{"title":"基于可靠性的维修方法在考虑装配时间和能量的作业车间调度中的效果仿真评估模拟研究","authors":"Shrajal Gupta, Ajai Jain","doi":"10.1080/23080477.2021.1938502","DOIUrl":null,"url":null,"abstract":"ABSTRACT Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system’s performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"9 1","pages":"283 - 304"},"PeriodicalIF":2.4000,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23080477.2021.1938502","citationCount":"3","resultStr":"{\"title\":\"Assessing the Effect of Reliability-Based Maintenance Approach in Job Shop Scheduling with Setup Time and Energy Consideration Using Simulation; A Simulation Study\",\"authors\":\"Shrajal Gupta, Ajai Jain\",\"doi\":\"10.1080/23080477.2021.1938502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system’s performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study.\",\"PeriodicalId\":53436,\"journal\":{\"name\":\"Smart Science\",\"volume\":\"9 1\",\"pages\":\"283 - 304\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2021-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/23080477.2021.1938502\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Smart Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23080477.2021.1938502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2021.1938502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Assessing the Effect of Reliability-Based Maintenance Approach in Job Shop Scheduling with Setup Time and Energy Consideration Using Simulation; A Simulation Study
ABSTRACT Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system’s performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study.
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