Represent products, machines and scheduling choices in a research simulator inspired by earlier manufacturing research.
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.
The working flow
Define a scenario
Select a strategy
Run the simulation
Review behaviour
These capabilities describe source and research scope. Deployment, integration, security and outcome claims need use-case-specific validation. Concept illustrations are not product screenshots.
Explore the technical record and evidence
Business ↔ technology bridge · Explore scheduling decisions before changing a production line.
A flexible job-shop simulation exploring product and machine agents, scheduling strategies, reinforcement learning and genetic algorithms.
A flexible job-shop simulation exploring product and machine agents, scheduling strategies, reinforcement learning and genetic algorithms.
This is a portfolio and source interpretation—not a customer, deployment, certification, or outcome claim.
01
Business pressure
Industrial product teams, manufacturing researchers and operations specialists comparing scheduling approaches. Explore scheduling decisions before changing a production line.
02
Product decision
Represent products, machines and scheduling choices in a research simulator inspired by earlier manufacturing research.
03
Technical record
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
Evidence return
Review the experiment against its intended use: Use a representative scenario to frame a scheduling experiment. No deployed scheduler, plant improvement or production control is claimed.
Source evidence
What the source actually establishes
A flexible job-shop research simulator with product and machine agents, scheduling strategies, reinforcement-learning and genetic-algorithm paths, and React/FastAPI interfaces.
Maturity
Manufacturing simulation research
Evidence review
Source snapshot through
Architecture and product decisions
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.
Inspectable evidence
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.
Claim boundary
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.
Repository evidence is not client, deployment, certification, or outcome evidence.
Bring your next product decision.
Use a representative scenario to frame a scheduling experiment. No deployed scheduler, plant improvement or production control is claimed.
