discrimination review.

### Dedicated Worker Pool

**Mechanism type:** capacity_assignment

**Role:** Assigns specific servers, staff, processors, or teams to a partitioned queue.

Dedicated capacity makes partitions real, but can reduce flexibility when demand shifts. Dynamic borrowing rules may be needed.

### Overflow Lane

**Mechanism type:** overflow_handling

**Role:** Moves items or capacity across lane boundaries when a partition exceeds its tolerable backlog or wait-time threshold.

Overflow lanes soften rigid partitioning. They must be governed so overflow does not erase the original purpose of the partition.

### Triage Router

**Mechanism type:** routing_mechanism

**Role:** Classifies and routes incoming items to the correct queue before they join an inappropriate waiting line.

A triage router can also instantiate Intake Queue Staging. For Queue Partitioning, its role is to maintain correct lane membership.

prose_failure_modes:
Common failure modes include arbitrary partitioning, hidden lane starvation, misclassification, privileged fast lanes, rigid siloing, overpartitioning, and aggregate metric masking. These failures usually occur when lanes are created without capacity, visibility, ownership, transfer rules, or fairness review.

A particularly dangerous failure is the exception dump: hard cases are moved out of the main queue so routine flow looks better, but no one owns or resolves the exception lane. Another is the premium fast lane: a queue split is presented as operational necessity while actually allocating faster service to a favored group without defensible criteria.

prose_neighbor_distinctions:
Queue Partitioning is distinct from Queue Discipline Design because it changes the queue structure rather than only the service-order rule inside one queue. It is distinct from Priority-Based Admission because it is not primarily about who gets in or who ranks first; it is about maintaining separate waiting structures and lane policies. It is distinct from Stratified Treatment because the category distinction is specifically embodied in queue structure. It is distinct from Load Balancing because the work is not necessarily interchangeable across servers. It is distinct from Bulkhead Isolation because the primary aim is reducing waiting-line interference and service mismatch, not failure containment alone.

It composes naturally with Backlog Visibility, Queue Discipline Design, Bounded Backlog, and Queue Aging and Starvation Prevention. A partitioned system often needs visibility per lane, discipline within each lane, caps on lane backlogs, and aging rules to prevent slow-lane starvation.

prose_cross_domain_examples: In healthcare, emergency departments partition queues by triage acuity so high-risk patients do not wait behind routine cases. In customer support, tickets are separated into billing, technical, account-security, and escalation queues so each receives the right expertise and service standard. In software systems, message queues are partitioned into routine jobs, high-priority events, slow batch jobs, and dead-letter exceptions so one class does not block another. In public permitting, simple renewals, complex reviews, safety inspections, and incomplete applications can use different lanes. In retail or public-service counters, express and complex-service lanes can reduce interference when they are backed by fairness safeguards.

prose_non_examples: A single FIFO queue with homogeneous work is not Queue Partitioning. A dashboard that shows backlog state is Backlog Visibility unless it changes the queue structure. A rule that rejects excess demand is Bounded Backlog or Load Shedding unless it creates separate waiting lanes. A VIP skip-the-line policy is Priority-Based Admission unless it includes governed lane structure. Distributing identical jobs across identical servers is Load Balancing rather than Queue Partitioning.

prose_tradeoffs:
Partitioning buys service fit by spending complexity. More lanes mean more routing decisions, more monitoring, and more boundary disputes. Dedicated lanes can protect urgent or specialist work, but they can reduce flexibility when demand shifts. Express lanes can speed simple work, but may leave complex work in a slower and more stigmatized lane. Priority lanes can protect high-risk items, but may create unfair or opaque privilege if criteria are weak.

The design challenge is to split enough to reduce interference, but not so much that the service system becomes fragmented and hard to govern.

prose_invariants_to_preserve: Queue Partitioning should preserve several invariants. Every item should have a valid lane, owner, service policy, and correction path. No lane should become invisible or exempt from backlog review. Lane differences should be justified by service need, risk, capacity path, or fairness logic rather than status or convenience alone. The system should prevent indefinite waiting in low-priority or low-status lanes. Classification should be auditable, and lane structure should remain simpler than the problem it solves.

prose_target_outcomes: The archetype aims to reduce interference among unlike work classes, improve fit between work and service capacity, reduce avoidable head-of-line blocking, make lane-specific backlog visible, and clarify ownership for specialized or exception work. It can improve throughput, safety, fairness, and predictability when the original queue was mixing work that should not be governed by one rule.

prose_parameter_dimensions:
Important tuning dimensions include the number of lanes, the partition basis, the strictness of classification, the amount of dedicated versus shared capacity, the threshold for overflow, the allowed transfer paths between lanes, and the review cadence for changing the partition design.

Other parameters include per-lane backlog limits, maximum wait times, service-level targets, escalation thresholds, self-selection rules, degree of transparency to waiting actors, and whether lanes are permanent, temporary, dynamic, or event-triggered. The best settings depend on how costly misclassification is, how different the service paths are, and how quickly demand shifts.

prose_variants_and_near_names:
Recognized variants include class-based queue partitioning, service-type queue partitioning, complexity or duration lane partitioning, risk or urgency lane partitioning, and exception queue partitioning. Near names include multi-class queues, priority lanes, express lanes, emergency versus routine queues, issue-specific support queues, and lane splitting.

These names should not all become standalone archetypes. Most are variants or mechanisms under Queue Partitioning unless the causal center shifts. Head-of-Line Blocking Relief remains a separate likely second-wave candidate because it can use bypass or resequencing without a durable partition. Intake Queue Staging remains separate when the central intervention is pre-admission classification and commitment control. Queue Reservation remains separate when the central intervention is preserving position without continuous waiting.

raw_bundle_path: solution_archetypes/_Batches/First Iteration/Batch 06/Wave 2/queue_partitioning_solution_archetype_output_bundle.zip

Class-Based Queue Partitioning

variant_type: subtype

short_definition: Splits a shared queue by item, actor, risk, or service class so each class can receive fitting treatment without direct interference from unlike classes.

distinctive_feature: The partition criterion is a class or membership distinction, not simply a capacity or server assignment.

distinct_from_parent: It is the class/membership-centered subtype of the broader Queue Partitioning archetype.

still_same_parent_because: It still depends on maintaining multiple waiting lines and governing their service policies.

use_when: different classes have materially different service needs or standards

use_when: a shared queue hides class-specific delay or backlog age

use_when: one class repeatedly blocks or crowds out another class

Partition Basis

role: defines class distinction

Classification Rule

role: assigns items to classes

Cross-Partition Fairness Policy

role: prevents unjust class effects

Multi-Class Queue

role: maintains class-specific waiting lines

Tenant or Segment Queue

role: separates actor segments

Service-Type Queue Partitioning

short_definition: Splits a queue by the kind of service required so each lane connects to the right people, tools, procedures, or capacity path.

distinctive_feature: The lanes map to different service capabilities rather than only different priorities.

distinct_from_parent: It emphasizes matching work to service paths; the parent also includes risk, actor, urgency, or exception partitioning.

still_same_parent_because: The solution remains a governed split of a waiting set into separate queues.

source_primes: queueing resource_management boundary

use_when: work types require different expertise, tools, permissions, or processing times

use_when: general queues repeatedly misroute items or create avoidable handoffs

use_when: service mismatch is more important than strict global arrival order

Lane Service Policy

role: defines service per queue

Specialist Lane Owner

role: maintains ownership

Service-Type Queue

role: routes by service type

Specialist Queue

role: handles specialized work

name: Complexity or Duration Lane Partitioning

short_definition: Splits quick/simple items from long/complex items so one class does not impose avoidable delay on the other.

distinctive_feature: The partition basis is expected service duration, complexity, or blocking likelihood.

distinct_from_parent: It is a specific queue-partitioning strategy aimed at service-time mismatch and head-of-line effects.

still_same_parent_because: The solution is still a set of governed lanes with class-specific service policies.

source_primes: queueing blocking flow

use_when: short tasks wait behind long tasks despite requiring little service time

use_when: long or complex cases need protected attention rather than constant interruption

use_when: average service time hides a bimodal or highly skewed workload

Classification Rule

role: estimates simplicity or complexity

Overflow and Transfer Rule

role: handles misclassified or complex items

Express Lane

role: serves short/simple items

Exception Queue

role: isolates cases that cannot proceed normally

Risk or Urgency Lane Partitioning

short_definition: Separates urgent, high-risk, or safety-sensitive items into a queue with different service guarantees and escalation rules.

distinctive_feature: The partition basis is risk or urgency, and the lane usually has stricter service standards.

distinct_from_parent: It is the risk-sensitive subtype of the broader partitioning pattern.

still_same_parent_because: It still separates waiting work into distinct queues rather than merely assigning priority weights inside one queue.

use_when: routine work can safely wait but urgent work cannot

use_when: high-risk items must not be buried in a large general backlog

use_when: the system needs auditable protection for urgent classes while preserving fair treatment of routine classes

Cross-Partition Fairness Policy

role: balances urgent and routine lanes

Capacity Allocation Model

role: protects risk-sensitive capacity

Priority Lane

role: separates urgent work

Triage Router

role: assigns risk category

Exception Queue Partitioning

short_definition: Moves blocked, anomalous, incomplete, stale, or disputed items into a distinct queue so routine flow can continue while exceptions receive governed handling.

distinctive_feature: The partition is based on abnormal handling needs rather than ordinary class membership.

distinct_from_parent: It is the exception-handling subtype of Queue Partitioning.

still_same_parent_because: It still relies on a distinct waiting line and service policy for a separated class of queued items.

source_primes: queueing blocking boundary

use_when: exception items repeatedly stall routine work

use_when: blocked items require different authority, information, or review

use_when: routine workers need a clean path without silently abandoning exceptions

Overflow and Transfer Rule

role: moves exceptions between lanes

Shared Invariants

role: ensures exceptions are not abandoned

Exception Queue

role: holds abnormal cases

Specialist Queue

role: routes exceptions to capable handlers

near_names:
Multi-Class Queue
Separate Queues by Service Type
Priority Lanes
Express Lanes
Emergency vs Routine Queues
Issue-Specific Support Queues
Lane Splitting


Queue Reservation

Queue Place Reservation

notes: Emphasizes the preservation of place rather than the mechanism used to hold it.

Virtual Queueing

notes: Common mechanism-centered name; narrower than the full archetype because reservations may be physical tokens, callbacks, or appointments rather than digital queues.

Appointment Queueing

notes: Temporal reservation variant; merge-sensitive with Scheduling and Load Leveling / Demand Smoothing.

Callback Reservation

notes: Communication-centered variant where a turn is activated by recontact rather than physical presence.

Numbered Ticket Queue

notes: Tokenized implementation; should remain mechanism or variant unless full reservation governance is described.

source_primes: queueing scheduling representation

slug: scheduling

slug: representation

slug: observability

slug: virtualization

slug: feedback

components:
Reservation Token
Position or Slot Record
Eligibility and Claim Rule
Service Window or Callback Policy
Notification Policy
No-Show Policy
Release and Reassignment Rule
Fairness and Access Policy
Standby or Waitlist Policy
Capacity Holdback Rule
Identity Verification Rule
Reservation Status View
Rescheduling Path

mechanisms:
Appointment System
Virtual Queue
Numbered Ticket
Callback Queue
Timed Entry
Online Booking Portal
Standby List
Reminder and Confirmation Sequence

essence_one_line: Reserve positions, slots, or service opportunities so actors can preserve access and order without physically or continuously waiting.

compression_statement: When continuous waiting consumes time, space, attention, dignity, or capacity, convert open waiting into a governed reservation claim that preserves place, slot, or service opportunity while defining return, no-show, and release rules.

core_intuition: A queue does not always need bodies, open calls, live sessions, or active items occupying the waiting channel. If the system can safely represent place or access, it can let people or work wait elsewhere while still preserving order and accountability. Queue Reservation turns waiting from a continuous occupation into a durable claim, reducing burden and congestion while adding governance responsibilities around eligibility, notification, no-shows, and fairness.

canonical_formula: continuous_waiting_cost + scarce_service_channel -> reservation_token_or_slot + claim_rule + notification_return_policy + no_show_release_rule

structural_problem: Physical, synchronous, or continuous waiting imposes unnecessary cost, congestion, exclusion, lost productive time, uncertainty, or service-channel occupancy even though the system could preserve order through a durable representation of place or service opportunity.

trigger_conditions: actors must wait in person, on hold, logged in, watching a screen, or keeping work active to preserve access

trigger_conditions: the waiting environment is crowded, unsafe, expensive, stigmatizing, stressful, or operationally disruptive

trigger_conditions: service capacity is scarce enough that unrestricted arrival creates long lines but predictable enough that positions or windows can be represented

trigger_conditions: actors who cannot physically wait are disadvantaged even when their claims are otherwise legitimate

trigger_conditions: open waiting causes abandonment, repeated inquiries, conflict over place, or hidden inequity in who can afford to wait

trigger_conditions: the queue needs to preserve order or access, but not continuous physical or synchronous presence

symptoms: people spend excessive time in lobbies, phone holds, web refresh loops, parking lots, or service counters before meaningful service begins

symptoms: a physical line creates congestion, conflict, exposure, accessibility problems, or a visible backlog that overwhelms the service setting

symptoms: waiting actors repeatedly ask for status because they have no reliable claim record or return instruction

symptoms: those with jobs, caregiving duties, transportation limits, disabilities, or weak connectivity lose access because they cannot continuously wait

symptoms: service capacity is wasted when people disappear, miss their turn, duplicate bookings, or cannot be reconnected at the right time

symptoms: operators improvise informal lists, sticky notes, callbacks, or promises that are not auditable or fair

root_tension: The system must preserve service order and access under scarcity, but making actors continuously occupy the queue transfers system constraints onto their bodies, time, attention, or local environment. Reservation reduces that burden, but it also creates new risks: hoarding, no-shows, inaccessible booking channels, inequitable slot capture, and false promises about service availability.

anti_signatures: continuous presence is genuinely required for safety, readiness, identity verification, custody, or real-time mutual adjustment

anti_signatures: the central problem is deciding who deserves scarce service rather than reducing the burden of waiting; Priority-Based Admission may be the better pattern

anti_signatures: the central problem is smoothing demand over a calendar rather than preserving queue position; Load Leveling / Demand Smoothing may be the better pattern

anti_signatures: the central problem is protecting capacity for future surge or priority classes rather than reserving a queue position for current waiters; Capacity Reservation may be the better pattern

anti_signatures: the central problem is communicating expected wait or position while actors still wait continuously; Queue Transparency and Expectation Setting may be only a component

anti_signatures: the system lacks reliable identity, notification, or release mechanisms and would create more lost turns than saved waiting time

structural_intervention: Replace continuous waiting with a governed reservation layer: issue a durable token, place, slot, callback, or access claim; define eligibility and claim validity; notify actors when service is approaching; handle no-shows and cancellations; and release unused capacity without corrupting queue order or fairness.

logic: Identify what waiting burden should be reduced: physical presence, live hold time, channel occupancy, uncertainty, travel, crowding, or active work retention.

logic: Define exactly what is being reserved: ordinal queue position, time slot, callback turn, service capacity window, access credential, or conditional standby claim.

logic: Create a durable reservation record that links the actor or item to its claim, status, timestamp, eligibility, and next action.

logic: Specify how reservations are obtained, confirmed, changed, transferred, cancelled, expired, or revoked.

logic: Provide notifications, status views, reminders, or return instructions so the reservation holder can wait elsewhere without losing coordination.

logic: Define no-show, late-arrival, missed-callback, duplicate-booking, and unclaimed-slot rules that protect capacity without creating unfair exclusion.

logic: Release or reassign unused reserved capacity through transparent standby, waitlist, or reentry rules.

logic: Monitor waiting burden, no-shows, slot capture, access inequity, utilization, delay, and complaints; tune reservation rules accordingly.

decision_rules: reserve only what the system can reasonably honor, and distinguish between guaranteed, approximate, conditional, and standby claims

decision_rules: do not let reservation tokens become invisible priority privileges unless priority rules are explicit and reviewable

decision_rules: preserve queue order or service access unless a documented exception, urgency rule, or fairness policy justifies deviation

decision_rules: provide an accessible path for people who cannot use the dominant reservation channel or cannot respond immediately to notifications

decision_rules: make cancellation, late arrival, no-show, and rescheduling consequences clear before the service opportunity is lost

decision_rules: hold enough walk-in, urgent, or accessible capacity when pure reservation would exclude legitimate demand

decision_rules: audit who captures slots, who misses turns, who waits longest despite reservation, and whose reservations are most often cancelled or unserved

target_invariants: a reservation represents a real and inspectable claim, not a vague promise

target_invariants: waiting burden is reduced without destroying order, safety, or service accountability

target_invariants: reservation status, ownership, time, and next action remain visible to the system

target_invariants: no-show and release rules protect scarce capacity without punishing unavoidable constraints disproportionately

target_invariants: reservation channels do not systematically exclude actors with less flexibility, weaker technology, disabilities, language barriers, or unstable contact access

target_invariants: cancellations, delays, and overbooking are communicated before they become silent service failures

target_invariants: standby, urgent, or walk-in exceptions are governed rather than informal backdoors

expected_outcomes: reduced physical congestion, hold time, live waiting, crowding, and channel occupancy

expected_outcomes: lower waiting burden for actors who would otherwise spend time, attention, travel, or space to preserve access

expected_outcomes: more orderly reconnection between waiting actors and available service capacity

expected_outcomes: better utilization of service windows through confirmations, reminders, cancellations, and standby fill-ins

expected_outcomes: clearer accountability for reserved claims, missed turns, cancellations, and overbooking decisions

expected_outcomes: improved accessibility and dignity when reservation channels are designed inclusively

expected_outcomes: less conflict over place in line because position or slot is represented explicitly

prose_essence:
Queue Reservation converts a costly waiting line into a governed claim. Instead of requiring people, work items, callers, jobs, or vehicles to remain continuously present in a scarce service channel, the system preserves their place, slot, callback turn, or access opportunity through a token or record. The archetype works when the system can reliably represent the claim and reconnect it to service at the right time.

The key move is not merely "make an appointment" or "show a wait time." It is to separate the right to be served from the burden of continuous waiting while preserving queue integrity. A reservation can be a numbered ticket, appointment slot, callback, timed-entry pass, virtual queue position, standby claim, or execution lease, but those mechanisms only become Queue Reservation when they are wrapped in rules for claim validity, notification, no-shows, release, and fair access.

prose_when_to_use:
Use Queue Reservation when waiting itself is causing avoidable harm: people must stand in a lobby, remain on hold, repeatedly refresh a page, keep a process active, or physically occupy a place to avoid losing service order. It is especially useful when physical waiting creates congestion, uncertainty, exclusion, exposure, anxiety, or conflict, and when a durable representation of place or slot can preserve service order.

The archetype fits public service counters, clinics, call centers, events, restaurants, repair queues, software execution queues, and other systems where the service path is scarce but waiting can safely happen elsewhere. It is weaker when real-time presence is essential for safety, identity, readiness, custody, or mutual adjustment.

prose_structural_problem:
A queue exists because service capacity is scarce, but the system requires actors or items to continuously occupy the waiting channel to preserve access. That transfers operational scarcity onto the waiter: time, space, attention, physical presence, digital presence, travel, or channel occupancy. Actors who cannot afford that burden lose access even when they arrived legitimately.

The visible symptoms are long physical lines, long phone holds, crowded lobbies, repeated status inquiries, conflict over place, abandonment, duplicate bookings, and informal lists that operators cannot audit. Reservation reduces the waiting burden, but it can also create new pathologies if the reservation layer is opaque or inaccessible.

prose_intervention_logic:
The intervention begins by defining what is being reserved: an ordinal place, a time slot, a callback turn, a service window, a timed-entry claim, a conditional standby claim, or a future execution opportunity. The system then creates a durable record that links the holder to that claim, specifies when and how service will be activated, and defines what happens if the holder cannot use the opportunity.

A complete Queue Reservation design includes a claim rule, a notification or return rule, a no-show rule, a cancellation and release rule, and an access policy. These rules allow the system to reduce continuous waiting without letting absent holders block the queue indefinitely, without issuing promises it cannot honor, and without letting privileged actors capture all convenient slots.

prose_key_components:
- **Reservation Token** (`reservation_token`): The token represents the actor, item, slot, or position while continuous waiting is suspended. It may be a number, appointment, callback record, digital place, timed-entry credential, or execution lease.
- **Position or Slot Record** (`position_or_slot_record`): This is the operational memory of the reservation. It records who or what holds a place, what is promised, when service is expected, and what status the claim currently has.
- **Eligibility and Claim Rule** (`eligibility_and_claim_rule`): This rule defines who may claim a reservation, how claims are validated, and when they are invalid, duplicated, transferred, or revoked.
- **Service Window or Callback Policy** (`service_window_or_callback_policy`): This policy specifies when the holder should return, be contacted, or become eligible for service. It turns indefinite waiting into a coordinated interval.
- **Notification Policy** (`notification_policy`): The notification policy tells holders when to act, when delays occur, and when a turn or window is approaching. It supports waiting elsewhere without losing coordination.
- **No-Show Policy** (`no_show_policy`): The no-show policy governs missed windows, missed calls, late arrivals, and unclaimed turns. It protects capacity but must be calibrated for fairness.
- **Release and Reassignment Rule** (`release_and_reassignment_rule`): This rule returns abandoned or unused reserved capacity to the queue through cancellation, standby, waitlist, or reentry logic.
- **Fairness and Access Policy** (`fairness_and_access_policy`): This policy prevents reservation from becoming an exclusionary barrier or hidden privilege system.

Optional components include standby/waitlist rules, capacity holdbacks for walk-ins or urgent cases, identity verification, reservation status views, and rescheduling paths.

prose_common_mechanisms:
Appointment systems implement Queue Reservation by assigning service opportunities to named slots or windows. They are not the same as the archetype: without claim validity, no-show, release, and access rules, they are ordinary scheduling tools.

Virtual queues preserve a place in line while the actor waits elsewhere. Numbered tickets do the same with a simple token and a display or call sequence. Callback queues preserve order while recontacting the actor later. Timed entry assigns access windows to reduce congestion. Online booking portals provide an interface for claiming and updating reservations. Standby lists fill unused capacity when primary reservations are cancelled or missed.

Each mechanism implements only part of the archetype. Queue Reservation is the full governance pattern that keeps the claim honest, visible, usable, and fair.

prose_failure_modes:
Common failure modes include reservation hoarding, duplicate booking, inaccessible booking channels, no-show cascades, false promise backlogs, absent holders blocking later service, slot capture by advantaged actors or bots, and overbooking that violates trust.

Mitigations include eligibility rules, duplicate detection, confirmations, accessible alternatives, reminder sequences, grace periods, standby lists, clear release rules, backlog caps, public reporting, appeal paths, and regular equity audits.

prose_neighbor_distinctions:
Queue Reservation is distinct from **Scheduling** because it is grounded in a queueing problem: preserving access or order while reducing waiting burden. Scheduling may arrange any activity in time; Queue Reservation is specifically about substituting a represented claim for continuous waiting.

It is distinct from **Load Leveling / Demand Smoothing** because smoothing moves demand to reduce peaks, while Queue Reservation preserves a claim so the waiter need not occupy the channel. It is distinct from **Capacity Reservation** because the reserved object is a specific queue place, slot, callback turn, or service claim, not generic spare capacity for future surge or priority need.

It is distinct from **Priority-Based Admission** because a reservation need not change who deserves service first. It is distinct from **Queue Discipline Design** because the queue discipline determines order, while reservation represents that order or opportunity outside the continuous waiting channel. It is distinct from **Queue Transparency** because communication about waiting is a component, not the claim-preserving intervention itself.

prose_cross_domain_examples:
In a call center, a callback queue lets callers keep their place without staying on hold. In a clinic, appointment slots and reminders reserve service windows and reduce lobby crowding. In a public office, numbered tickets preserve order while visitors sit, leave briefly, or monitor a display. In a museum, timed-entry passes reduce congestion at the door. In cloud computing, execution leases or future resource reservations let jobs wait without occupying active worker capacity.

These examples differ in implementation, but they share the same abstraction: service access is represented so waiting can be moved out of the scarce channel.

prose_non_examples: A normal calendar meeting is not Queue Reservation unless it solves a queueing burden. A priority triage decision is not Queue Reservation unless it also creates a reserved claim or slot. A wait-time display is not Queue Reservation if the user must remain continuously present. A capacity holdback for emergencies is Capacity Reservation unless it attaches to specific waiting actors or items. A passive buffer that stores work is not Queue Reservation unless a claim to future service is represented and governed.

prose_tradeoffs:
Queue Reservation replaces one burden with another. It reduces continuous waiting but adds administrative complexity. It can improve utilization but introduce no-show penalties and slot-capture problems. It can make service feel orderly but hide scarcity behind a booking calendar. It can give people more autonomy but require them to monitor texts, portals, calls, or deadlines.

The core tradeoff is between flexibility, fairness, and capacity utilization. Strict reservation protects order but may punish unavoidable lateness. Loose reservation is humane but may waste service windows. Overbooking protects utilization but can damage trust if the system bumps legitimate holders.

prose_invariants_to_preserve:
A reservation must represent a real claim, not a vague hope. It must remain visible to the system and understandable to the holder. Queue order or access should be preserved unless a documented priority, urgency, or release rule justifies deviation. No-show rules must protect capacity without disproportionately punishing people facing practical constraints. Exceptions such as walk-ins, urgent cases, and standby fills must be governed rather than informal backdoors.

The most important invariant is that reservation reduces waiting burden without erasing unmet demand. If the visible line disappears but the system silently pushes people into an inaccessible booking backlog, the intervention has failed.

prose_target_outcomes:
Effective Queue Reservation reduces live waiting, physical congestion, phone hold time, channel occupancy, anxiety, and conflict over place. It improves reconnection between waiting actors and service capacity, increases trust when claims are honored, and can improve capacity utilization through confirmations, reminders, cancellations, and standby fill-ins.

It also improves equity when designed well: people who cannot physically wait can preserve access. But that equity gain is not automatic; it depends on inclusive channels, assisted alternatives, walk-in or urgent capacity, and audit of who actually gets reservations.

prose_parameter_dimensions:
Important tuning dimensions include reservation horizon, slot size, service-window width, grace period, no-show threshold, cancellation deadline, overbooking rate, standby priority, walk-in holdback percentage, notification frequency, number of retry attempts, identity-verification strictness, and maximum reservation backlog.

Designers also tune how precise the promise is. A reservation may guarantee exact service time, approximate return window, ordinal place, callback opportunity, conditional standby access, or only a claim to be reconsidered. The more precise the promise, the stronger the operational burden to honor it.

prose_variants_and_near_names:
Recognized variants include virtual queue reservation, appointment-based queue reservation, callback queue reservation, numbered-token queue reservation, and standby or overbooking reservation. Near names include virtual queue, appointment system, callback queue, numbered ticket, timed entry, waitlist, and booking portal.

Most near names are mechanisms. They should collapse into Queue Reservation when the broader structure is present: a durable claim to place, slot, or service opportunity, plus rules for validity, notification, no-show, release, and fair access.

raw_bundle_path: solution_archetypes/_Batches/First Iteration/Batch 06/Wave 2/queue_reservation_solution_archetype_output_bundle.zip

Virtual Queue Reservation

short_definition: Preserves a queue position digitally or symbolically while the actor waits away from the physical line.

distinctive_feature: The reservation substitutes a virtual representation for physical presence in the waiting line.

distinct_from_parent: It is the digital or symbolic-place subtype of Queue Reservation; the parent also includes appointments, callbacks, numbered tickets, timed entry, and standby reservations.

still_same_parent_because: It still reserves a position or service opportunity so continuous waiting is not required.

source_primes: queueing representation virtualization

use_when: physical waiting creates crowding, anxiety, exposure, travel burden, or lost productive time

use_when: service order should be preserved even though actors are not physically present

use_when: the system can notify actors reliably when their turn approaches

Reservation Token

role: represents queue position

Notification Policy

role: signals when to return

No-Show Policy

role: handles missed turns

Virtual Queue

role: holds remote place in line

Queue Position Indicator

role: shows place and movement

example_domains:
customer service
theme parks and events
clinics and public counters
online support

Appointment-Based Queue Reservation

short_definition: Converts open waiting into named service slots or windows so actors arrive near the time service capacity is expected to be available.

distinctive_feature: The reservation is anchored to time rather than only to ordinal queue position.

distinct_from_parent: It is the time-slot subtype; the parent includes non-time-based tokens and callbacks too.

still_same_parent_because: The core move is still to preserve access/order without requiring continuous waiting.

use_when: service can be divided into slots, windows, or predictable service opportunities

use_when: open waiting would create congestion, wasted time, or inequitable burden

use_when: no-shows, late arrivals, and overruns can be managed through explicit rules

Service Window or Callback Policy

role: sets return window

No-Show Policy

role: protects unused capacity

Rescheduling Path

role: supports legitimate changes

Appointment System

role: assigns slots

Online Booking Portal

mechanism_type: interface

role: claims and updates slots

example_domains:
healthcare appointments
government services
repair bookings
professional services

Callback Queue Reservation

short_definition: Keeps actors in order while returning contact later, allowing them to leave a live hold, lobby, or service channel.

distinctive_feature: The reserved turn is activated through outbound contact rather than through a fixed arrival time or persistent place display.

distinct_from_parent: It focuses on remote recontact; the parent includes all mechanisms that reserve place or slot.

still_same_parent_because: The callback preserves queue access without continuous waiting.

source_primes: queueing representation feedback

use_when: live waiting is costly, frustrating, or likely to cause abandonment

use_when: the system can reliably call, message, or otherwise recontact the actor

use_when: queue order should remain recognizable even when actors disconnect or leave the site

Notification Policy

role: controls callback timing

Release and Reassignment Rule

role: handles unclaimed callback

Fairness and Access Policy

role: protects reachable and less-reachable actors

Callback Queue

role: holds place and triggers contact

Reminder and Confirmation Sequence

mechanism_type: communication

role: reduces missed contact

example_domains:
call centers
telehealth
public service hotlines
technical support

Numbered-Token Queue Reservation

short_definition: Issues numbered or ordered tokens that preserve service sequence while actors wait outside a physical line.

distinctive_feature: The representation is an explicit ordered token rather than a scheduled appointment or invisible internal record.

distinct_from_parent: It is a simple tokenized form of the broader reservation pattern.

still_same_parent_because: It substitutes a durable claim record for continuous line presence.

source_primes: queueing representation order

use_when: the system needs a low-technology way to preserve order

use_when: physical lines create congestion or conflict but service order remains important

use_when: actors can monitor calls, displays, or announcements for their token

Reservation Token

role: holds ordered claim

Eligibility and Claim Rule

role: defines valid use

Reservation Status View

role: announces current service number

Numbered Ticket

role: represents place

Ticket Display Board

role: calls turns

example_domains:
delicatessens
public offices
pharmacies
repair counters

Standby or Overbooking Reservation

short_definition: Uses secondary reservation claims to fill unused service capacity when primary reservations are cancelled, missed, or released.

distinctive_feature: It reserves conditional access rather than guaranteed service at a specific time or place.

distinct_from_parent: It is a utilization-sensitive variant that layers conditional claims on top of ordinary reservations.

still_same_parent_because: It still governs represented claims to a place or service opportunity in a queueing system.

use_when: no-shows or cancellations are common enough to waste scarce service capacity

use_when: some actors accept uncertainty or later confirmation in exchange for possible earlier service

use_when: the system can communicate standby status and priority without deception

Standby or Waitlist Policy

role: orders conditional claims

Release and Reassignment Rule

role: moves unused capacity

Fairness and Access Policy

role: limits exploitative uncertainty

Standby List

role: fills released slots

Confirmation Sequence

role: checks primary use

example_domains:
airline standby
clinic waitlists
restaurant reservations
maintenance windows

near_names:
Virtual Queue
Appointment System
Callback Queue
Numbered Ticket
Timed Entry
Queue Reservation