# Simultaneous Production and AGV Scheduling using Multi-Agent Deep Reinforcement Learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/simultaneous-production-and-agv-scheduling-using-multi-agent-deep-reinforcement/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jens Popper,Vassilios Yfantis,Martin Ruskowski |
| Citations | 32 |
| DOI | 10.1016/j.procir.2021.11.257 |
| Fields | Engineering |
| Open Access | true |
| OA Status | diamond |
| OA URL | https://doi.org/10.1016/j.procir.2021.11.257 |
| OpenAlex ID | https://openalex.org/W3215999497 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Martin Ruskowski](https://scholariq.org/researchers/martin-ruskowski/)

## Paper journal

- [Procedia CIRP](https://scholariq.org/journals/procedia-cirp/)

## Paper primary topic

- [Scheduling and Optimization Algorithms](https://scholariq.org/topics/scheduling-and-optimization-algorithms/)

## Paper topics

- [Scheduling and Optimization Algorithms](https://scholariq.org/topics/scheduling-and-optimization-algorithms/)
- [Advanced Manufacturing and Logistics Optimization](https://scholariq.org/topics/advanced-manufacturing-and-logistics-optimization/)
- [Flexible and Reconfigurable Manufacturing Systems](https://scholariq.org/topics/flexible-and-reconfigurable-manufacturing-systems/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.