c6412f6832
Some checks are pending
Sync Graph Data to teleo-app / sync (push) Waiting to run
Pentagon-Agent: Ganymede <F99EBFA6-547B-4096-BEEA-1D59C3E4028A>
42 lines
3.1 KiB
Markdown
42 lines
3.1 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
|
|
tags: [pipeline-architecture, operations-research, combinatorial-optimization, job-shop-scheduling, flexible-scheduling]
|
|
processed_by: rio
|
|
processed_date: 2026-03-11
|
|
claims_extracted: ["hybrid-flow-shop-scheduling-with-simple-dispatching-rules-performs-within-5-10-percent-of-optimal-for-homogeneous-workers.md", "general-job-shop-scheduling-is-np-complete-for-more-than-two-machines.md"]
|
|
extraction_model: "anthropic/claude-sonnet-4.5"
|
|
extraction_notes: "Extracted two claims about scheduling problem complexity and tractability. The source is an operations research review that provides theoretical foundations for understanding pipeline coordination. Key insight: Teleo's pipeline is a hybrid flow-shop, which is computationally easier than general JSSP and can use simple dispatching rules effectively. No entities to extract — this is pure operations research theory with no companies, products, or decisions mentioned."
|
|
---
|
|
|
|
# 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.
|
|
|
|
|
|
## Key Facts
|
|
- Flow-shop: all jobs follow the same machine order
|
|
- Job-shop: jobs can have different machine orders
|
|
- Hybrid flow-shop: multiple machines at each stage, jobs follow same stage order
|
|
- Flexible JSSP adds machine assignment as decision variable on top of classical JSSP
|
|
- Recent trend in FJSP research: multi-agent reinforcement learning for dynamic scheduling with worker heterogeneity
|