Orchestrate Plug-and-Produce MTP Factory Rescheduling with LangChain and OpenRouter
Modular Type Package (MTP) standardization enables modular process plants to reconfigure production modules dynamically for 'plug-and-produce' automation. However, when a modular equipment asset fails or a new order arrives, plant schedulers must dynamically reallocate process modules without violating material, labor, or machine setup constraints. In this challenge, you will construct a dynamic factory rescheduling agent using LangChain (LangGraph) and OpenRouter for multi-model model orchestration. The agent will monitor MTP module state descriptions, re-route process execution workflows, and produce optimized work order sequences to minimize factory downtime.
What you are building
Build an MTP dynamic factory scheduling graph using LangChain/LangGraph and OpenRouter.
How work is evaluated
Evaluates the agent's graph execution in producing valid, constraint-compliant dynamic factory rescheduling outputs upon module failure events.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
schedule_feasibility_test
Checks that output schedule contains no double-booked MTP modules.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
scheduling_efficiency
Measures total throughput loss minimization score • target: 88 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Build a stateful multi-agent workflow with LangGraph for modular plant scheduling.
Integrate OpenRouter as a unified multi-LLM routing layer for constraint analysis.
Parse MTP (Module Type Package) state manifests and operational capabilities.
Solve scheduling allocation problems under material and module availability constraints.
Resources
Modular Type Package (MTP) manifest files, module state transition tables, and work order scheduling queues.
Challenge setup
Evaluates the agent's graph execution in producing valid, constraint-compliant dynamic factory rescheduling outputs upon module failure events.
Challenge input
JSON with list of active MTP modules, active work order queue, and failed module alert
LangChain
Offers structured tool integration and stateful graph management via LangGraph.
OpenRouter
Provides unified API access to multiple LLM providers for reasoning and scheduling.
Evaluated output
JSON containing rescheduled_timeline, active_modules, and bottleneck_warning
- Checks that output schedule contains no double-booked MTP modules.
- Measures total throughput loss minimization score • target: 88 • range: 0-100
- Benchmark: Operations Research & Production Scheduling Benchmark
- Scheduling Efficiency target: 88
- 1 public reference case
- 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
- Langchain · Required
- LangChain · Optional
- OpenRouter · Optional
- Langchain · Required
- LangChain · Optional
- OpenRouter · Optional
Evaluation contract
- schedule_feasibility_test · Weight 1
- scheduling_efficiency · Weight 1
Metrics
- scheduling_efficiency · Target 88
Benchmarks
- Operations Research & Production Scheduling Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with list of active MTP modules, active work order queue, and failed module alert
Output: JSON containing rescheduled_timeline, active_modules, and bottleneck_warning
Execution harness: Python
Evaluation tools
- LangChain · agent_framework
- OpenRouter · llm_gateway
Recipe freeze marker
No recipe freeze time is recorded.