Phosphate Rail Logistics Orchestrator with AutoGen
Saudi Arabia awarded an estimated $1bn phosphate rail track doubling deal. Build a multi-agent conversational system using Microsoft AutoGen where Logistics Agents, Track Operators, and Freight Schedulers negotiate train dispatching schedules. Increase throughput efficiency by 12% without track collision risks.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement conversational multi-agent collaboration with Microsoft AutoGen to optimize freight train schedules on Saudi Arabia's doubled phosphate rail corridor.
How work is evaluated
Evaluates freight train schedule output generated through AutoGen negotiation for safety and throughput.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zero_collision_check
Verifies safe headway separation between consecutive trains
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
throughput_efficiency
Total metric tons transported per day on schedule • target: 85000 • range: 0-100000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct a group conversation in AutoGen with `AssistantAgent` and `UserProxyAgent` roles.
Formulate custom consensus rules for train headway and station pass loops.
Incorporate rail network constraints for heavy mineral transport.
Validate autonomous negotiation outcomes against physical safety thresholds.
Reference links and supporting material
Train movement and track topology data for Saudi Arabia's North-South mining railway network.
How this agent runs
Evaluates freight train schedule output generated through AutoGen negotiation for safety and throughput.
Challenge input
JSON containing list of train demand, origin, destination, and speed profile
AutoGen
Enables multi-agent conversational negotiation.
Evaluated output
JSON schedule with departure times and siding reservations
- Verifies safe headway separation between consecutive trains
- Total metric tons transported per day on schedule • target: 85000 • range: 0-100000
- Benchmark: MultiAgentRailBench
- Throughput Efficiency target: 85000
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- AutoGen · Required
- RAI · Optional
- Decipher AI · Optional
Evaluation contract
- zero_collision_check · Weight 1
- throughput_efficiency · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.