{"title":"Learning to schedule (L2S): adaptive job shop scheduling using double deep Q network","authors":"Abebaw Degu Workneh, Maha Gmira","doi":"10.1080/23080477.2023.2187528","DOIUrl":null,"url":null,"abstract":"ABSTRACT The stochasticity and randomly changing nature of the production environment posed a significant challenge in developing real-time responsive scheduling solutions. Many previous scheduling solutions assumed static environments, user-anticipated, and hand-crafted dynamic scenarios. However, real-world production environment events are random and unpredictable. This study considers Job Shop Scheduling Problem (JSSP) as an iterative decision-making problem, and Deep Reinforcement Learning (DRL)-based solution is designed to address these challenges. A deep neural network is utilized for function approximation, and the input feature vectors are extracted iteratively to be used in the sequential decision-making process. The production states are expressed with randomly changing feature vectors of each job’s operations and the corresponding machines. This work proposes Double Deep Q Network (DDQN) methods to train the model. Results are evaluated on the renowned OR-Library benchmark problems. The evaluation result indicates that the proposed approach is comparative in benchmark problems, and the scheduling agent can get good results in unseen instances with an average of 94.86% of the scheduling score. Graphical abstract","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2187528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACT The stochasticity and randomly changing nature of the production environment posed a significant challenge in developing real-time responsive scheduling solutions. Many previous scheduling solutions assumed static environments, user-anticipated, and hand-crafted dynamic scenarios. However, real-world production environment events are random and unpredictable. This study considers Job Shop Scheduling Problem (JSSP) as an iterative decision-making problem, and Deep Reinforcement Learning (DRL)-based solution is designed to address these challenges. A deep neural network is utilized for function approximation, and the input feature vectors are extracted iteratively to be used in the sequential decision-making process. The production states are expressed with randomly changing feature vectors of each job’s operations and the corresponding machines. This work proposes Double Deep Q Network (DDQN) methods to train the model. Results are evaluated on the renowned OR-Library benchmark problems. The evaluation result indicates that the proposed approach is comparative in benchmark problems, and the scheduling agent can get good results in unseen instances with an average of 94.86% of the scheduling score. Graphical abstract
期刊介绍:
Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials