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Challenge

Packaging Line Predictive Maintenance Scheduler with OpenAI Agents SDK

Inflexible fixed maintenance schedules cause premature component replacement or unexpected packaging line breakdown. Build an autonomous predictive maintenance scheduler using OpenAI Agents SDK that analyzes continuous wear models, vibration history, and active work orders. Reduce total maintenance overhead costs by 20% while maintaining 99% line availability.

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Challenge brief

What you are building

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

Build a predictive maintenance agent using OpenAI Agents SDK pattern to schedule dynamic component servicing actions.

Delivery guide

How work is evaluated

Evaluation

Evaluates optimization of maintenance schedules against remaining useful life estimates and production plans.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

breakdown_prevention_check

Ensure scheduled service occurs before estimated RUL depletion

Binary check

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

Dimension 2

cost_reduction_score

Percentage cost savings over run-to-failure strategy • target: 20 • range: 0-100

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

  • Configure OpenAI Agents SDK with function tools for remaining useful life (RUL) estimation

  • Formulate production window optimization constraints for scheduling maintenance outages

  • Validate maintenance schedule outputs against plant line availability targets

Resources and assets

Reference links and supporting material

Dataset notes

Packaging line component wear logs and operational shift schedules.

How this agent runs

Evaluates optimization of maintenance schedules against remaining useful life estimates and production plans.

Challenge input

JSON containing asset health state, RUL hours, and production shift schedule

OpenAI Agents SDK

Provides clean tool-calling agent abstraction for scheduling workflows.

Evaluated output

JSON scheduled maintenance window timestamp and availability score

Checks for
  • Ensure scheduled service occurs before estimated RUL depletion
  • Percentage cost savings over run-to-failure strategy • target: 20 • range: 0-100
Proof of success
  • Benchmark: Predictive Maintenance Scheduling Suite (PDM-SCHED-2025)
  • Cost Reduction Score target: 20
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Azure OpenAI Service · Optional
  • OpenAI o3 · Optional

Evaluation contract

  • breakdown_prevention_check · Weight 1
  • cost_reduction_score · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Packaging Line Predictive Maintenance Scheduler with OpenAI Agents SDK