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
- Industry Profile
vehicle-rental-assign-rental-vehicle-auto-rent-008version1.0.0- Default Profile
balanced- Runnable Scenario
standard_rental- Catalog
DKR-AUTO-RUNTIME-001, version13.9.3- API Compatibility
7.4.0or later catalog execution flow
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.
Architecture
- Request a token from the Identity service.
- Use the returned
jwtTokenas the bearer token. - Load the catalog and decision detail from DKS.
- Build a Prepared Decision Input using canonical criterion IDs.
- Execute the decision through DDE.
- Use
X-Request-IdandX-Correlation-Idfor tracing across DDE and DKS.
Authentication
Request a secure session token from Identity. The current token response field is jwtToken.
POST https://identity.vinquery.com/connect/token
Content-Type: application/json
{
"clientId": "{clientId}",
"clientSecret": "{clientSecret}",
"audience": "vinquery:api:decisioq"
}
Catalog Discovery
Use DKS to discover the catalog and then load complete metadata for AUTO-RENT-008.
GET https://dks.vinquery.com/decisioncatalog
Authorization: Bearer {jwtToken}
GET https://dks.vinquery.com/decisioncatalog/decisions/AUTO-RENT-008
Authorization: Bearer {jwtToken}
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 |
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 DKS for AUTO-RENT-008 | No hard constraints defined | Candidate eligibility is determined by request validation; all validated candidates are ranked by criteria. |
{
"constraintSummary": {
"definedConstraintCount": 0,
"activeConstraintCount": 0,
"eligibleOptionCount": 3,
"excludedOptionCount": 0,
"eligibleOptions": [
"OPTION-001",
"OPTION-002",
"OPTION-003"
],
"excludedOptions": []
}
}
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 DKS currently defines none for this decision.
Industry Profile
The active industry profile uses $.reservations as the options path, $.optionId as the option ID, and $.name as the option display name.
| Business Data Path | Criterion ID |
|---|---|
option.vehicle.assignment.reservationVehicleFit | assign_rental_vehicle_customer_fit_score |
option.vehicle.assignment.availableUnitMatch | assign_rental_vehicle_vehicle_or_resource_fit |
option.vehicle.assignment.vehiclePrepReadiness | assign_rental_vehicle_operational_readiness |
option.vehicle.assignment.rentalMarginImpact | assign_rental_vehicle_financial_impact |
option.risk.assignmentRisk | assign_rental_vehicle_risk_exposure |
option.schedule.pickupTimePressure | assign_rental_vehicle_time_sensitivity |
{
"requestContext": {
"sourceSystem": "vehicle-rental-demo",
"correlationId": "auto-rent-008-demo-001"
},
"reservations": [
{
"optionId": "OPTION-001",
"name": "Compact SUV Unit",
"vehicle": {
"assignment": {
"reservationVehicleFit": 88,
"availableUnitMatch": 91,
"vehiclePrepReadiness": 84,
"rentalMarginImpact": 76
}
},
"risk": {
"assignmentRisk": 20
},
"schedule": {
"pickupTimePressure": 26
}
}
]
}
{
"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
}
}
Profiles and Scenarios
A profile changes criterion weights. A scenario describes the operating context for the execution. The active DKS catalog exposes these choices with the decision detail response.
| 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. |
standard_rental in runnable examples because it is present in the active DKS scenario list for AUTO-RENT-008.Prepared Input
For Prepared Decision Input, send decisionId, selected profile/scenario IDs, and option values keyed by canonical criterion ID.
{
"decisionId": "AUTO-RENT-008",
"profileId": "balanced",
"scenarioId": "standard_rental",
"algorithm": "TOPSIS",
"weightStrategy": "Manual",
"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 -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 Engine validates the request, retrieves authoritative criteria, constraints, profiles, scenarios, and validation metadata from DKS, applies hard constraints, ranks eligible options, and returns the decision result plus execution metadata.
Interpret the Result
The successful response is centered on decisionResult. The winner and ranking are deterministic outputs; optional explanation fields add context but do not change the ranking.
{
"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": "Compact SUV Unit ranked highest based on the submitted rental assignment criteria.",
"strengths": [],
"weaknesses": [],
"exclusions": []
}
},
"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"
}
}
decisionResult.winner.Sensitivity Analysis
Set runSensitivity to true on catalog execution when the client wants recommendation-stability information in the same response. The sensitivity engine perturbs criterion weights by controlled factors and reports whether the winner remains stable.
{
"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 can include deterministic explanation text under decisionResult.explanation and explanation. If the optional AI layer is enabled, the response may also include an ai block with structured business-language explanation fields.
{
"ai": {
"enabled": true,
"generated": true,
"schemaValidated": true,
"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": []
}
}
}
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 DDE to DKS.
requestContext.correlationId- Optional payload value echoed in the response and used for tracing.
executionTrace- Advanced execution metadata including selected profile, selected scenario, effective weights, and applied constraints.
Code Examples
These examples demonstrate the current DecisioQ flow:
- Request
jwtTokenfromhttps://identity.vinquery.com/connect/token. - Load
AUTO-RENT-008metadata fromhttps://dks.vinquery.com/decisioncatalog/decisions/AUTO-RENT-008. - Execute a Prepared Decision Input at
https://dde.vinquery.com/api/v1/decide.
Set these environment variables before running any companion example:
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:
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, DKS, or DDE. |
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 DKS 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 AI text override deterministic results.
User Experience
Show business labels to users and keep raw execution trace collapsed for advanced diagnostics.
