Novel Multi-Agent Action Masked Deep Reinforcement Learning for General Industrial Assembly Lines Balancing Problems

• URL: http://arxiv.org/abs/2507.16635v1
• Date: Tue, 22 Jul 2025 14:34:36 GMT
• Title: Novel Multi-Agent Action Masked Deep Reinforcement Learning for General Industrial Assembly Lines Balancing Problems
• Authors: Ali Mohamed Ali, Luca Tirel, Hashim A. Hashim,
• Abstract summary: This paper introduces a novel mathematical model of a generic industrial assembly line formulated as a Markov Decision Process (MDP)<n>The proposed model is employed to create a virtual environment for training Deep Reinforcement Learning (DRL) agents to optimize task and resource scheduling.
• Score: 1.8434042562191815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards, prevent project constraint violations, and achieve cost-effective operations. While exact solutions to such challenges can be obtained through Integer Programming (IP), the dependence of the search space on input parameters often makes IP computationally infeasible for large-scale scenarios. Heuristic methods, such as Genetic Algorithms, can also be applied, but they frequently produce suboptimal solutions in extensive cases. This paper introduces a novel mathematical model of a generic industrial assembly line formulated as a Markov Decision Process (MDP), without imposing assumptions on the type of assembly line a notable distinction from most existing models. The proposed model is employed to create a virtual environment for training Deep Reinforcement Learning (DRL) agents to optimize task and resource scheduling. To enhance the efficiency of agent training, the paper proposes two innovative tools. The first is an action-masking technique, which ensures the agent selects only feasible actions, thereby reducing training time. The second is a multi-agent approach, where each workstation is managed by an individual agent, as a result, the state and action spaces were reduced. A centralized training framework with decentralized execution is adopted, offering a scalable learning architecture for optimizing industrial assembly lines. This framework allows the agents to learn offline and subsequently provide real-time solutions during operations by leveraging a neural network that maps the current factory state to the optimal action. The effectiveness of the proposed scheme is validated through numerical simulations, demonstrating significantly faster convergence to the optimal solution compared to a comparable model-based approach.

Related papers

• LLM-guided Chemical Process Optimization with a Multi-Agent Approach [5.417632175667162]
  Chemical process optimization is crucial to maximize production efficiency and economic performance.<n>Traditional methods, including gradient-based, evolutionary algorithms, and parameter grid searches, become impractical when operating constraints are ill-defined or unavailable.<n>We present a multi-agent framework of large language model (LLM) agents that autonomously infer operating constraints from minimal process descriptions.
  arXiv  Detail & Related papers  (2025-06-26T01:03:44Z)
• Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models [50.19188692497892]
  Traditional alignment methods often require retraining large pretrained models.<n>We propose a novel textitResidual Alignment Model (textitRAM) that formalizes the alignment process as a type of importance sampling.<n>We develop a resampling algorithm with iterative token-level decoding to address the common first-token latency issue in comparable methods.
  arXiv  Detail & Related papers  (2025-05-26T08:53:02Z)
• Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks [2.1111289252277197]
  Mixed Linear Programs (MILPs) are essential tools for solving planning and scheduling problems across critical industries such as construction, manufacturing, and logistics.<n>We present a learning-based framework that leverages Behavior Cloning (BC) and Reinforcement Learning (RL) to train Graph Neural Networks (GNNs)<n>Our method reduces optimization time and variance compared to traditional techniques while maintaining solution quality and feasibility.
  arXiv  Detail & Related papers  (2025-05-17T01:31:53Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models.<n>Controlled Decoding provides a mechanism for aligning a model at inference time without retraining.<n>We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning [0.08192907805418582]
  Current approaches either yield subpar results when using pretrained models without task-specific adaptation, or require substantial computational resources for fine-tuning.<n>We propose a novel method for adapting foundational, multimodal embeddings to downstream tasks, without the need of expensive fine-tuning processes.
  arXiv  Detail & Related papers  (2025-02-04T06:30:12Z)
• Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization [6.713974813995327]
  We present MEMENTO, an approach that leverages memory to improve the adaptation of neural solvers at time. We successfully train all RL auto-regressive solvers on large instances, and show that MEMENTO can scale and is data-efficient. Overall, MEMENTO enables to push the state-of-the-art on 11 out of 12 evaluated tasks.
  arXiv  Detail & Related papers  (2024-06-24T08:18:19Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling [0.0]
  This article presents a memetic algorithm with applying deep reinforcement learning (DRL) to 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 manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration.
  arXiv  Detail & Related papers  (2022-12-21T11:24:32Z)
• Adaptive Serverless Learning [114.36410688552579]
  We propose a novel adaptive decentralized training approach, which can compute the learning rate from data dynamically. Our theoretical results reveal that the proposed algorithm can achieve linear speedup with respect to the number of workers. To reduce the communication-efficient overhead, we further propose a communication-efficient adaptive decentralized training approach.
  arXiv  Detail & Related papers  (2020-08-24T13:23:02Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.