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- What: operations research, queueing theory, stochastic modeling for pipeline architecture - Why: Leo/Cory brief — need disciplined approach to variable-load scaling Pentagon-Agent: Rio <2EA8DBCB-A29B-43E8-B726-45E571A1F3C8>
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1.9 KiB
| type | title | author | url | date | domain | format | status | tags | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 | unprocessed |
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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.