Skip to main content
Back to challenges
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.

EngineeringHosted by Vera
Sign in free to start
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

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

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.

Challenge setup

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

Evaluation checks
  • Ensure required allergen sanitation step is not skipped
  • Normalized score measuring total late delivery penalty (lower is better) • target: 15 • range: 0-100
Configured evaluation evidence
  • Benchmark: Industrial Batch Scheduling Suite (BATCH-SCHED-2025)
  • Delay Penalty Score target: 15
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python 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
  • AutoGen · Required
  • Microsoft AutoGen · Optional

Evaluation contract

  • washdown_rule · Weight 1
  • delay_penalty_score · Weight 1

Metrics

  • delay_penalty_score · Target 15

Benchmarks

  • Industrial Batch Scheduling Suite (BATCH-SCHED-2025)
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing current schedule and disruption event details

    Output: JSON modified schedule with zero sanitation interval violations

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-Agent Orchestrator

Recipe freeze marker

No recipe freeze time is recorded.

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