Pub Date : 2023-11-01DOI: 10.1080/00207543.2023.2267680
Huosong Xia, Zelin Sun, Yuan Wang, Justin Zuopeng Zhang, Muhammad Mustafa Kamal, Sajjad M. Jasimuddin, Nazrul Islam
Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.
{"title":"Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques","authors":"Huosong Xia, Zelin Sun, Yuan Wang, Justin Zuopeng Zhang, Muhammad Mustafa Kamal, Sajjad M. Jasimuddin, Nazrul Islam","doi":"10.1080/00207543.2023.2267680","DOIUrl":"https://doi.org/10.1080/00207543.2023.2267680","url":null,"abstract":"Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"84 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/00207543.2023.2274340
Fatima Ezzahra Achamrah, Mariam Lafkihi, Eric Ballot
{"title":"A dynamic and reactive routing protocol for the physical internet network","authors":"Fatima Ezzahra Achamrah, Mariam Lafkihi, Eric Ballot","doi":"10.1080/00207543.2023.2274340","DOIUrl":"https://doi.org/10.1080/00207543.2023.2274340","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"27 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/00207543.2023.2265508
Ai Zhao, Jonathan F. Bard
{"title":"Batch scheduling in a multi-purpose system with machine downtime and a multi-skilled workforce","authors":"Ai Zhao, Jonathan F. Bard","doi":"10.1080/00207543.2023.2265508","DOIUrl":"https://doi.org/10.1080/00207543.2023.2265508","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"69 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-26DOI: 10.1080/00207543.2023.2271093
Yitian Liu, Kang Hu, Ruifeng Zhou, Xianfeng Ai, Yunqing Chen
AbstractMany theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers’ subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches.KEYWORDS: Product designdesign optimisationtheory of planned behaviourBayesian networkscustomer requirements Disclosure statementNo potential conflict of interest was reported by the author(s).AcknowledgmentsThe research also received support and assistance from College of Information Technology Shanghai Ocean University and Central China Normal University.Data availability statementBased on the protection of human subjects, the sequence files and note data for all samples used in this study have been desposited in Figshare (https://doi.org/10.6084/m9.figshare.21118321.v1). The data includes the statistical data table with the information of the participants removed, the Bayesian network graph collection generated by the R operation, the data table processed by the SPSSAU platform, and the Netica processing file.CRediT authorship contribution statementYitian Liu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Writing – review & editing, Writing – translate, Continuous modification. Kang Hu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Continuous modification. Ruifeng Zhou: Writing – review & editing, Writing – translate and Continuous modification. Xianfeng Ai: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft. Yunqing Chen: Conceptualisation, Research, Writing – original draft, Writing – review & editing, Writing – translate.Additional informationFunding.Notes on contributorsYitian LiuYitian Liu is a graduate student. Graduated from the School of Art and Design, Wuhan University of Science and Technology in 2020, majoring in industrial Design, and is studying for a doctorate degree in industri
{"title":"Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks","authors":"Yitian Liu, Kang Hu, Ruifeng Zhou, Xianfeng Ai, Yunqing Chen","doi":"10.1080/00207543.2023.2271093","DOIUrl":"https://doi.org/10.1080/00207543.2023.2271093","url":null,"abstract":"AbstractMany theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers’ subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches.KEYWORDS: Product designdesign optimisationtheory of planned behaviourBayesian networkscustomer requirements Disclosure statementNo potential conflict of interest was reported by the author(s).AcknowledgmentsThe research also received support and assistance from College of Information Technology Shanghai Ocean University and Central China Normal University.Data availability statementBased on the protection of human subjects, the sequence files and note data for all samples used in this study have been desposited in Figshare (https://doi.org/10.6084/m9.figshare.21118321.v1). The data includes the statistical data table with the information of the participants removed, the Bayesian network graph collection generated by the R operation, the data table processed by the SPSSAU platform, and the Netica processing file.CRediT authorship contribution statementYitian Liu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Writing – review & editing, Writing – translate, Continuous modification. Kang Hu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Continuous modification. Ruifeng Zhou: Writing – review & editing, Writing – translate and Continuous modification. Xianfeng Ai: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft. Yunqing Chen: Conceptualisation, Research, Writing – original draft, Writing – review & editing, Writing – translate.Additional informationFunding.Notes on contributorsYitian LiuYitian Liu is a graduate student. Graduated from the School of Art and Design, Wuhan University of Science and Technology in 2020, majoring in industrial Design, and is studying for a doctorate degree in industri","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/00207543.2023.2270689
Hamzea Al-Jabouri, Ahmed Saif, Abdelhakim Khatab, Claver Diallo, Uday Venkatadri
AbstractThe selective maintenance problem (SMP) arises in many mission-oriented multi-component systems that are operated for consecutive missions interspersed with finite breaks, during which only limited component repairs can be performed due to constrained resources. This NP-hard problem decides which components to maintain and to what levels of repair to guarantee a pre-specified performance level during the subsequent mission. Over the last two decades, a sizeable body of literature has been published on this topic. However, the contributions have stagnated in quality, and most articles deal with small to moderate problems. This paper provides a critical review of the SMP literature. A total of 136 research articles related to SMP are reviewed and a selection of key representative models is discussed in detail. This review is framed according to two feature categories: formulation characteristics, composed of three sub-groups of characteristics related to the system, maintenance and mathematical model characteristics; and solution approaches, grouped by exact methods and approximate algorithms. This critical review is aimed at identifying drawbacks, shortcomings, and blind spots of the SMP literature, and providing a roadmap for the challenges to be addressed and innovative future research topics to further advance the academic and industrial contributions of SMP.Keywords: Selective maintenancemaintenance planningreliability maximisationresource assignmentimperfect maintenance AcknowledgmentsWe also thank the anonymous reviewers for their suggestions and comments.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article. The review data is freely available at www.smpreview.com (Al-Jabouri, Saif, Diallo, Khatab, and Venkatadri Citation2023), enabling custom sorting based on characteristics of interest.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Canadian Natural Science and Engineering Research Council (NSERC) grants awarded to the second, fourth and fifth authors through the Discovery Grant Programme.Notes on contributorsHamzea Al-JabouriHamzea Al-Jabouri Ph.D., is a Simulation Specialist at MAGNA International, located in Brampton, Ontario. He earned his Ph.D. in Industrial Engineering from Dalhousie University, Halifax, Nova Scotia, and attained a Master of Applied Science in Industrial Engineering from the University of Regina, Saskatchewan. Dr. Al-Jabouri is a member of the Association of Professional Engineers and Geoscientists of Saskatchewan (APEGS). Presently, his research endeavours focus on simulation-based optimisation, as well as large-scale and robust optimisation strategies for intelligent maintenance operations.Ahmed SaifAhmed Saif , P.Eng., Ph.D., is an Associate Professor in the Department of Industrial Engineering at Dalhousie University. He received his
摘要面向任务的多部件系统存在选择性维修问题(SMP)，这些系统在连续任务中穿插有限的中断，在此期间由于资源的限制，只能进行有限的部件维修。这个NP-hard问题决定了要维护哪些部件以及维修到什么程度，以保证在后续任务中达到预先规定的性能水平。在过去的二十年里，关于这个话题的文献已经发表了相当多。然而，贡献的质量停滞不前，大多数文章都涉及小到中等的问题。本文对SMP文献进行了综述。本文综述了与SMP相关的136篇研究论文，并对其中具有代表性的模型进行了详细讨论。本文根据两个特征类别进行综述:制定特征，由与系统、维护和数学模型相关的特征组成三个子组;以及解的方法，按精确方法和近似算法分组。这篇批判性的综述旨在找出SMP文献的缺陷、不足和盲点，并为需要解决的挑战和创新的未来研究课题提供路线图，以进一步推进SMP的学术和工业贡献。关键词:选择性维护计划可靠性最大化资源分配完善维护感谢匿名审稿人提出的建议和意见数据可用性声明作者确认在文章中可以获得支持本研究结果的数据。综述数据可在www.smpreview.com免费获得(Al-Jabouri, Saif, Diallo, Khatab, and Venkatadri Citation2023)，支持根据感兴趣的特征进行自定义排序。披露声明作者未报告潜在的利益冲突。本研究由加拿大自然科学与工程研究委员会(NSERC)资助，通过发现资助计划授予第二、第四和第五作者。hamzea Al-Jabouri博士是位于安大略省宾顿市的麦格纳国际公司的模拟专家。他在新斯科舍省哈利法克斯的达尔豪斯大学获得工业工程博士学位，并在萨斯喀彻温省里贾纳大学获得工业工程应用科学硕士学位。Al-Jabouri博士是萨斯喀彻温省专业工程师和地球科学家协会(APEGS)的成员。目前，他的研究主要集中在基于仿真的优化，以及智能维护操作的大规模和鲁棒优化策略。Ahmed Saif, p.p。博士，达尔豪斯大学工业工程系副教授。他获得the University of Waterloo的管理科学博士学位、Masdar Institute of Science and Technology的工程系统与管理硕士学位、New York Institute of Technology的工商管理硕士学位和Alexandria University的生产工程学士学位。他的研究兴趣包括大规模优化、不确定性下的决策和数据分析技术及其在混合可再生能源系统、可持续供应链、人道主义物流和选择性维护中的应用。Abdelhakim Khatab博士，法国洛林大学教授。毕业于高等师范学校(ENS-Mohammedia，摩洛哥)后，他获得了里昂(法国)国家应用科学研究所(INSA)工业自动化工程硕士学位和博士学位。自2018年起，哈塔布博士担任加拿大达尔豪斯大学工业工程系兼职教授。他是IFAC TC 5.2的成员，在那里他是智能、可靠和可持续制造分销系统工作组的主席。他也是加拿大自然科学与工程研究委员会(NSERC)的科学专家成员。Khatab博士的研究兴趣包括可靠性理论、系统生产和智能维护管理的优化和决策支持、逆向供应链的设计和优化、可持续性和再制造。Claver Diallo博士，p.p。他是新斯科舍省哈利法克斯达尔豪斯大学工业工程系的教授。2007年10月起任教于达尔豪斯大学。他持有加拿大魁北克省拉瓦尔大学工业工程应用科学博士学位和硕士学位，以及机械工程学士学位。他是工业与系统工程研究所(IISE)、加拿大运筹学学会(CORS)和新斯科舍省工程师学会(ENS)的成员。
{"title":"A critical review of selective maintenance for mission-oriented systems: challenges and a roadmap for novel contributions","authors":"Hamzea Al-Jabouri, Ahmed Saif, Abdelhakim Khatab, Claver Diallo, Uday Venkatadri","doi":"10.1080/00207543.2023.2270689","DOIUrl":"https://doi.org/10.1080/00207543.2023.2270689","url":null,"abstract":"AbstractThe selective maintenance problem (SMP) arises in many mission-oriented multi-component systems that are operated for consecutive missions interspersed with finite breaks, during which only limited component repairs can be performed due to constrained resources. This NP-hard problem decides which components to maintain and to what levels of repair to guarantee a pre-specified performance level during the subsequent mission. Over the last two decades, a sizeable body of literature has been published on this topic. However, the contributions have stagnated in quality, and most articles deal with small to moderate problems. This paper provides a critical review of the SMP literature. A total of 136 research articles related to SMP are reviewed and a selection of key representative models is discussed in detail. This review is framed according to two feature categories: formulation characteristics, composed of three sub-groups of characteristics related to the system, maintenance and mathematical model characteristics; and solution approaches, grouped by exact methods and approximate algorithms. This critical review is aimed at identifying drawbacks, shortcomings, and blind spots of the SMP literature, and providing a roadmap for the challenges to be addressed and innovative future research topics to further advance the academic and industrial contributions of SMP.Keywords: Selective maintenancemaintenance planningreliability maximisationresource assignmentimperfect maintenance AcknowledgmentsWe also thank the anonymous reviewers for their suggestions and comments.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article. The review data is freely available at www.smpreview.com (Al-Jabouri, Saif, Diallo, Khatab, and Venkatadri Citation2023), enabling custom sorting based on characteristics of interest.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Canadian Natural Science and Engineering Research Council (NSERC) grants awarded to the second, fourth and fifth authors through the Discovery Grant Programme.Notes on contributorsHamzea Al-JabouriHamzea Al-Jabouri Ph.D., is a Simulation Specialist at MAGNA International, located in Brampton, Ontario. He earned his Ph.D. in Industrial Engineering from Dalhousie University, Halifax, Nova Scotia, and attained a Master of Applied Science in Industrial Engineering from the University of Regina, Saskatchewan. Dr. Al-Jabouri is a member of the Association of Professional Engineers and Geoscientists of Saskatchewan (APEGS). Presently, his research endeavours focus on simulation-based optimisation, as well as large-scale and robust optimisation strategies for intelligent maintenance operations.Ahmed SaifAhmed Saif , P.Eng., Ph.D., is an Associate Professor in the Department of Industrial Engineering at Dalhousie University. He received his","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135316128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/00207543.2022.2142689
D. V. Enrique, G. Marodin, F. Charrua-Santos, A. G. Frank
ABSTRACT Productivity, quality, and flexibility are key production targets pursued by companies that adopt Industry 4.0. However, it is unclear how Industry 4.0 technologies can help achieve these different and sometimes competing targets. This study investigates this relationship through a survey of 92 manufacturers. The study employs Exploratory Factor Analysis to define four main technology arrangements based on 18 Industry 4.0 technologies: Vertical Integration, Virtual Manufacturing, Advanced Manufacturing Processing Technologies, and Online Traceability. Then, independent samples tests were conducted to compare the implementation status of these arrangements when manufacturing flexibility, process quality, and productivity are (or are not) pursued as the main production targets. The results show that Vertical Integration is a general-purpose technology arrangement because it supports all targets. On the other hand, Virtual Manufacturing and Online Traceability are specific-purpose arrangements, adopted especially for flexibility and productivity targets, respectively. Advanced Manufacturing Processing Technologies, in turn, is an integrative-purpose technology arrangement since it is adopted when two competing targets are pursued: productivity and manufacturing flexibility. The study ends with a decision model to implement Industry 4.0 based on the production targets a company may pursue. It shows the interconnection and trade-offs between these production targets and the Industry 4.0 technologies adopted.
{"title":"Implementing industry 4.0 for flexibility, quality, and productivity improvement: technology arrangements for different purposes","authors":"D. V. Enrique, G. Marodin, F. Charrua-Santos, A. G. Frank","doi":"10.1080/00207543.2022.2142689","DOIUrl":"https://doi.org/10.1080/00207543.2022.2142689","url":null,"abstract":"ABSTRACT Productivity, quality, and flexibility are key production targets pursued by companies that adopt Industry 4.0. However, it is unclear how Industry 4.0 technologies can help achieve these different and sometimes competing targets. This study investigates this relationship through a survey of 92 manufacturers. The study employs Exploratory Factor Analysis to define four main technology arrangements based on 18 Industry 4.0 technologies: Vertical Integration, Virtual Manufacturing, Advanced Manufacturing Processing Technologies, and Online Traceability. Then, independent samples tests were conducted to compare the implementation status of these arrangements when manufacturing flexibility, process quality, and productivity are (or are not) pursued as the main production targets. The results show that Vertical Integration is a general-purpose technology arrangement because it supports all targets. On the other hand, Virtual Manufacturing and Online Traceability are specific-purpose arrangements, adopted especially for flexibility and productivity targets, respectively. Advanced Manufacturing Processing Technologies, in turn, is an integrative-purpose technology arrangement since it is adopted when two competing targets are pursued: productivity and manufacturing flexibility. The study ends with a decision model to implement Industry 4.0 based on the production targets a company may pursue. It shows the interconnection and trade-offs between these production targets and the Industry 4.0 technologies adopted.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"61 1","pages":"7001 - 7026"},"PeriodicalIF":9.2,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46582805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/00207543.2023.2269275
Vladmir Polotski, Jean-Pierre Kenné, Ali Gharbi
AbstractHybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems.Keywords: Remanufacturingfailure-proneuncertaintyestimationforecastingKalman filter Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data supporting the findings of this study are available from the corresponding author, V. Polotski, upon reasonable request.Additional informationNotes on contributorsVladmir PolotskiVladimir Polotski is a researcher in the Mechanical Engineering Department at Ecole de Technologie Superieure (ETS). Hi graduated from Moscow State University in 1974 and obtained his Ph.D. in Mechanics and Control in 1978. After moving to Canada in 1993, he joined the Perception and Robotics Group at Ecole Polytechnique de Montreal where he worked as a researcher from 1994 to 2004. From 2005 to 2009 he was a Chief algorithm designer in Frontline Robotics Inc. working on autonomous robotic systems for security applications. From 2010 to 2012 he played a key role in the design and development of navigation systems for two planetary rover projects launched by CSA working for Cohort Systems Inc. and Neptec Design Group. Since 2012, he works as a researcher in the Department of Mechanical Engineering at ETS. His research interests focus o stochastic control of manufacturing systems and mathematical problems in product development. Dr. Polotski has more than 40 years of experience in automatic control, signal processing, sensor fusion, optimisation, mobile robotics and numerical modelling.Jean-Pierre KennéJean-Pierr
这种方法可以解决许多复杂的问题，同时最大限度地降低成本和其他标准，如环境影响和产品易腐性。
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Pub Date : 2023-10-17DOI: 10.1080/00207543.2023.2270588
Fotios K. Konstantinidis, Nikolaos Myrillas, Konstantinos A. Tsintotas, Spyridon G. Mouroutsos, Antonios Gasteratos
AbstractWhen considering how an intelligent factory can ‘see,’ the answer lies in machine vision technology. To assess the current technological advancements of machine vision systems and propose a technology maturity assessment framework, a nine-phase Systematic Literature Review (SLR) strategy was implemented. As the automotive industry stands at the forefront of autonomous systems, we analysed 85 works across the entire automotive manufacturing life cycle. The findings revealed that machine vision is utilised in each technological pillar of Industry 4.0, encompassing autonomous robots, augmented reality, predictive maintenance, additive manufacturing, and more. In analysing 22 vision-based applications in 47 automotive components, we clustered machine vision systems' architectural components and processing techniques, ranging from threshold-based methods to advanced reinforcement learning techniques suitable for the I5.0 environment. Leveraging the insights gathered, we propose the I5.0 technology maturity assessment framework for machine vision systems, evaluating nine functional components across five scaling technology levels. This framework serves as a valuable tool to identify weaknesses and opportunities for improvement, guiding machine vision integration into an intelligent factory.Keywords: Maturity assessmentmachine visionsystematic literatureautomotive manufacturingindustry 5.0zero defect manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable – no new data generatedNotes1 https://fortune.com/fortune500/2021/.2 https://fortune.com/fortune500/2021/.3 https://bit.ly/ReviewedPapersAndAnalytics.Additional informationNotes on contributorsFotios K. KonstantinidisFotios Konstantinidis is a Team leader in Industry 5.0 & Smart Manufacturing at the Institute of Communication and Computer Systems (ICCS) of the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and holds a Ph.D. in Smart Manufacturing from the department of Production & Management Engineering at the Democritus University of Thrace (DUTh). He is currently leading a team of researchers and professionals with the objective of developing advanced industrial waste sorting systems. These systems utilize cutting-edge technologies such as hyperspectral & visual imaging, delta robots, air nozzles, X-ray sensors, and pretreatment units. Their focus areas include the efficient sorting of (bio)plastic waste, construction and demolition waste, metal scraps, mining characterization, and wood waste. Before this, Fotios worked as an I4.0 Technology Analyst, analysing the plants' maturity level and proposing I4.0 strategies for Fortune 500 companies. In contrast, he worked in the telecom industry at the Next-Generation Access networks. He has also organised workshops, delivered presentations at conferences/workshops, and published peer-reviewed journal
当考虑智能工厂如何“看”时，答案在于机器视觉技术。为了评估当前机器视觉系统的技术进展并提出技术成熟度评估框架，实施了九阶段系统文献综述(SLR)策略。由于汽车行业处于自动驾驶系统的前沿，我们分析了整个汽车制造生命周期中的85项工作。研究结果显示，机器视觉在工业4.0的每个技术支柱中都得到了应用，包括自主机器人、增强现实、预测性维护、增材制造等。在分析47个汽车部件中的22个基于视觉的应用时，我们对机器视觉系统的架构组件和处理技术进行了聚类，从基于阈值的方法到适用于I5.0环境的高级强化学习技术。利用收集到的见解，我们提出了机器视觉系统的I5.0技术成熟度评估框架，评估了五个扩展技术级别的九个功能组件。这个框架是一个有价值的工具，可以识别弱点和改进机会，指导机器视觉集成到智能工厂中。关键词:成熟度评估机器视觉系统文献汽车制造业零缺陷制造披露声明作者未报告潜在利益冲突数据可用性声明数据共享不适用-没有新数据生成notes1 https://fortune.com/fortune500/2021/.2 https://fortune.com/fortune500/2021/.3 https://bit.ly/ReviewedPapersAndAnalytics.Additional信息贡献者说明fotios K. Konstantinidis fotios Konstantinidis是国立技术大学电气与计算机工程学院通信与计算机系统研究所(ICCS)的工业5.0和智能制造团队负责人在雅典(NTUA)，并拥有博士学位，在德谟克利特大学(DUTh)生产与管理工程系智能制造。他目前领导着一个由研究人员和专业人员组成的团队，目标是开发先进的工业废物分类系统。这些系统采用了尖端技术，如高光谱和视觉成像、delta机器人、空气喷嘴、x射线传感器和预处理单元。他们的重点领域包括(生物)塑料废物、建筑和拆除废物、金属废料、采矿特性和木材废物的有效分类。在此之前，Fotios曾担任工业4.0技术分析师，分析工厂的成熟度水平，并为财富500强公司提出工业4.0战略。相反，他在电信行业的下一代接入网络工作。在他的职业生涯中，他还组织了研讨会，在会议/研讨会上发表演讲，并发表了同行评议的期刊论文。Nikolaos Myrillas毕业于色雷斯德谟克利特大学。他拥有生产和管理工程学士学位。主要研究方向为工业4.0和第四次工业革命时期的先进制造技术。这也是他论文的主题，这是他研究的最后一步。Nikolaos曾在雅典供水和污水处理公司(EYDAP S.A. - ATHENS WATER SUPPLY AND污水处理公司)实习，通过可再生能源设施开发方面的培训，他了解了EYDAP的可持续管理实践。Nikolaos还没有那么有经验，但他对4.0相关话题的热爱和热情正在引导着他。Konstantinos Tsintotas (IEEE高级会员)，2010年毕业于希腊Psachna Chalkida技术教育学院(现为雅典国立和卡波迪斯特大学)自动化工程系学士学位，2015年毕业于希腊kla Kozanis西马其顿技术教育学院(现为西马其顿大学)电气工程系机电一体化硕士学位。并于2021年获得希腊色雷斯德谟克利特大学生产与管理工程系机器人博士学位。他目前是色雷斯德谟克利特大学生产与管理工程系机器人与自动化实验室的博士后研究员。他的工作得到了欧洲委员会和希腊政府资助的几个研究项目的支持。他的研究兴趣包括现代智能机电一体化系统的基于视觉的方法。详细信息请访问:https://robotics.pme.duth.gr/ktsintotasSpyridon。
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Pub Date : 2023-10-17DOI: 10.1080/00207543.2023.2270076
Hu Qin, Haocheng Zhuang, Chunlong Yu, Jiliu Li
AbstractThe lot-sizing problem aims at determining the products to be produced and their quantities for each time period, which is a difficult problem in production planning. This problem becomes even more complicated when practical aspects such as limited production capacity, bill of materials, and item substitution are considered. In this paper, we study a new variant of the lot-sizing problem, called the multi-level capacitated lot-sizing problem with substitution and backorder. Unlike previous studies, this variant considers substitutions at both the product and component levels, which is based on the real needs of manufacturers to increase planning flexibility. Backorders are allowed, but should be delivered within a certain time limitation. We formulate this problem using a mathematical programming model. A matheuristic approach is proposed to solve the problem. This first generates an initial feasible solution using a relax-and-fix algorithm, and then improves it using a hybrid fix-and-optimise algorithm. The proposed algorithm is calibrated with a full factorial design of experiments, and its efficiency is well validated. Finally, through extensive numerical experiments, we analyse the properties of this new lot-sizing problem, such as the effect of substitution options, and the influence of backorder time limitation, and provide several useful managerial insights for manufacturing companies to save costs in production planning.KEYWORDS: Lot-sizing problemsubstitutionbackordermatheuristicfix-and-optimiserelax-and-fix Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe instance data that used in this paper are openly available in https://github.com/ZhuangHaoCheng/MLCLSPSB_Instance.Additional informationFundingThis research was partially supported by the National Key R&D Program of China [grant number 2018YFB1700600], National Natural Science Foundation of China [grant number 71971090,71821001], Shanghai Pujiang Program [grant number 21PJ1413300], and the Tongji University Fundamental Research Funds for the Central Universities.Notes on contributorsHu QinHu Qin received the Ph.D. degree from the City University of Hong Kong, Hong Kong, in 2011. He is currently a Professor with the School of Management, Huazhong University of Science and Technology. His current research interests are in the fields of algorithms and artificial intelligence, including various topics in operations research, such as vehicle routeing problem, freight allocation problem, container loading problems, and transportation problems.Haocheng ZhuangHaocheng Zhuang received B.S. degree from School of Management, Huazhong University of Science and Technology, Wuhan, China, 2020. He is currently pursuing the Ph.D. degree with the School of Management, Huazhong University of Science and Technology. His work focuses on the combinatorial optimisation problems in the production and logistics.Chunlong YuChunlong Yu is
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Pub Date : 2023-10-16DOI: 10.1080/00207543.2023.2269565
Pierre Bouquet, Ilya Jackson, Mostafa Nick, Amin Kaboli
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