AUTO-RENT-008 End-to-End Example

Overview

AUTO-RENT-008, Assign Rental Vehicle, compares candidate rental vehicles using reservation fit, available unit match, prep readiness, rental margin impact, assignment risk, and pickup time pressure.

Decision ID: AUTO-RENT-008
Decision Name: Assign Rental Vehicle
Decision Preparation Model: vehicle-rental-assign-rental-vehicle-auto-rent-008 version 1.0.0
Default Profile: balanced
Runnable Scenario: standard_rental
Catalog: DKR-AUTO-RUNTIME-001, version 13.9.3
API Compatibility: 7.4.0 or later Prepared Criteria Mode execution flow

The deterministic Decision Service ranks the options. The explanation object is explanatory only and must not choose, rerank, or override the deterministic result.

Understanding This Decision

Assign Rental Vehicle helps a vehicle-rental operation compare available candidate vehicles before assigning one to a reservation. The decision is useful when a branch or rental platform needs a repeatable recommendation that balances customer fit, available unit match, operational readiness, margin impact, risk exposure, and pickup timing pressure.

Business question

Which vehicle should be recommended for assign rental vehicle in the selected Vehicle Rental context?

Expected outcome

A recommended vehicle or ranked set of vehicles with the criteria that most influenced the result.

Typical users

Rental operations managers, branch teams, fleet coordinators, reservation platforms, and integration teams building rental assignment workflows.

Decision boundary

Use this decision to rank supplied candidate vehicles. It does not discover missing vehicles and does not replace rental policy checks, eligibility controls, damage review, or required approvals.

Assumptions: submitted vehicles represent the real options under consideration, criterion values use the units and scales requested by the catalog, and the selected profile and scenario reflect the intended business priorities.

Criteria

Criterion IDs are intentionally stable machine identifiers. Display labels are for users; request values should be keyed by canonical criterionId.

Criterion ID	Name	Direction	Weight	Validation
`assign_rental_vehicle_customer_fit_score`	Reservation Vehicle Fit	maximize	18	`score_0_to_100`
`assign_rental_vehicle_vehicle_or_resource_fit`	Available Unit Match	maximize	18	`score_0_to_100`
`assign_rental_vehicle_operational_readiness`	Vehicle Prep Readiness	maximize	17	`score_0_to_100`
`assign_rental_vehicle_financial_impact`	Rental Margin Impact	maximize	17	`score_0_to_100`
`assign_rental_vehicle_risk_exposure`	Assignment Risk	minimize	15	`score_0_to_100`
`assign_rental_vehicle_time_sensitivity`	Pickup Time Pressure	minimize	15	`score_0_to_100`

Data Preparation Guide

Loading criterion-specific integration guidance...

Constraint Processing

This decision currently has no catalog-defined hard constraints. All validated candidates proceed to criteria-based ranking.

Verified Catalog Constraint	Status	Effect
None returned by Decision Catalog for `AUTO-RENT-008`	No hard constraints defined	Candidate eligibility is determined by request validation; all validated candidates are ranked by criteria.

Eligible and Excluded Candidates

{
  "constraintSummary": {
    "definedConstraintCount": 0,
    "activeConstraintCount": 0,
    "eligibleOptionCount": 3,
    "excludedOptionCount": 0,
    "eligibleOptions": [
      "OPTION-001",
      "OPTION-002",
      "OPTION-003"
    ],
    "excludedOptions": []
  }
}

Constraint Handling Pattern

const constraints = decisionDetail.constraints || [];
if (constraints.length === 0) {
  // No catalog-defined hard constraints.
  // Submit all validated candidates for criteria-based ranking.
}

const summary = response.constraintSummary;
const excluded = response.decisionResult?.excludedOptions || [];

Excluded candidates do not participate in ranking because hard constraints are evaluated before scoring. For AUTO-RENT-008, no candidates are excluded by catalog-defined constraints because Decision Catalog currently defines none for this decision.

Decision Preparation Model

The published Decision Preparation Model validates and transforms illustrative Business Data into the canonical criteria required by AUTO-RENT-008 before Decision Service applies ranking.

Profile ID: vehicle-rental-assign-rental-vehicle-auto-rent-008
Version: 1.0.0
Options Path: $.reservations
Option ID Path: $.optionId
Display Name Path: $.name

Client Business Data Path	Transformation	Canonical Criterion ID	Unit	Validation
`option.vehicle.assignment.reservationVehicleFit`	direct value	`assign_rental_vehicle_customer_fit_score`	Score	Input Contract
`option.vehicle.assignment.availableUnitMatch`	direct value	`assign_rental_vehicle_vehicle_or_resource_fit`	Score	Input Contract
`option.vehicle.assignment.vehiclePrepReadiness`	direct value	`assign_rental_vehicle_operational_readiness`	Score	Input Contract
`option.vehicle.assignment.rentalMarginImpact`	direct value	`assign_rental_vehicle_financial_impact`	Score	Input Contract
`option.risk.assignmentRisk`	direct value	`assign_rental_vehicle_risk_exposure`	Score	Input Contract
`option.schedule.pickupTimePressure`	direct value	`assign_rental_vehicle_time_sensitivity`	Score	Input Contract

Illustrative Business Data

{
  "requestContext": {
    "sourceSystem": "automotive-profile-factory",
    "correlationId": "factory-auto-rent-008"
  },
  "reservations": [
    {
      "optionId": "RENTAL-008-01",
      "name": "Assign Rental Vehicle Option 1",
      "vehicle": {
        "assignment": {
          "reservationVehicleFit": 60.0,
          "availableUnitMatch": 69.0,
          "vehiclePrepReadiness": 83.0,
          "rentalMarginImpact": 80
        }
      },
      "risk": {
        "assignmentRisk": 39.0
      },
      "schedule": {
        "pickupTimePressure": 42.0
      }
    },
    {
      "optionId": "RENTAL-008-02",
      "name": "Assign Rental Vehicle Option 2",
      "vehicle": {
        "assignment": {
          "reservationVehicleFit": 72.0,
          "availableUnitMatch": 57.0,
          "vehiclePrepReadiness": 65.0,
          "rentalMarginImpact": 66
        }
      },
      "risk": {
        "assignmentRisk": 67.0
      },
      "schedule": {
        "pickupTimePressure": 64.0
      }
    }
  ]
}

Prepared Option Values

{
  "optionId": "RENTAL-008-01",
  "name": "Assign Rental Vehicle Option 1",
  "values": {
    "assign_rental_vehicle_customer_fit_score": 60.0,
    "assign_rental_vehicle_vehicle_or_resource_fit": 69.0,
    "assign_rental_vehicle_operational_readiness": 83.0,
    "assign_rental_vehicle_financial_impact": 80,
    "assign_rental_vehicle_risk_exposure": 39.0,
    "assign_rental_vehicle_time_sensitivity": 42.0
  }
}

This example illustrates the published request structure. Adapt it to your organization and validate it against the Input Contract.

Profiles and Scenarios

This example selects a Profile for evaluation emphasis and a Scenario for operating context. Discover both from the selected Decision Catalog definition.

Profile ID	Name	Purpose
`balanced`	Balanced	Balances customer fit, resource fit, readiness, financial impact, risk, and timing.
`revenue_focused`	Revenue Focused	Places extra emphasis on revenue, margin, and financial impact.
`risk_control`	Risk Control	Places extra emphasis on reducing rental, compliance, insurance, and operational risk.

Scenario ID	Name	Use When
`standard_rental`	Standard Rental	Normal demand, standard customer risk, and normal branch capacity.
`peak_demand`	Peak Demand	High utilization, limited availability, and elevated timing pressure.
`risk_sensitive`	Risk Sensitive	Higher-than-normal fraud, damage, compliance, or insurance exposure.

Use standard_rental in runnable examples because it is present in the active Decision Catalog scenario list for AUTO-RENT-008.

Prepared Input

For Prepared Criteria Mode, send decisionId, selected profile/scenario IDs, and option values keyed by canonical criterion ID.

Prepared Criteria Mode JSON

{
  "decisionId": "AUTO-RENT-008",
  "profileId": "balanced",
  "scenarioId": "standard_rental",
  "algorithm": "TOPSIS",
  "weightStrategy": "Expert",
  "runSensitivity": false,
  "requestContext": {
    "correlationId": "auto-rent-008-demo-001"
  },
  "options": [
    {
      "optionId": "OPTION-001",
      "name": "Compact SUV Unit",
      "values": {
        "assign_rental_vehicle_customer_fit_score": 88,
        "assign_rental_vehicle_vehicle_or_resource_fit": 91,
        "assign_rental_vehicle_operational_readiness": 84,
        "assign_rental_vehicle_financial_impact": 76,
        "assign_rental_vehicle_risk_exposure": 20,
        "assign_rental_vehicle_time_sensitivity": 26
      }
    },
    {
      "optionId": "OPTION-002",
      "name": "Standard Sedan Unit",
      "values": {
        "assign_rental_vehicle_customer_fit_score": 79,
        "assign_rental_vehicle_vehicle_or_resource_fit": 85,
        "assign_rental_vehicle_operational_readiness": 92,
        "assign_rental_vehicle_financial_impact": 70,
        "assign_rental_vehicle_risk_exposure": 18,
        "assign_rental_vehicle_time_sensitivity": 34
      }
    },
    {
      "optionId": "OPTION-003",
      "name": "Premium SUV Unit",
      "values": {
        "assign_rental_vehicle_customer_fit_score": 92,
        "assign_rental_vehicle_vehicle_or_resource_fit": 73,
        "assign_rental_vehicle_operational_readiness": 68,
        "assign_rental_vehicle_financial_impact": 88,
        "assign_rental_vehicle_risk_exposure": 42,
        "assign_rental_vehicle_time_sensitivity": 50
      }
    }
  ]
}

Execute

cURL

curl -X POST "https://dde.vinquery.com/api/v1/decide" \
  -H "Authorization: Bearer ${DECISIOQ_TOKEN}" \
  -H "Content-Type: application/json" \
  -H "X-Correlation-Id: auto-rent-008-demo-001" \
  --data @auto-rent-008-execute.json

The Decision Service validates the request, retrieves authoritative criteria, constraints, profiles, scenarios, and validation metadata from Decision Catalog, applies hard constraints, ranks eligible options, and returns the decision result plus execution metadata.

Interpret the Result

The successful response includes both the deterministic decisionResult and a top-level explanation object. The explanation adds context but does not select, rerank, recalculate, or override the ranking.

Successful Response Shape

{
  "service": "decisioq",
  "version": "7.6.3",
  "requestId": "0HNE...",
  "operation": "Decide",
  "success": true,
  "decisionType": "AUTO-RENT-008",
  "decisionVersion": "13.9.3",
  "timestampUtc": "2026-07-18T00:00:00Z",
  "decisionResult": {
    "winner": "OPTION-001",
    "confidence": 72.4,
    "ranking": [
      {
        "optionId": "OPTION-001",
        "score": 0.8421,
        "breakdown": {
          "assign_rental_vehicle_customer_fit_score": 0.18,
          "assign_rental_vehicle_vehicle_or_resource_fit": 0.18,
          "assign_rental_vehicle_operational_readiness": 0.17,
          "assign_rental_vehicle_financial_impact": 0.17,
          "assign_rental_vehicle_risk_exposure": 0.15,
          "assign_rental_vehicle_time_sensitivity": 0.15
        },
        "normalizationBreakdown": {}
      }
    ],
    "excludedOptions": []
  },
  "explanation": {
      "summary": "The selected rental vehicle provided the strongest overall assignment fit.",
      "whyRecommended": "It combined strong reservation fit, available unit match, and readiness with acceptable risk exposure and pickup time pressure.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard rental scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
  },
  "constraintSummary": {
    "definedConstraintCount": 0,
    "activeConstraintCount": 0,
    "eligibleOptionCount": 3,
    "excludedOptionCount": 0,
    "eligibleOptions": [
    "OPTION-001",
    "OPTION-002",
    "OPTION-003"
    ],
    "excludedOptions": []
  },
  "warnings": [],
  "requestContext": {
    "correlationId": "auto-rent-008-demo-001"
  }
}

WinnerThe selected option ID in decisionResult.winner.

RankingAll eligible options ordered by score.

BreakdownCriterion-level evidence for the ranking.

Sensitivity Analysis

runSensitivity is an optional execution flag supported by both Business Data Mode and Prepared Criteria Mode. Set it to true when the client wants recommendation-stability information in the same response. The sensitivity engine analyzes the prepared criteria produced by either input path, perturbs criterion weights by controlled factors, and reports whether the winner remains stable.

Sensitivity Result Shape

{
  "sensitivityResult": {
    "stableWinner": true,
    "winner": "OPTION-001",
    "mostSensitiveCriterion": "assign_rental_vehicle_vehicle_or_resource_fit",
    "confidence": 95,
    "criterionImpacts": {
      "assign_rental_vehicle_vehicle_or_resource_fit": 0.1842
    },
    "winnerChangeCounts": {
      "assign_rental_vehicle_vehicle_or_resource_fit": 0
    }
  }
}

Use sensitivity output to decide whether a recommendation is robust enough for automation or should be reviewed by a person.

Explanation of Decision Result

Every successful response includes a provider-neutral top-level explanation object. It explains the already-finalized deterministic result and does not change the ranking.

Explanation Shape

{
  "explanation": {
      "summary": "The selected rental vehicle provided the strongest overall assignment fit.",
      "whyRecommended": "It combined strong reservation fit, available unit match, and readiness with acceptable risk exposure and pickup time pressure.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard rental scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
  }
}

Explanation output is supporting context. Business-facing pages should render the explanation, warnings, assumptions, and limitations without provider branding.

Tracing and Logs

Use request identifiers to connect client, catalog, and execution activity during support or integration testing.

X-Request-Id: Optional client-supplied request ID. If omitted, the server generates one.
X-Correlation-Id: Optional client correlation value propagated from Decision Service to Decision Catalog.
requestContext.correlationId: Optional payload value echoed in the response and used for tracing.
configurationUsed: The authoritative effective weight strategy, ranking algorithm, profile, scenario, sensitivity setting, and source for each value.
diagnostics: Safe execution counts returned only when responseOptions.includeDiagnostics is true.

Code Examples

These examples demonstrate the current DecisioQ flow:

Request jwtToken from https://identity.vinquery.com/connect/token.
Load AUTO-RENT-008 metadata from https://dks.vinquery.com/decisioncatalog/decisions/AUTO-RENT-008.
Execute a Prepared Criteria Mode at https://dde.vinquery.com/api/v1/decide.

Set these environment variables before running any companion example:

Environment variables

DECISIOQ_CLIENT_ID
DECISIOQ_CLIENT_SECRET

Optional:
DECISIOQ_IDENTITY_URL=https://identity.vinquery.com/connect/token
DECISIOQ_DKS_URL=https://dks.vinquery.com
DECISIOQ_DDE_URL=https://dde.vinquery.com

Download the source files directly:

README.mdSetup and run notes Prepared Input JSONShared request payload cURL shell scriptrun-auto-rent-008.sh C# Program.cs.NET 8 console example JavaScriptNode.js module TypeScriptTyped fetch example PythonStandard library example PHPcURL extension example Gonet/http example Javajava.net.http example Complete ASP.NET Core ExampleRunnable .NET 8 project ZIP

Troubleshooting

Symptom	Likely Cause	What to Check
401 Unauthorized	Missing, expired, or invalid bearer token.	Request a fresh `jwtToken` from Identity and send it as `Authorization: Bearer ...`.
Decision not found	The decision ID is not in the active catalog.	Load `/decisioncatalog/decisions/AUTO-RENT-008` and confirm the ID is published.
Validation failed	A required criterion value is missing or outside its rule.	Use canonical criterion IDs and keep score values in the expected range.
HTML error response	An upstream hosted service failed before returning JSON.	Check service health and server logs for Identity, Decision Catalog, or Decision Service.

Production Checklist

Security

Keep API Consumer credentials and jwtTokens on the server side. Use HTTPS, short-lived bearer tokens, and an API Consumer linked to a DecisioQ account for usage accounting.

Catalog

Load decision metadata from Decision Catalog and cache cautiously. Refresh when catalog versions change.

Request Quality

Use canonical criterion IDs, validate value ranges, and send at least two candidate options.

Operations

Send correlation IDs, record request IDs, and monitor non-JSON upstream failures.

Explanation

Display explanation text as supporting context only. Never let generated explanation text override deterministic results.

User Experience

Show business labels to users and keep raw execution trace collapsed for advanced diagnostics.