---
type: claim
domain: internet-finance
description: "Operations research shows simple priority rules suffice for pipeline architectures with sequential stages and uniform worker capability"
confidence: likely
source: "ScienceDirect review article on Flexible Job Shop Scheduling Problem, 2023"
created: 2026-03-11
---

# Hybrid flow-shop scheduling with simple dispatching rules performs within 5-10 percent of optimal for homogeneous workers

For pipeline architectures where all work flows through the same sequence of stages (hybrid flow-shop), and workers within each stage have similar capabilities, simple priority dispatching rules like shortest-job-first or FIFO within priority classes achieve near-optimal performance without requiring complex metaheuristic optimization.

This matters for Teleo's pipeline architecture (research → extract → eval) because it means we don't need sophisticated scheduling algorithms. The computational complexity that makes general Job Shop Scheduling Problems NP-hard doesn't apply when:
1. All sources follow the same stage sequence (flow-shop property)
2. Multiple workers exist at each stage but are roughly interchangeable
3. The number of stages is small (3 in our case)

The review shows that for hybrid flow-shops with these properties, metaheuristics (genetic algorithms, simulated annealing, tabu search) provide only marginal improvements over well-designed dispatching rules, while adding significant implementation complexity.

## Evidence

The ScienceDirect review distinguishes several scheduling problem types:
- **Classical JSSP**: n jobs, m machines, fixed operation-to-machine mapping, NP-complete for m > 2
- **Flexible JSSP**: operations can run on any eligible machine from a set
- **Flow-shop**: all jobs follow the same machine order
- **Hybrid flow-shop**: multiple machines at each stage, jobs follow same stage order but can use any machine within a stage

For hybrid flow-shop problems specifically, the review notes that "simple priority dispatching rules (shortest-job-first, FIFO within priority classes) perform within 5-10% of optimal" when workers within stages are homogeneous.

The review also documents that recent trends focus on "multi-agent reinforcement learning for dynamic scheduling with worker heterogeneity and uncertainty" — but this is for cases where worker capabilities differ significantly, which is not the primary bottleneck in our pipeline.

## Implications for Teleo Pipeline

Our pipeline is definitionally a hybrid flow-shop:
- Three sequential stages: research → extract → eval
- Multiple AI agents can work at each stage
- All sources flow through the same stage sequence
- Workers within each stage have similar (though not identical) capabilities

This means our scheduling problem is computationally tractable with simple rules rather than requiring optimization algorithms designed for general JSSP.

---

Relevant Notes:
- domains/internet-finance/_map

Topics:
- domains/internet-finance/_map