A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

TL;DR

This paper introduces a novel memetic algorithm enhanced with deep reinforcement learning to efficiently solve complex, real-world production scheduling problems involving multiple resources, automation, and multi-criteria optimization.

Contribution

It presents a new integrated framework combining a memetic algorithm, deep reinforcement learning, and discrete event simulation for complex industrial scheduling tasks.

Findings

The framework produces feasible schedules efficiently for real-world data.

DRL-enhanced approach outperforms traditional methods in fewer iterations.

Balanced optimization of makespan and total tardiness achieved.

Abstract

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.