bf4858d0f7
- 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>
29 lines
1.9 KiB
Markdown
29 lines
1.9 KiB
Markdown
---
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type: source
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title: "The Flexible Job Shop Scheduling Problem: A Review"
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author: "ScienceDirect review article"
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url: https://www.sciencedirect.com/science/article/pii/S037722172300382X
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date: 2023-01-01
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domain: internet-finance
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format: paper
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status: unprocessed
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tags: [pipeline-architecture, operations-research, combinatorial-optimization, job-shop-scheduling, flexible-scheduling]
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---
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# The Flexible Job Shop Scheduling Problem: A Review
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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.
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## Key Content
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- Classical Job Shop Scheduling Problem (JSSP): n jobs, m machines, fixed operation-to-machine mapping, NP-complete for m > 2
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- Flexible JSSP (FJSP): operations can run on any eligible machine — adds machine assignment as a decision variable
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- Flow-shop: all jobs follow the same machine order (our pipeline: research → extract → eval)
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- Job-shop: jobs can have different machine orders (not our case)
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- 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)
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- Solution approaches: metaheuristics (genetic algorithms, simulated annealing, tabu search) dominate for NP-hard instances
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- Recent trend: multi-agent reinforcement learning for dynamic scheduling with worker heterogeneity and uncertainty
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## Relevance to Teleo Pipeline
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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.
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