# Utilizing Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Under Sustainable Viewpoints

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/utilizing-multi-agent-deep-reinforcement-learning-for-flexible-job-shop/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jens Popper,William Motsch,Alexander David,Teresa Petzsche,Martin Ruskowski |
| Citations | 21 |
| DOI | 10.1109/iceccme52200.2021.9590925 |
| Fields | Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3213202163 |
| Type | conference-paper |
| Year | 2021 |

## Paper authors

- [William Motsch](https://scholariq.org/researchers/william-motsch/)

## Paper journal

- [2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)](https://scholariq.org/journals/2021-international-conference-on-electrical-computer-communications-and/)

## 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/)
- [Assembly Line Balancing Optimization](https://scholariq.org/topics/assembly-line-balancing-optimization/)

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