Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize station assignment and collision-free paths jointly with PITO-L | Lifelong TAPF · Agent simulator, total idle time | total idle time | Helped |
| Use ITO-L rather than H(Q)-L | Lifelong TAPF · Industrial simulator, 250 agents | average workload: +9.01% | Helped |
| Use ITO-L rather than H(Q)-L | Lifelong TAPF · Industrial simulator, 300 agents | average workload: +11.97% | Helped |
| Use ITO-L rather than H(Q)-L | Lifelong TAPF · Industrial simulator, 350 agents | average workload: +4.86% | Helped |
| Use ITO-L instead of H(Inf)-L | Lifelong TAPF · Agent simulator, computation time per one-shot instance | computation time | Helped |
| Use smaller stride intervals W | Lifelong TAPF algorithms · Agent simulator, total idle time | total idle time | Helped |
| Use H(1)-L rather than other Hungarian variants | Lifelong TAPF · Agent simulator, total idle time | total idle time | Helped |
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