Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning

• URL: http://arxiv.org/abs/2305.05119v2
• Date: Sat, 17 Jun 2023 05:33:23 GMT
• Title: Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning
• Authors: Runqing Wang, Gang Wang, Jian Sun, Fang Deng and Jie Chen
• Abstract summary: In flexible job shop scheduling problem (FJSP), operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. This paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making.
• Score: 73.19312285906891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decisionmaking. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks.

Related papers

• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [50.485788083202124]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks. We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing [11.509669981978874]
  In smart manufacturing systems, flexible job-shop scheduling with transportation constraints is essential to optimize solutions for maximizing productivity. Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a scale generalization challenge. We introduce a novel graph-based DRL method, named the Heterogeneous Graph Scheduler (HGS)
  arXiv  Detail & Related papers  (2024-02-14T06:49:23Z)
• Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning [92.31528918811007]
  We propose a simple and efficient reinforcement learning framework -- namely, reinforcement learning for presolve (RL4Presolve) -- to tackle (P1)-(P3) simultaneously. Experiments on two solvers and eight benchmarks (real-world and synthetic) demonstrate that RL4Presolve significantly and consistently improves the efficiency of solving large-scale LPs.
  arXiv  Detail & Related papers  (2023-10-18T09:51:59Z)
• Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions [2.4541568670428915]
  Machine scheduling aims to optimize job assignments to machines while adhering to manufacturing rules and job specifications. Deep Reinforcement Learning (DRL), a key component of artificial general intelligence, has shown promise in various domains like gaming and robotics. This paper offers a comprehensive review and comparison of DRL-based approaches, highlighting their methodology, applications, advantages, and limitations.
  arXiv  Detail & Related papers  (2023-10-04T22:45:09Z)
• Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem [67.32731657297377]
  Traveling Salesman Problem (TSP) is a classic routing optimization problem originally arising in the domain of transportation and logistics. Recently, Deep Reinforcement Learning has been increasingly employed to solve TSP due to its high inference efficiency. We propose a novel end-to-end DRL approach, referred to as Pointerformer, based on multi-pointer Transformer.
  arXiv  Detail & Related papers  (2023-04-19T03:48:32Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Two-Stage Learning For the Flexible Job Shop Scheduling Problem [18.06058556156014]
  This paper investigates the potential of using a deep learning framework to generate fast and accurate approximations for the Flexible Job-shop Scheduling Problem. In particular, this paper proposes a two-stage learning framework that explicitly models the hierarchical nature of FJSP decisions. Results show that 2SL-FJSP can generate high-quality solutions in milliseconds, outperforming a state-of-the-art reinforcement learning approach.
  arXiv  Detail & Related papers  (2023-01-23T20:23:35Z)
• 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)
• Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method [44.4747903763245]
  The Jobs shop Scheduling Problem (JSP) is a canonical optimization problem that is routinely solved for a variety of industrial purposes. The problem is NP-hard and computationally challenging even for medium-sized instances. This paper explores a deep learning approach to deliver efficient and accurate approximations to the problem.
  arXiv  Detail & Related papers  (2021-10-12T21:15:19Z)
• 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.