Fan Yang , Cheng Ren , Wei Liu , Mingxing Li , Ming Li
{"title":"航空维修中不确定计划、调度和执行的失序启用操作系统","authors":"Fan Yang , Cheng Ren , Wei Liu , Mingxing Li , Ming Li","doi":"10.1016/j.jmsy.2025.04.011","DOIUrl":null,"url":null,"abstract":"<div><div>Maintenance has long been a concern in manufacturing, both in the production and product-service phases. As a type of large product, aviation maintenance produces a collection of services to ensure that aircrafts or aircraft systems, components, and structures meet airworthiness standards. Planning, scheduling, and execution (PSE) is important for maintenance systems to optimize resource utilization and job sequencing through decision-making at different time cycles. However, stochastic uncertainty always exists, affecting the stability of the entire maintenance process. Therefore, in this study, which was inspired by operating systems (i.e., Windows, Android, etc.) for processing uncertain user actions with high efficiency, an out-of-order enabled operation system in aviation maintenance (OoO-AMOS) is designed to mitigate the influence of uncertainties that exist in the PSE procedure. Two key components, namely, thread manager and resource manager, are proposed at the kernel level of the OoO-AMOS. The concept of out-of-order (OoO) is deployed for the thread manager to dynamically select the optimal order sequence based on task dependencies and feasibility. A finite state machine (FSM) model is integrated as the operation validation mechanism to formulize the resource states and their transitions. Finally, a case study is conducted to evaluate the effectiveness of the proposed OoO-AMOS. The results show that OoO-AMOS presents significant advantages over traditional approaches. In uncertain environments, the total setup time was reduced by more than 55 %, whereas the maintenance makespan, average order tardiness, and hangar turnover rate achieved improvements of more than 22 %, 31 %, and 23 %, respectively.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 824-840"},"PeriodicalIF":12.2000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Out-of-order enabled operating system for uncertain planning, scheduling and execution in aviation maintenance\",\"authors\":\"Fan Yang , Cheng Ren , Wei Liu , Mingxing Li , Ming Li\",\"doi\":\"10.1016/j.jmsy.2025.04.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Maintenance has long been a concern in manufacturing, both in the production and product-service phases. As a type of large product, aviation maintenance produces a collection of services to ensure that aircrafts or aircraft systems, components, and structures meet airworthiness standards. Planning, scheduling, and execution (PSE) is important for maintenance systems to optimize resource utilization and job sequencing through decision-making at different time cycles. However, stochastic uncertainty always exists, affecting the stability of the entire maintenance process. Therefore, in this study, which was inspired by operating systems (i.e., Windows, Android, etc.) for processing uncertain user actions with high efficiency, an out-of-order enabled operation system in aviation maintenance (OoO-AMOS) is designed to mitigate the influence of uncertainties that exist in the PSE procedure. Two key components, namely, thread manager and resource manager, are proposed at the kernel level of the OoO-AMOS. The concept of out-of-order (OoO) is deployed for the thread manager to dynamically select the optimal order sequence based on task dependencies and feasibility. A finite state machine (FSM) model is integrated as the operation validation mechanism to formulize the resource states and their transitions. Finally, a case study is conducted to evaluate the effectiveness of the proposed OoO-AMOS. The results show that OoO-AMOS presents significant advantages over traditional approaches. In uncertain environments, the total setup time was reduced by more than 55 %, whereas the maintenance makespan, average order tardiness, and hangar turnover rate achieved improvements of more than 22 %, 31 %, and 23 %, respectively.</div></div>\",\"PeriodicalId\":16227,\"journal\":{\"name\":\"Journal of Manufacturing Systems\",\"volume\":\"80 \",\"pages\":\"Pages 824-840\"},\"PeriodicalIF\":12.2000,\"publicationDate\":\"2025-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0278612525001001\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278612525001001","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Out-of-order enabled operating system for uncertain planning, scheduling and execution in aviation maintenance
Maintenance has long been a concern in manufacturing, both in the production and product-service phases. As a type of large product, aviation maintenance produces a collection of services to ensure that aircrafts or aircraft systems, components, and structures meet airworthiness standards. Planning, scheduling, and execution (PSE) is important for maintenance systems to optimize resource utilization and job sequencing through decision-making at different time cycles. However, stochastic uncertainty always exists, affecting the stability of the entire maintenance process. Therefore, in this study, which was inspired by operating systems (i.e., Windows, Android, etc.) for processing uncertain user actions with high efficiency, an out-of-order enabled operation system in aviation maintenance (OoO-AMOS) is designed to mitigate the influence of uncertainties that exist in the PSE procedure. Two key components, namely, thread manager and resource manager, are proposed at the kernel level of the OoO-AMOS. The concept of out-of-order (OoO) is deployed for the thread manager to dynamically select the optimal order sequence based on task dependencies and feasibility. A finite state machine (FSM) model is integrated as the operation validation mechanism to formulize the resource states and their transitions. Finally, a case study is conducted to evaluate the effectiveness of the proposed OoO-AMOS. The results show that OoO-AMOS presents significant advantages over traditional approaches. In uncertain environments, the total setup time was reduced by more than 55 %, whereas the maintenance makespan, average order tardiness, and hangar turnover rate achieved improvements of more than 22 %, 31 %, and 23 %, respectively.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.