# A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

arXiv: https://arxiv.org/abs/2212.10936 · HTML: https://mlsanity.com/paper/2212.10936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 64 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)

## Built on by (most cited first, 2 of 2)

- [SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions](https://mlsanity.com/paper/2606.17266/) (2026)
- [APEX: An Extensible Model for Agent-Assisted Production Scheduling](https://mlsanity.com/paper/2609.33430/) (2026)

## Abstract (arXiv)

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.