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Challenge

Multi-Agent Food Processing Line Rescheduler with AutoGen

Custom high-mix food production lines face frequent real-time bottlenecks due to material mixing delay and changeover constraints. Build a multi-agent conversational rescheduling system using AutoGen with specialized line coordinator, inventory, and machine maintenance agents. Lower schedule delay penalties by 25% while respecting all continuous hygiene sanitation intervals.

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Challenge brief

What you are building

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

Develop an AutoGen multi-agent system where dedicated roles negotiate real-time schedule adjustments for custom food processing lines.

Delivery guide

How work is evaluated

Evaluation

Evaluates schedule efficiency and sanitation rule adherence during dynamic disruption events.

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

washdown_rule

Ensure required allergen sanitation step is not skipped

Binary check

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

Dimension 2

delay_penalty_score

Normalized score measuring total late delivery penalty (lower is better) • target: 15 • 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

  • Set up an AutoGen GroupChat orchestrating LineManager, InventoryAgent, and SanitationAgent

  • Formulate constraint rules for continuous frying, freezing, and allergen line washdowns

  • Demonstrate real-time dynamic schedule adjustment when an unpredicted batch delay occurs

Resources and assets

Reference links and supporting material

Dataset notes

Batch order schedules and sanitation constraint rules from a multi-line poultry product plant.

How this agent runs

Evaluates schedule efficiency and sanitation rule adherence during dynamic disruption events.

Challenge input

JSON containing current schedule and disruption event details

AutoGen

Enables multi-agent conversational negotiation across disparate domain rules.

Evaluated output

JSON modified schedule with zero sanitation interval violations

Checks for
  • Ensure required allergen sanitation step is not skipped
  • Normalized score measuring total late delivery penalty (lower is better) • target: 15 • range: 0-100
Proof of success
  • Benchmark: Industrial Batch Scheduling Suite (BATCH-SCHED-2025)
  • Delay Penalty Score target: 15
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • AutoGen · Required
  • Microsoft AutoGen · Optional

Evaluation contract

  • washdown_rule · Weight 1
  • delay_penalty_score · Weight 1

Recipe state

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

Frequently Asked Questions about Multi-Agent Food Processing Line Rescheduler with AutoGen