Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem

• URL: http://arxiv.org/abs/2309.15517v2
• Date: Tue, 3 Oct 2023 01:28:16 GMT
• Title: Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem
• Authors: Kuo-Hao Ho, Ruei-Yu Jheng, Ji-Han Wu, Fan Chiang, Yen-Chi Chen, Yuan-Yu Wu, I-Chen Wu
• Abstract summary: Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing. In this paper, we propose a new approach, named residual scheduling, to solving/FJSP. Our approach even reaches zero gap for 49 among 50 instances whose job numbers are more than 150 on 20 machines.
• Score: 8.398387430247201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes important to develop efficient heuristics to solve JSP/FJSP. A kind of method of solving scheduling problems is construction heuristics, which constructs scheduling solutions via heuristics. Recently, many methods for construction heuristics leverage deep reinforcement learning (DRL) with graph neural networks (GNN). In this paper, we propose a new approach, named residual scheduling, to solving JSP/FJSP. In this new approach, we remove irrelevant machines and jobs such as those finished, such that the states include the remaining (or relevant) machines and jobs only. Our experiments show that our approach reaches state-of-the-art (SOTA) among all known construction heuristics on most well-known open JSP and FJSP benchmarks. In addition, we also observe that even though our model is trained for scheduling problems of smaller sizes, our method still performs well for scheduling problems of large sizes. Interestingly in our experiments, our approach even reaches zero gap for 49 among 50 JSP instances whose job numbers are more than 150 on 20 machines.

Related papers

• Learning to Solve Job Shop Scheduling under Uncertainty [1.3002317221601185]
  Job-Shop Scheduling Problem (JSSP) is a optimization problem where tasks need to be scheduled on machines in order to minimize criteria such as makespan or delay. This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions.
  arXiv  Detail & Related papers  (2024-03-04T08:38:55Z)
• Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning [73.19312285906891]
  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.
  arXiv  Detail & Related papers  (2023-05-09T01:35:48Z)
• A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping [0.0]
  JSSP falls into the category of NP-hard COP, in which solving the problem through exhaustive search becomes unfeasible. In recent years, the research towards using DRL to solve COP has gained interest and has shown promising results in terms of solution quality and computational efficiency. In particular, we employ the PPO algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs.
  arXiv  Detail & Related papers  (2023-02-27T16:45:04Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories [72.15369769265398]
  Machine learning has emerged as a promising paradigm for branching. We propose retro branching; a simple yet effective approach to RL for branching. We outperform the current state-of-the-art RL branching algorithm by 3-5x and come within 20% of the best IL method's performance on MILPs with 500 constraints and 1000 variables.
  arXiv  Detail & Related papers  (2022-05-28T06:08:07Z)
• An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents [1.3812010983144802]
  We propose a deep reinforcement learning methodology for the job shop scheduling problem (JSSP) The aim is to build up a greedy-like able to learn on some distribution of JSSP instances, different in the number of jobs and machines. As expected, the model can generalize, to some extent, to larger problems or instances originated by a different distribution from the one used in training.
  arXiv  Detail & Related papers  (2021-10-18T07:55:39Z)
• 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)
• Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical and Quantum Approaches [0.0]
  In this paper a use case is provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem (JSSP) The goal of the use case is to show how to create an optimized duty rooster for certain projects in a flexible organized machinery. The results of a classical solution based on CP and on an Annealing model are presented and discussed.
  arXiv  Detail & Related papers  (2021-09-10T12:28:51Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• 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.