A simulated shop floor
Represent products, machines and scheduling choices in a research simulator inspired by earlier manufacturing research.
Tous les projets
Illustrative walkthrough · not a product screenshot or deployment
Les descriptions produit actualisées et les dossiers techniques sont maintenus en anglais.
IA physique et robotique
FlexShop
Explore scheduling decisions before changing a production line.
A rush order or unavailable machine changes the plan. Compare the scheduling question in simulation before proposing a plant change.
Manufacturing product teams and operations researchers
Manufacturing simulation research
A scheduling question
| Resource | Candidate A | Candidate B |
|---|---|---|
| Machine 1 | Job A → Job B | Job B → Job A |
| Machine 2 | Unavailable in this fictional scenario | |
Illustrative ordering only. No durations, solver output or improvement is asserted.
- Scenario
- Fictional jobs and machine availability
- Change
- A machine becomes unavailable
- Comparison
- Two candidate job orderings
- Evidence needed
- Simulation configuration and observed behaviour
Explore the reasoning
Define jobs, machines and availability assumptions for a fictional or authorised scenario.
The decision behind the system
Which scheduling assumption should you test next?
A flexible job-shop simulation exploring product and machine agents, scheduling strategies, reinforcement learning and genetic algorithms.
FlexShop compares scheduling strategies in simulation. It is distinct from Auralink’s coordination model and from a deployed plant scheduler.
Compare strategies
Explore agent-based scheduling and learning or optimisation approaches against a defined simulation scenario.
Inspect the experiment
React and FastAPI surfaces connect the experiment interface to simulation logic rather than live plant control.
Read the complete illustrative walkthrough
Describe
Define jobs, machines and availability assumptions for a fictional or authorised scenario.
Choose
Choose the agent-based, learning or optimisation strategy to examine.
Simulate
Retain scenario and strategy configuration alongside the simulation output.
Compare
Inspect behaviour and failures. A toy ordering is not an optimisation result or evidence of a factory improvement.
One inspectable artefact
Flexible job-shop simulation and agent source.
A current boundary
Simulation results do not establish production scheduling performance.
How to start
Use a representative scenario to frame a scheduling experiment. No deployed scheduler, plant improvement or production control is claimed.
These are source and research records. Illustrative examples do not establish deployment, integration, certification or customer outcomes.
Explorer le dossier technique et les preuves
Pont métier ↔ technologie · Explore scheduling decisions before changing a production line.
Which scheduling assumption should you test next?
FlexShop compares scheduling strategies in simulation. It is distinct from Auralink’s coordination model and from a deployed plant scheduler.
Il s'agit d'une interprétation du portefeuille et des sources, et non d'une affirmation de client, déploiement, certification ou résultat.
- 01
Pression métier
A rush order or unavailable machine changes the plan. Compare the scheduling question in simulation before proposing a plant change.
- 02
Décision produit
Define jobs, machines and availability assumptions for a fictional or authorised scenario. Inspect behaviour and failures. A toy ordering is not an optimisation result or evidence of a factory improvement.
- 03
Dossier technique
Represent products, machines and scheduling choices in a research simulator inspired by earlier manufacturing research. Explore agent-based scheduling and learning or optimisation approaches against a defined simulation scenario. React and FastAPI surfaces connect the experiment interface to simulation logic rather than live plant control.
- 04
Retour des preuves
Inspect behaviour and failures. A toy ordering is not an optimisation result or evidence of a factory improvement.
Preuves issues du code source
Ce que le code source permet réellement d’établir
A flexible job-shop research simulator with product and machine agents, scheduling strategies, reinforcement-learning and genetic-algorithm paths, and React/FastAPI interfaces.
Le relevé technique détaillé est maintenu en anglais.
- Maturité
- Manufacturing simulation research
- Revue des preuves
- État du code au
Décisions produit et architecture
- Model scheduling choices in simulation before considering live plant integration.
- Separate product and machine agents from the strategy used to schedule work.
- Keep experimental behaviour and industrial performance claims distinct.
Éléments vérifiables
- Flexible job-shop simulation and agent source.
- Scheduling, reinforcement-learning and genetic-algorithm experiment paths.
- React interface, FastAPI boundary and documented lineage to earlier manufacturing research.
Limites des affirmations
- Simulation results do not establish production scheduling performance.
- No deployed plant controller, customer installation or measured factory improvement is claimed.
- Scenario assumptions, data and strategy comparison require use-case-specific evaluation.
Les preuves du dépôt ne constituent pas des preuves client, de déploiement, de certification ou de résultats.
Work with Hyperion
Compare a scheduling scenario.
A scheduling experiment with explicit assumptions, comparison criteria and a next evaluation step.
Bring a short description of your situation, constraints and decision. We agree the scope, responsibilities and quote before work begins.