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Challenge

Real-Time Port Congestion and Vessel Queue Simulation Engine

Terminal expansions like the Seattle-Tacoma 50% capacity lift require continuous real-time queue management algorithms to prevent anchorages from backing up during peak ocean freight arrival spikes. In this challenge, you will implement a discrete-event port queue simulator using Vercel AI SDK in TypeScript. The engine monitors incoming vessel streams, predicts berth bottleneck points, dynamically allocates container yard slots, and streams real-time status updates to port operators via AI SDK UI streaming hooks.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a real-time port vessel queue simulation and berth planner using Vercel AI SDK in TypeScript.

Delivery guide

How work is evaluated

Evaluation

Evaluates queue simulation mathematical accuracy and stream payload structure.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

queue_formula_accuracy

Simulated average wait time must match analytical M/M/c queue target.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

stream_chunk_latency_ms

Latency between dynamic stream frame broadcasts • target: 25 • range: 1-200

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement Vercel AI SDK streaming primitives (`streamText`, `createDataStreamResponse`)

  • Simulate M/M/c port terminal queue models in TypeScript

  • Predict vessel waiting times under fluctuating terminal gang capacity

  • Stream dynamic queue status objects to web dashboard interfaces

Resources and assets

Reference links and supporting material

Dataset notes

Vessel arrival log dataset containing AIS timestamp streams, TEU load counts, and terminal berth specifications.

Challenge setup

Evaluates queue simulation mathematical accuracy and stream payload structure.

Challenge input

JSON arrival stream with ETA timestamps and container counts

AI SDK

Required framework for building streaming simulation tools in TypeScript.

Evaluated output

JSON summary with average wait time, utilization rate, and dynamic berth schedule

Evaluation checks
  • Simulated average wait time must match analytical M/M/c queue target.
  • Latency between dynamic stream frame broadcasts • target: 25 • range: 1-200
Configured evaluation evidence
  • Benchmark: TS-Sim-Bench
  • Stream Chunk Latency Ms target: 25
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Vercel AI SDK · Required
  • Zep · Optional
  • TensorBoard · Optional
Orchestration
  • Vercel AI SDK · Required

Evaluation contract

  • queue_formula_accuracy · Weight 1
  • stream_chunk_latency_ms · Weight 1

Metrics

  • stream_chunk_latency_ms · Target 25

Benchmarks

  • TS-Sim-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON arrival stream with ETA timestamps and container counts

    Output: JSON summary with average wait time, utilization rate, and dynamic berth schedule

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · TypeScript AI Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Real-Time Port Congestion and Vessel Queue Simulation Engine