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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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What you are building

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

How work is evaluated

Evaluation

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

Datasets

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

How submissions are scored

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

  • 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

Dataset notes

Packaging line component wear logs and operational shift schedules.

Challenge setup

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

Evaluation checks
  • Ensure scheduled service occurs before estimated RUL depletion
  • Percentage cost savings over run-to-failure strategy • target: 20 • range: 0-100
Configured evaluation evidence
  • Benchmark: Predictive Maintenance Scheduling Suite (PDM-SCHED-2025)
  • Cost Reduction Score target: 20
  • 1 public reference case
Configured runtime
  • 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

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

Evaluation contract

  • breakdown_prevention_check · Weight 1
  • cost_reduction_score · Weight 1

Metrics

  • cost_reduction_score · Target 20

Benchmarks

  • Predictive Maintenance Scheduling Suite (PDM-SCHED-2025)
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

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

    Output: JSON scheduled maintenance window timestamp and availability score

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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