A simulated shop floor
Represent products, machines and scheduling choices in a research simulator inspired by earlier manufacturing research.
全部项目
Decision logic and control flow
商业摘要已提供本语言版本。产品详细流程和技术证据以英语维护。
物理 AI 与机器人
FlexShop
在改动生产线之前探索排程决策。
紧急订单或不可用的设备会改变计划。在提议工厂改动之前，先在仿真中比较排程选项。
制造业产品团队和运筹学研究人员
制造仿真研究
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 | |
Compare how each ordering uses the available machine.
- 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.
- Engage now
- A scheduling experiment with explicit assumptions, comparison criteria and a next evaluation step.
- Inspect
- Manufacturing simulation research
- Flexible job-shop simulation and agent source.
- Still to validate
- Simulation results do not establish production scheduling performance.
- Use a representative scenario to frame a scheduling experiment. No deployed scheduler, plant improvement or production control is claimed.
源码审查日期
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 system 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.
Inspect the source, test conditions and operating limits in the technical record.
查看技术记录与证据
业务 ↔ 技术桥梁 · Physical AI capability experiment
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.
这是对项目组合与源代码的解读，并非客户、部署、认证或成果声明。
- 01
业务压力
A rush order or unavailable machine changes the plan. Compare the scheduling question in simulation before proposing a plant change.
- 02
产品决策
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
技术记录
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
证据回流
Inspect behaviour and failures. A toy ordering is not an optimisation result or evidence of a factory improvement.
仓库／运行时边界
本页解读的是独立项目仓库中的源代码。源代码存在，并不表示该项目作为服务运行在 hyperion-consulting.io 内。
访客及客户数据的 AI 处理限于通过受治理的欧盟端点使用 Mistral。所有者授权的 Helio、搜索及 Product Council 流程已启用。独立的公开来源编辑流程使用 Anthropic，并以受治理的 Mistral 作为备用；该流程不接收网站咨询、客户文档或访客对话。远程 AI 不可用时，确定性工具仍可使用。
源码证据
源码实际能够证明什么
A flexible job-shop research simulator with product and machine agents, scheduling strategies, reinforcement-learning and genetic-algorithm paths, and React/FastAPI interfaces.
详细技术快照以英文维护。
- 成熟度
- Manufacturing simulation research
- 证据审查
- 源码快照截至
产品与架构决策
- 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.
可核验的证据
- 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.
声明边界
- 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.
代码仓库证据不等同于客户、部署、认证或成果证据。