Johor-Batam Truck Ferry Logistics Scheduling Agent with Pydantic AI
The proposed Johor-Batam Ro-Ro truck ferry service aims to streamline cross-border freight between Malaysia, Singapore, and Indonesia. However, delays in infrastructure readiness require dynamic contingency scheduling for cross-straits logistics providers operating multi-modal routes. In this challenge, you will build a type-safe freight scheduling agent using Pydantic AI. The agent validates truck manifest inputs, evaluates queue delays at sea ports (Johor, Batam, Singapore), dynamically re-routes cross-border freight via alternative sea or land routes, and guarantees strict output schema validation.
What you are building
Develop a type-safe cross-border truck ferry logistics optimization agent using Pydantic AI.
How work is evaluated
Evaluates dynamic re-routing accuracy and Pydantic schema validation adherence.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
pydantic_schema_validation
Output must pass strict Pydantic model validation without exceptions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
transit_time_efficiency
Optimized route transit time relative to direct baseline • target: 15 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement type-safe agent workflows with Pydantic AI models and dependency injection
Model multi-modal transit delays across Johor, Batam, and Singapore ports
Dynamically select optimal transport routes (Direct Ferry vs Feeder Barge vs Causeway Trucking)
Generate structured, schema-validated route itineraries with cost and time estimates
Resources
Cross-straits truck transit log dataset containing ferry schedules, causeway delay metrics, and port barge schedules.
Challenge setup
Evaluates dynamic re-routing accuracy and Pydantic schema validation adherence.
Challenge input
JSON with origin, destination, cargo urgency, and port disruption flags
Pydantic AI
Required Python framework for type-safe structured output parsing.
Evaluated output
JSON matching Pydantic schema for selected itinerary, transit time, and estimated cost
- Output must pass strict Pydantic model validation without exceptions.
- Optimized route transit time relative to direct baseline • target: 15 • range: 0-100
- Benchmark: LogiBench-Routing
- Transit Time Efficiency target: 15
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Pydantic AI · Required
- RAI · Optional
- Aim · Optional
- Pydantic AI · Required
Evaluation contract
- pydantic_schema_validation · Weight 1
- transit_time_efficiency · Weight 1
Metrics
- transit_time_efficiency · Target 15
Benchmarks
- LogiBench-Routing
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with origin, destination, cargo urgency, and port disruption flags
Output: JSON matching Pydantic schema for selected itinerary, transit time, and estimated cost
Execution harness: Python
Evaluation tools
- Pydantic AI · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.