Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organized as a job shop, which ultimately results in the Job Shop Scheduling Problem (JSSP). The JSSP is addressed with a focus on extending traditional models to better represent the complexities of real-world production environments. Existing approaches frequently fail to consider critical factors such as machine setup times or varying batch sizes. A concept for a model is proposed that provides a higher level of information detail to enhance scheduling accuracy and efficiency. The concept introduces the integration of DRL for production planning, particularly suited to batch production industries such as the furniture industry. The model extends traditional approaches to JSSPs by including job volumes, buffer management, transportation times, and machine setup times. This enables more precise forecasting and analysis of production flows and processes, accommodating the variability and complexity inherent in real-world manufacturing processes. The RL agent learns to optimize scheduling decisions. It operates within a discrete action space, making decisions based on detailed observations. A reward function guides the agent's decision-making process, thereby promoting efficient scheduling and meeting production deadlines. Two integration strategies for implementing the RL agent are discussed: episodic planning, which is suitable for low-automation environments, and continuous planning, which is ideal for highly automated plants. While episodic planning can be employed as a standalone solution, the continuous planning approach necessitates the integration of the agent with ERP and Manufacturing Execution Systems. This integration enables real-time adjustments to production schedules based on dynamic changes.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 31 references that are in the map. First posted on arXiv 18 Sep 2024.
Builds on
8 of 31 references.
References · 8
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
- 2021Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learningcited by 52
- 2021A Reinforcement Learning Environment For Job-Shop Schedulingcited by 21
- 2021SOLO: Search Online, Learn Offline for Combinatorial Optimization Problemscited by 9
- 2021Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problemscited by 2
- 2021Artificial Intelligence Narratives: An Objective Perspective on Current Developmentscited by 1
- 2023A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swappingcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints
- 2024Learning to Solve Job Shop Scheduling under Uncertainty
- 2025An efficient deep reinforcement learning environment for flexible job-shop scheduling
- 2026PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem
- 2025Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
- 2026Variational Approach for Job Shop Scheduling
- 2024Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
- 2024Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning