58c5d59380
- What: grand-strategy claim that hybrid flow-shop pipelines with few stages and homogeneous workers achieve near-optimal throughput with simple dispatching rules (within 5-10% of optimal), making metaheuristic optimization unnecessary - Why: ScienceDirect FJSP review (2023) establishes this bound from OR literature; directly relevant to Teleo pipeline architecture (research → extract → eval is a hybrid flow-shop) - Connections: links to [[designing coordination rules is categorically different from designing coordination outcomes]] and [[mechanism design enables incentive-compatible coordination]] in foundations/collective-intelligence - Note: source was tagged internet-finance but contains no internet-finance claims; claim correctly placed in grand-strategy; domain mismatch flagged in archive Pentagon-Agent: Rio <2EA8DBCB-A29B-43E8-B726-45E571A1F3C8>
35 lines
2.4 KiB
Markdown
35 lines
2.4 KiB
Markdown
---
|
|
type: source
|
|
title: "The Flexible Job Shop Scheduling Problem: A Review"
|
|
author: "ScienceDirect review article"
|
|
url: https://www.sciencedirect.com/science/article/pii/S037722172300382X
|
|
date: 2023-01-01
|
|
domain: internet-finance
|
|
format: paper
|
|
status: processed
|
|
processed_by: rio
|
|
processed_date: 2026-03-12
|
|
claims_extracted:
|
|
- core/grand-strategy/hybrid-flow-shop-pipelines-with-few-stages-and-homogeneous-workers-perform-within-5-10-percent-of-optimal-with-simple-priority-dispatching-rules.md
|
|
enrichments: []
|
|
notes: "Source tagged internet-finance but contains no internet-finance claims. Single claim extracted to grand-strategy — scheduling theory applied to pipeline architecture. Domain mismatch in source metadata."
|
|
tags: [pipeline-architecture, operations-research, combinatorial-optimization, job-shop-scheduling, flexible-scheduling]
|
|
---
|
|
|
|
# The Flexible Job Shop Scheduling Problem: A Review
|
|
|
|
Comprehensive review of the Flexible Job Shop Scheduling Problem (FJSP) — a generalization of classical JSSP where operations can be processed on any machine from a set of eligible machines.
|
|
|
|
## Key Content
|
|
|
|
- Classical Job Shop Scheduling Problem (JSSP): n jobs, m machines, fixed operation-to-machine mapping, NP-complete for m > 2
|
|
- Flexible JSSP (FJSP): operations can run on any eligible machine — adds machine assignment as a decision variable
|
|
- Flow-shop: all jobs follow the same machine order (our pipeline: research → extract → eval)
|
|
- Job-shop: jobs can have different machine orders (not our case)
|
|
- Hybrid flow-shop: multiple machines at each stage, jobs follow same stage order but can use any machine within a stage (THIS is our model)
|
|
- Solution approaches: metaheuristics (genetic algorithms, simulated annealing, tabu search) dominate for NP-hard instances
|
|
- Recent trend: multi-agent reinforcement learning for dynamic scheduling with worker heterogeneity and uncertainty
|
|
|
|
## Relevance to Teleo Pipeline
|
|
|
|
Our pipeline is a **hybrid flow-shop**: three stages (research → extract → eval), multiple workers at each stage, all sources flow through the same stage sequence. This is computationally easier than general JSSP. Key insight: for a hybrid flow-shop with relatively few stages and homogeneous workers within each stage, simple priority dispatching rules (shortest-job-first, FIFO within priority classes) perform within 5-10% of optimal. We don't need metaheuristics — we need good dispatching rules.
|