teleo-codex/inbox/archive/2023-00-00-sciencedirect-flexible-job-shop-scheduling-review.md

type	title	author	url	date	domain	format	status	tags	processed_by	processed_date	claims_extracted	extraction_model	extraction_notes
source	The Flexible Job Shop Scheduling Problem: A Review	ScienceDirect review article	https://www.sciencedirect.com/science/article/pii/S037722172300382X	2023-01-01	internet-finance	paper	processed	pipeline-architecture operations-research combinatorial-optimization job-shop-scheduling flexible-scheduling	rio	2026-03-11	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	anthropic/claude-sonnet-4.5	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