Pub Date : 2023-12-06DOI: 10.1080/00207543.2023.2289076
Gyeongho Kim, Sang Min Yang, S. Kim, Do Young Kim, Jae Gyeong Choi, Hyung Wook Park, Sunghoon Lim
{"title":"A multi-domain mixture density network for tool wear prediction under multiple machining conditions","authors":"Gyeongho Kim, Sang Min Yang, S. Kim, Do Young Kim, Jae Gyeong Choi, Hyung Wook Park, Sunghoon Lim","doi":"10.1080/00207543.2023.2289076","DOIUrl":"https://doi.org/10.1080/00207543.2023.2289076","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"95 3","pages":""},"PeriodicalIF":9.2,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596014","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 : 2023-12-02DOI: 10.1080/00207543.2022.2032860
Jianyu Long, Yibin Chen, Zhe Yang, Yunwei Huang, Chuan Li
Fault diagnosis is an indispensable basis for the collaborative maintenance in prognostic and health management. Most of existing data-driven fault diagnosis approaches are designed in the framework of supervised learning, which requires a large number of labelled samples. In this paper, a novel self-training semi-supervised deep learning (SSDL) approach is proposed to train a fault diagnosis model together with few labelled and abundant unlabelled samples. The addressed SSDL approach is realised by initialising a stacked sparse auto-encoder classifier using the labelled samples, and subsequently updating the classifier via sampling a few candidates with most reliable pseudo labels from the unlabelled samples step by step. Unlike the commonly used static sampling strategy in existing self-training semi-supervised frameworks, a gradually exploiting mechanism is proposed in SSDL to increase the number of selected pseudo-labelled candidates gradually. In addition, instead of using the prediction accuracy as the confidence estimation for pseudo-labels, a distance-based sampling criterion is designed to assign the label for each unlabelled sample by its nearest labelled sample based on their Euclidean distances in the deep feature space. The experimental results show that the proposed SSDL approach can achieve good prediction accuracy compared to other self-training semi-supervised learning algorithms.
{"title":"A novel self-training semi-supervised deep learning approach for machinery fault diagnosis","authors":"Jianyu Long, Yibin Chen, Zhe Yang, Yunwei Huang, Chuan Li","doi":"10.1080/00207543.2022.2032860","DOIUrl":"https://doi.org/10.1080/00207543.2022.2032860","url":null,"abstract":"Fault diagnosis is an indispensable basis for the collaborative maintenance in prognostic and health management. Most of existing data-driven fault diagnosis approaches are designed in the framework of supervised learning, which requires a large number of labelled samples. In this paper, a novel self-training semi-supervised deep learning (SSDL) approach is proposed to train a fault diagnosis model together with few labelled and abundant unlabelled samples. The addressed SSDL approach is realised by initialising a stacked sparse auto-encoder classifier using the labelled samples, and subsequently updating the classifier via sampling a few candidates with most reliable pseudo labels from the unlabelled samples step by step. Unlike the commonly used static sampling strategy in existing self-training semi-supervised frameworks, a gradually exploiting mechanism is proposed in SSDL to increase the number of selected pseudo-labelled candidates gradually. In addition, instead of using the prediction accuracy as the confidence estimation for pseudo-labels, a distance-based sampling criterion is designed to assign the label for each unlabelled sample by its nearest labelled sample based on their Euclidean distances in the deep feature space. The experimental results show that the proposed SSDL approach can achieve good prediction accuracy compared to other self-training semi-supervised learning algorithms.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"61 6","pages":"8238 - 8251"},"PeriodicalIF":9.2,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138606706","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 : 2023-12-01DOI: 10.1080/00207543.2023.2288866
M. Elyasi, O. Ö. Özener, Ihsan Yanikoglu, Ali Ekici, Alexandre Dolgui
{"title":"A column generation-based approach for the adaptive stochastic blood donation tailoring problem","authors":"M. Elyasi, O. Ö. Özener, Ihsan Yanikoglu, Ali Ekici, Alexandre Dolgui","doi":"10.1080/00207543.2023.2288866","DOIUrl":"https://doi.org/10.1080/00207543.2023.2288866","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":" 4","pages":""},"PeriodicalIF":9.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138614778","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 : 2023-12-01DOI: 10.1080/00207543.2023.2285424
Riccardo Aldrighetti, Martina Calzavara, Michele Martignago, I. Zennaro, Daria Battini, Dmitry Ivanov
{"title":"A methodological framework for the design of efficient resilience in supply networks","authors":"Riccardo Aldrighetti, Martina Calzavara, Michele Martignago, I. Zennaro, Daria Battini, Dmitry Ivanov","doi":"10.1080/00207543.2023.2285424","DOIUrl":"https://doi.org/10.1080/00207543.2023.2285424","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"14 9","pages":""},"PeriodicalIF":9.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624924","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 : 2023-11-14DOI: 10.1080/00207543.2023.2280696
Xiang Sun, Shunsheng Guo, Jun Guo, Baigang Du, Zhijie Yang, Kaipu Wang
ABSTRACTMost existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.KEYWORDS: Hybrid production line balancingdisassembly and assemblycycle timeworkload smoothnesshybrid genetic simulated annealing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be made available on request.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Project (No. 51705386) and by China Scholarship Council (No. 201606955091).Notes on contributorsXiang SunXiang Sun received the B.Eng degree from Huazhong Agricultural University, Wuhan, China, in 2018. He is pursuing the Ph.D. degree at Wuhan University of Technology, Wuhan, China. His current research interests include manufacturing scheduling, machine learning and intelligent optimization algorithms.Shunsheng GuoShunsheng Guo received the B.Sc. degree in Mechanical manufacturing and automation from Huazhong University of Science and Technology, Wuhan, China, in 1984 and the Ph.D. degree in Mechanical Design and Theory from Wuhan University of Technology, Wuhan, China, in 2001. He is currently a Professor with the School of Mechanical and Electronic Engineering, Wuhan, China. His current research interests include manufacturing informatization and intelligent manufacturing.Jun GuoJun Guo received the M.S. degree (2009) and Ph.D. degree (2012) in Mechanical Engineering from Wuhan University of Technology, Wuhan, China. He is currently an Associate Professor with the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China. His current research interests include p
{"title":"A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly","authors":"Xiang Sun, Shunsheng Guo, Jun Guo, Baigang Du, Zhijie Yang, Kaipu Wang","doi":"10.1080/00207543.2023.2280696","DOIUrl":"https://doi.org/10.1080/00207543.2023.2280696","url":null,"abstract":"ABSTRACTMost existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.KEYWORDS: Hybrid production line balancingdisassembly and assemblycycle timeworkload smoothnesshybrid genetic simulated annealing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be made available on request.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Project (No. 51705386) and by China Scholarship Council (No. 201606955091).Notes on contributorsXiang SunXiang Sun received the B.Eng degree from Huazhong Agricultural University, Wuhan, China, in 2018. He is pursuing the Ph.D. degree at Wuhan University of Technology, Wuhan, China. His current research interests include manufacturing scheduling, machine learning and intelligent optimization algorithms.Shunsheng GuoShunsheng Guo received the B.Sc. degree in Mechanical manufacturing and automation from Huazhong University of Science and Technology, Wuhan, China, in 1984 and the Ph.D. degree in Mechanical Design and Theory from Wuhan University of Technology, Wuhan, China, in 2001. He is currently a Professor with the School of Mechanical and Electronic Engineering, Wuhan, China. His current research interests include manufacturing informatization and intelligent manufacturing.Jun GuoJun Guo received the M.S. degree (2009) and Ph.D. degree (2012) in Mechanical Engineering from Wuhan University of Technology, Wuhan, China. He is currently an Associate Professor with the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China. His current research interests include p","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"15 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991400","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 : 2023-11-13DOI: 10.1080/00207543.2023.2279129
Paula Terán-Viadero, Antonio Alonso-Ayuso, F. Javier Martín-Campo
AbstractThis paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs.Keywords: Cutting stock problem2-dimensional cuttingvariable-sized stockmixed integer linear optimisationcardboard industry AcknowledgmentsThe authors would like to thank the company managers for providing us with real data and for giving us insight into the company's current operation.Data availability statementDue to the nature of the research, due to commercial supporting not all data is available. We refer the readers to Terán-Viadero, Alonso-Ayuso, and Martín-Campo (Citation2023) where for six instances, input data and results obtained are reported.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been supported by grant PID2021-122640OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’.Notes on contributorsPaula Terán-ViaderoPaula Terán-Viadero is a PhD student who received her Master's degree in 2019 in Mathematical Engineering from the Complutense University of Madrid (UCM), Spain. She specialised in operations research in 2019 when she was part of the Statistics and Operational Research department in the Faculty of Mathematical Sciences at UCM, developing optimisation models for a company in the hospitality sector. Since then, she has worked in the private sector, developing integer linear mathematical optimisation models to solve problems arising from real-world applications.Antonio Alonso-AyusoAntonio Alonso-Ayuso received his PhD in Mathematics from the Complutense University of Madrid, Spain, in 1997. He is currently a Full Professor in Statistics and Operational Research at Rey Juan Carlos University, Spain. His main research interests include linear and integer mathematical optimisation, decision models, and stochastic optimisation applied to combinatorial problems. He has developed several projects jointly with companies in different sectors (steel, oil and paper, among others).F. Javier Martín-CampoF. Javier Martín-Campo received his PhD from the Rey
摘要针对西班牙某公司蜂窝纸板工业中出现的变料大小的二维切料问题,介绍了新的数学优化模型。这一问题主要不同于传统的裁切料问题,裁切料被认为是可变尺寸的,即我们必须决定面板的尺寸、宽度和长度。这种方法在库存与切割过程同时生产的行业中是有用的。然后,库存被切成更小的矩形块,这些矩形块必须满足客户的要求,比如产品的类型、尺寸、需求和技术规格。此外,在本文处理的问题中,切割是断头台式的,从一边到另一边进行。利用该公司的实际数据验证了所提出的数学模型,得到的结果大大减少了生产材料和剩余物,减少了操作时间和经济成本。关键词:切料问题二维切料可变大小库存混合整数线性优化纸板行业致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢致谢数据可用性声明由于研究的性质,由于商业支持,并非所有数据都可用。我们建议读者参阅Terán-Viadero、Alonso-Ayuso和Martín-Campo (Citation2023),其中报告了六个实例的输入数据和获得的结果。披露声明作者未报告潜在的利益冲突。本研究由MCIN/AEI/10.13039/501100011033资助的PID2021-122640OB-I00基金和“ERDF A way of making Europe”资助。spula Terán-ViaderoPaula Terán-Viadero是一名博士生,于2019年获得西班牙马德里康普顿斯大学(UCM)数学工程硕士学位。她于2019年专注于运筹学,当时她是UCM数学科学学院统计与运筹学部门的一员,为酒店业的一家公司开发优化模型。此后,她一直在私营部门工作,开发整数线性数学优化模型,以解决现实应用中出现的问题。Antonio Alonso-Ayuso于1997年获得西班牙马德里康普顿斯大学数学博士学位。他目前是西班牙雷胡安卡洛斯大学统计学和运筹学的全职教授。他的主要研究兴趣包括线性和整数数学优化、决策模型和应用于组合问题的随机优化。他与不同行业的公司(钢铁、石油和造纸等)合作开发了几个项目。哈维尔Martin-CampoF。Javier Martín-Campo于2010年获得西班牙雷伊胡安卡洛斯大学博士学位。他目前是西班牙马德里康普顿斯大学统计与运筹学副教授。他的主要研究兴趣包括运筹学,特别是数学规划(整数,非线性),商业和人道主义物流的决策援助模型以及启发式/元启发式。他曾参与多个不同领域的项目:空中交通管理、钢铁和造纸等。
{"title":"A 2-dimensional guillotine cutting stock problem with variable-sized stock for the honeycomb cardboard industry","authors":"Paula Terán-Viadero, Antonio Alonso-Ayuso, F. Javier Martín-Campo","doi":"10.1080/00207543.2023.2279129","DOIUrl":"https://doi.org/10.1080/00207543.2023.2279129","url":null,"abstract":"AbstractThis paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs.Keywords: Cutting stock problem2-dimensional cuttingvariable-sized stockmixed integer linear optimisationcardboard industry AcknowledgmentsThe authors would like to thank the company managers for providing us with real data and for giving us insight into the company's current operation.Data availability statementDue to the nature of the research, due to commercial supporting not all data is available. We refer the readers to Terán-Viadero, Alonso-Ayuso, and Martín-Campo (Citation2023) where for six instances, input data and results obtained are reported.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been supported by grant PID2021-122640OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’.Notes on contributorsPaula Terán-ViaderoPaula Terán-Viadero is a PhD student who received her Master's degree in 2019 in Mathematical Engineering from the Complutense University of Madrid (UCM), Spain. She specialised in operations research in 2019 when she was part of the Statistics and Operational Research department in the Faculty of Mathematical Sciences at UCM, developing optimisation models for a company in the hospitality sector. Since then, she has worked in the private sector, developing integer linear mathematical optimisation models to solve problems arising from real-world applications.Antonio Alonso-AyusoAntonio Alonso-Ayuso received his PhD in Mathematics from the Complutense University of Madrid, Spain, in 1997. He is currently a Full Professor in Statistics and Operational Research at Rey Juan Carlos University, Spain. His main research interests include linear and integer mathematical optimisation, decision models, and stochastic optimisation applied to combinatorial problems. He has developed several projects jointly with companies in different sectors (steel, oil and paper, among others).F. Javier Martín-CampoF. Javier Martín-Campo received his PhD from the Rey","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"50 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348308","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 : 2023-11-10DOI: 10.1080/00207543.2023.2274335
Kendrik Yan Hong Lim, Theresia Stefanny Yosal, Chun-Hsien Chen, Pai Zheng, Lihui Wang, Xun Xu
AbstractThe increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies.KEYWORDS: Cognitive digital twinsindustrial knowledge graphscausal inferencesemantic modellingquality assurancemaintenance AcknowledgementsThe authors would like to acknowledge the professional advice of Teo Man Ru and Tiong Je Min from Tetra Pak Jurong Pte Ltd.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData is not available due to commercial restrictions. Due to the sensitive nature of this study, the participants of this study did not consent to public sharing of their data, so support data is not available.Additional informationNotes on contributorsKendrik Yan Hong LimKendrik Yan Hong LIM is a Ph.D. candidate at the School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore and a senior research engineer at Singapore’s Agency of Science and Technology (A*STAR). He holds a bachelor’s degree in mechanical engineering from NTU, and a master’s degree in Industry Engineering from Chiba University, Japan. His research interests include engineering informatics, digital twins, and smart product-service systems.Theresia Stefanny YosalTheresia Stefanny Yosal is currently working as an equipment engineer at a manufacturing company. She holds a bachelor’s degree in mechanical engineering from Nanyang Technological University (NTU), Singapore. Her research interests are digital twins, product design and development, and manufacturing.Chun-Hsien Ch
2020年被中小企业评选为智能制造领域20位最具影响力教授之一。徐迅,奥克兰大学机械与机电一体化工程系智能制造教授。他在智能制造解决方案领域工作了30多年。徐博士是工业4.0智能制造系统(LISMS)实验室主任。他目前的研究重点是工业4.0技术,如智能工厂、数字孪生和云制造。徐博士是ASME的研究员。他于2020年被Web of Science评为Clarivate™高被引研究员。同年被美国制造工程师学会(SME)评为“智能制造领域最具影响力的20位教授”之一。
{"title":"Graph-enabled cognitive digital twins for causal inference in maintenance processes","authors":"Kendrik Yan Hong Lim, Theresia Stefanny Yosal, Chun-Hsien Chen, Pai Zheng, Lihui Wang, Xun Xu","doi":"10.1080/00207543.2023.2274335","DOIUrl":"https://doi.org/10.1080/00207543.2023.2274335","url":null,"abstract":"AbstractThe increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies.KEYWORDS: Cognitive digital twinsindustrial knowledge graphscausal inferencesemantic modellingquality assurancemaintenance AcknowledgementsThe authors would like to acknowledge the professional advice of Teo Man Ru and Tiong Je Min from Tetra Pak Jurong Pte Ltd.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData is not available due to commercial restrictions. Due to the sensitive nature of this study, the participants of this study did not consent to public sharing of their data, so support data is not available.Additional informationNotes on contributorsKendrik Yan Hong LimKendrik Yan Hong LIM is a Ph.D. candidate at the School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore and a senior research engineer at Singapore’s Agency of Science and Technology (A*STAR). He holds a bachelor’s degree in mechanical engineering from NTU, and a master’s degree in Industry Engineering from Chiba University, Japan. His research interests include engineering informatics, digital twins, and smart product-service systems.Theresia Stefanny YosalTheresia Stefanny Yosal is currently working as an equipment engineer at a manufacturing company. She holds a bachelor’s degree in mechanical engineering from Nanyang Technological University (NTU), Singapore. Her research interests are digital twins, product design and development, and manufacturing.Chun-Hsien Ch","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"120 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137330","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 : 2023-11-10DOI: 10.1080/00207543.2023.2269584
Meenu Singh, Sunil Kumar Jauhar, Millie Pant, Sanjoy Kumar Paul
AbstractThe COVID-19 pandemic has increased the demand for life-saving devices known as ‘ventilators,’ which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company’s reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.KEYWORDS: Reverse logisticswaste recyclingmulti-objective programmingorder allocationhealthcare industry Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability StatementThe data supporting this study’s findings are available on request from the corresponding author.Notes1 Source: https://www.statista.com/statistics/1067081/generation-electronic-waste-globally-forecast/2 Source: https://theroundup.org/global-e-waste-statistics/.3 Source: https://www.deccanherald.com/business/covid-19-automakers-medical-device-makers-join-hands-to-produce-ventilators-819878.html.4 Source: https://www.weforum.org/agenda/2020/04/covid-19-ventilator-shortage-manufacturing-solution/5 Source: https://straitsresearch.com/report/ventilators-market6 Source:https://www.indiatoday.in/india/story/did-ventilators-from-pm-cares-fund-fail-or-states-failed-to-manage-them-1803473-2021-05-17Additional informationNotes on contributorsMeenu SinghDr. Meenu Singh received a Ph.D. in operations research and decision science from the Department of Applied Mathematics and Scientific Computing at the Indian Institute of Technology (IIT) Roorkee, India, in 2022. She is currently a postdoctoral researcher at VŠB – Technical University of Ostrava, Ostrava, Czech Republic. Her research focuses on operational research, supply chain management, data analysis, mathematical modelling, multi-criteria decision-making (MCDM) process, and the application of soft computing techniques.Sunil Kumar JauharDr. Sunil Kumar Jauhar is currently working as an assistant professor in the operations and decision science area at IIM
{"title":"Modeling third-party reverse logistics for healthcare waste recycling in the post-pandemic era","authors":"Meenu Singh, Sunil Kumar Jauhar, Millie Pant, Sanjoy Kumar Paul","doi":"10.1080/00207543.2023.2269584","DOIUrl":"https://doi.org/10.1080/00207543.2023.2269584","url":null,"abstract":"AbstractThe COVID-19 pandemic has increased the demand for life-saving devices known as ‘ventilators,’ which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company’s reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.KEYWORDS: Reverse logisticswaste recyclingmulti-objective programmingorder allocationhealthcare industry Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability StatementThe data supporting this study’s findings are available on request from the corresponding author.Notes1 Source: https://www.statista.com/statistics/1067081/generation-electronic-waste-globally-forecast/2 Source: https://theroundup.org/global-e-waste-statistics/.3 Source: https://www.deccanherald.com/business/covid-19-automakers-medical-device-makers-join-hands-to-produce-ventilators-819878.html.4 Source: https://www.weforum.org/agenda/2020/04/covid-19-ventilator-shortage-manufacturing-solution/5 Source: https://straitsresearch.com/report/ventilators-market6 Source:https://www.indiatoday.in/india/story/did-ventilators-from-pm-cares-fund-fail-or-states-failed-to-manage-them-1803473-2021-05-17Additional informationNotes on contributorsMeenu SinghDr. Meenu Singh received a Ph.D. in operations research and decision science from the Department of Applied Mathematics and Scientific Computing at the Indian Institute of Technology (IIT) Roorkee, India, in 2022. She is currently a postdoctoral researcher at VŠB – Technical University of Ostrava, Ostrava, Czech Republic. Her research focuses on operational research, supply chain management, data analysis, mathematical modelling, multi-criteria decision-making (MCDM) process, and the application of soft computing techniques.Sunil Kumar JauharDr. Sunil Kumar Jauhar is currently working as an assistant professor in the operations and decision science area at IIM ","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"102 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136537","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 : 2023-11-09DOI: 10.1080/00207543.2023.2270586
Emilia Vann Yaroson, Soumyadeb Chowdhury, Sachin Kumar Mangla, Prasanta Dey, Felix T. S. Chan, Melanie Roux
AbstractAmid the escalating environmental crises and economic disparities, Circular Economy (CE) has garnered recognition as a pragmatic mechanism for achieving Sustainable Development Goals (SDGs). In response, several supply chain organisations are integrating CE strategies into their business operations and production processes. Despite these developments and since the introduction of Business Charter for Sustainable Development by the International Chamber of Commerce in 1991, the academic corpus comprehensively connecting CE research themes, catalysts, deterrents, practices with the SDGs has remained limited. To bridge this gap, we present a systematic literature review (SLR) of CE research in operations, supply chain and production management encompassing a time span of 31 years (January 1991 – June 2022), by sourcing, screening, and analysing articles obtained from multiple research databases. Our thematic coding analysis generated ten research themes, and subsequently linking them with relevant SDGs. Additionally, we interweaved CE catalysts and deterrents, establishing a connection with the SDGs. This is further enriched with CE strategies aimed at equipping business practitioners to enhance sustainable business performance and contributing to specific SDGs. Lastly, we delineate CE knowledge data management and priority actions frameworks to aid organisations to enhance employee capability and actively leverage digital technologies for implementing CE strategies.KEYWORDS: Circular economySystematic literature reviewThematic evolutionGreen and responsible supply chain managementSustainability development goalsSustainable business performance Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to the size of the dataset.Additional informationFundingThe work described in this paper was substantially supported by grants from Macau University of Science and Technology Faculty Research Grants (FRG) under grant number FRG-22–108-MSB, and The Macau Foundation Fund (MFP) under grant number MF-23-008-R.Notes on contributorsEmilia Vann YarosonEmilia Vann Yaroson is a lecturer in Operations and Analytics at the University of Huddersfield Business School. She earned her PhD in Operations and Information Management at the University of Bradford, UK. Her current research focus include theoretical improvement and applications of artificial intelligence, big data analytics, block chain, within health service operations, supply chain resilience and sustainable production and open innovation. She has published in leading peer reviewed journals such as International Journal of Production Research, Journal of Business Research and Supply Chain Management.Soumyadeb ChowdhurySoumyadeb Chowdhury is Associate Professor of Digital Sustainability Mana
摘要在环境危机和经济差距日益加剧的背景下,循环经济作为实现可持续发展目标的一种务实机制已得到认可。作为回应,一些供应链组织正在将CE战略整合到他们的业务运营和生产过程中。尽管取得了这些进展,而且自1991年国际商会(International Chamber of Commerce)推出《可持续发展商业宪章》(Business Charter for Sustainable Development)以来,将企业行政研究主题、催化剂、威慑因素、实践与可持续发展目标全面联系起来的学术语料库仍然有限。为了弥补这一差距,我们通过采购、筛选和分析从多个研究数据库中获得的文章,对运营、供应链和生产管理方面的CE研究进行了系统的文献综述(SLR),时间跨度为31年(1991年1月至2022年6月)。我们的主题编码分析产生了十个研究主题,随后将它们与相关的可持续发展目标联系起来。此外,我们将CE催化剂和威慑物相结合,与可持续发展目标建立联系。这进一步丰富了旨在装备业务从业者提高可持续业务绩效和促进具体可持续发展目标的企业行政战略。最后,我们描述了CE知识数据管理和优先行动框架,以帮助组织提高员工能力并积极利用数字技术实施CE战略。关键词:循环经济系统文献综述主题演变绿色与负责任供应链管理可持续发展目标可持续经营绩效披露声明作者未报告潜在利益冲突数据可得性声明支持本研究结果的数据可根据通讯作者的合理要求获得。由于数据集的大小,这些数据不公开可用。本论文所描述的工作主要由澳门科技大学教师研究补助金(FRG)资助,资助号为FRG-22 - 108- msb,以及澳门基金基金(MFP)资助,资助号为MF-23-008-R。作者简介emilia Vann Yaroson是哈德斯菲尔德大学商学院运营与分析专业的讲师。她在英国布拉德福德大学获得运营和信息管理博士学位。她目前的研究重点包括人工智能的理论改进和应用,大数据分析,区块链,卫生服务运营,供应链弹性和可持续生产以及开放式创新。她曾在《International Journal of Production Research》、《Journal of Business Research》和《Supply Chain Management》等领先的同行评审期刊上发表文章。Soumyadeb Chowdhury,法国TBS教育学院信息、运营和决策科学系数字可持续管理、新兴技术和分析副教授。他是TBS可持续发展和企业社会责任卓越中心(SD-CSR)的负责人。他目前的研究重点是循环经济、中小企业的可持续发展、智能决策的数字分析以及新兴技术的采用和管理。他的研究项目获得了英国文化协会、牛顿皇家工程院(RAE)、英国研究与创新(UKRI)、经济与社会研究委员会、法国国家研究机构(ANR)的资助,并与世界各地新兴和发达经济体的学术界和中小企业从业者合作。他还参与撰写了《世界经济论坛2022年中小企业未来准备报告》(可持续发展章节)、《世界经济论坛中小企业作为数据驱动型企业白皮书》,并在论坛上为行业从业者举办了一个研讨会。他还在《英国管理杂志》、《人力资源管理杂志》、《国际生产经济学杂志》、《商业研究杂志》、《企业战略与管理》、《国际人力资源管理杂志》等顶级期刊上发表了关于可持续发展的多学科研究论文。Sachin Kumar Mangla在绿色和可持续供应链和运营领域工作;行业4.0;循环经济;决策与模拟。他在供应链、运营管理和决策方面有超过五年的教学经验,目前在英国、土耳其、印度、中国、法国等多所大学任教。他是印度O.P.金达尔全球大学“数字循环经济促进可持续发展目标(DCE-SDG)”研究中心主任。他致力于做和促进高质量的研究。
{"title":"A systematic literature review exploring and linking circular economy and sustainable development goals in the past three decades (1991–2022)","authors":"Emilia Vann Yaroson, Soumyadeb Chowdhury, Sachin Kumar Mangla, Prasanta Dey, Felix T. S. Chan, Melanie Roux","doi":"10.1080/00207543.2023.2270586","DOIUrl":"https://doi.org/10.1080/00207543.2023.2270586","url":null,"abstract":"AbstractAmid the escalating environmental crises and economic disparities, Circular Economy (CE) has garnered recognition as a pragmatic mechanism for achieving Sustainable Development Goals (SDGs). In response, several supply chain organisations are integrating CE strategies into their business operations and production processes. Despite these developments and since the introduction of Business Charter for Sustainable Development by the International Chamber of Commerce in 1991, the academic corpus comprehensively connecting CE research themes, catalysts, deterrents, practices with the SDGs has remained limited. To bridge this gap, we present a systematic literature review (SLR) of CE research in operations, supply chain and production management encompassing a time span of 31 years (January 1991 – June 2022), by sourcing, screening, and analysing articles obtained from multiple research databases. Our thematic coding analysis generated ten research themes, and subsequently linking them with relevant SDGs. Additionally, we interweaved CE catalysts and deterrents, establishing a connection with the SDGs. This is further enriched with CE strategies aimed at equipping business practitioners to enhance sustainable business performance and contributing to specific SDGs. Lastly, we delineate CE knowledge data management and priority actions frameworks to aid organisations to enhance employee capability and actively leverage digital technologies for implementing CE strategies.KEYWORDS: Circular economySystematic literature reviewThematic evolutionGreen and responsible supply chain managementSustainability development goalsSustainable business performance Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to the size of the dataset.Additional informationFundingThe work described in this paper was substantially supported by grants from Macau University of Science and Technology Faculty Research Grants (FRG) under grant number FRG-22–108-MSB, and The Macau Foundation Fund (MFP) under grant number MF-23-008-R.Notes on contributorsEmilia Vann YarosonEmilia Vann Yaroson is a lecturer in Operations and Analytics at the University of Huddersfield Business School. She earned her PhD in Operations and Information Management at the University of Bradford, UK. Her current research focus include theoretical improvement and applications of artificial intelligence, big data analytics, block chain, within health service operations, supply chain resilience and sustainable production and open innovation. She has published in leading peer reviewed journals such as International Journal of Production Research, Journal of Business Research and Supply Chain Management.Soumyadeb ChowdhurySoumyadeb Chowdhury is Associate Professor of Digital Sustainability Mana","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":" 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241824","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 : 2023-11-09DOI: 10.1080/00207543.2023.2279145
Jing Wang, Deming Lei, Hongtao Tang
AbstractHybrid flow shop scheduling problem (HFSP) with real-life constraints has been extensively considered; however, HFSP with batch processing machines (BPM) at a middle stage is seldom investigated. In this study, HFSP with BPM at a middle stage in hot & cold casting process is considered and an adaptive artificial bee colony (AABC) is proposed to minimise makespan. To produce high quality solutions, an adaptive search process with employed bee phase and adaptive search step is implemented. Adaptive search step, which may be onlooker bee phase or cooperation or empty, is decided by evolution quality and an adaptive threshold. Cooperation is performed between the improved solutions of one employed bee swarm and the unimproved solutions of another swarm. Six search operators are constructed and search operator is adaptively adjusted. A new scout phase is also given. A lower bound is provided and proved. Extensive experiments are conducted. The computational results validate that new strategies such as cooperation are effective and efficient and AABC can obtain better results than methods from existing literature on the considered problem.Keywords: Hot & cold castingartificial bee colonyhybrid flow shop scheduling problembatch processing machines Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData supporting this study are described in the first paragraph of Section 5.1.Additional informationFundingThis work is supported by the National Natural Science Foundation of China [61573264].Notes on contributorsJing WangJing Wang received the bachelor's degree in industrial engineering from the Hubei University of Technology, Wuhan, China, in 2017 and the master's degree in industrial engineering from Fuzhou University, Fuzhou, China, in 2020. She is currently pursuing the doctoral degree with the School of Automation, Wuhan University of Technology, Wuhan, China. Her current research interest includes manufacturing systems intelligent optimisation and scheduling.Deming LeiDeming Lei received the master's degree in applied mathematics from Xi'an Jiaotong University, Xi'an, China, in 1996 and the doctoral degree in automation science and engineering from Shanghai Jiaotong University, Shanghai, China, in 2005. He is currently a professor with the School of Automation, Wuhan University of Technology, Wuhan, China. He has published over 100 journal papers. His current research interests include intelligent system optimisation and control, and production scheduling.Hongtao TangHongtao Tang received the bachelor's degree in material molding and control engineering from the Wuhan University of Technology, Wuhan, China, in 2008, and the doctoral degree in digital material forming from Huazhong University of Science and Technology, Wuhan, China, in 2014. He is currently an associate professor with the School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China. His
{"title":"An adaptive artificial bee colony for hybrid flow shop scheduling with batch processing machines in casting process","authors":"Jing Wang, Deming Lei, Hongtao Tang","doi":"10.1080/00207543.2023.2279145","DOIUrl":"https://doi.org/10.1080/00207543.2023.2279145","url":null,"abstract":"AbstractHybrid flow shop scheduling problem (HFSP) with real-life constraints has been extensively considered; however, HFSP with batch processing machines (BPM) at a middle stage is seldom investigated. In this study, HFSP with BPM at a middle stage in hot & cold casting process is considered and an adaptive artificial bee colony (AABC) is proposed to minimise makespan. To produce high quality solutions, an adaptive search process with employed bee phase and adaptive search step is implemented. Adaptive search step, which may be onlooker bee phase or cooperation or empty, is decided by evolution quality and an adaptive threshold. Cooperation is performed between the improved solutions of one employed bee swarm and the unimproved solutions of another swarm. Six search operators are constructed and search operator is adaptively adjusted. A new scout phase is also given. A lower bound is provided and proved. Extensive experiments are conducted. The computational results validate that new strategies such as cooperation are effective and efficient and AABC can obtain better results than methods from existing literature on the considered problem.Keywords: Hot & cold castingartificial bee colonyhybrid flow shop scheduling problembatch processing machines Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData supporting this study are described in the first paragraph of Section 5.1.Additional informationFundingThis work is supported by the National Natural Science Foundation of China [61573264].Notes on contributorsJing WangJing Wang received the bachelor's degree in industrial engineering from the Hubei University of Technology, Wuhan, China, in 2017 and the master's degree in industrial engineering from Fuzhou University, Fuzhou, China, in 2020. She is currently pursuing the doctoral degree with the School of Automation, Wuhan University of Technology, Wuhan, China. Her current research interest includes manufacturing systems intelligent optimisation and scheduling.Deming LeiDeming Lei received the master's degree in applied mathematics from Xi'an Jiaotong University, Xi'an, China, in 1996 and the doctoral degree in automation science and engineering from Shanghai Jiaotong University, Shanghai, China, in 2005. He is currently a professor with the School of Automation, Wuhan University of Technology, Wuhan, China. He has published over 100 journal papers. His current research interests include intelligent system optimisation and control, and production scheduling.Hongtao TangHongtao Tang received the bachelor's degree in material molding and control engineering from the Wuhan University of Technology, Wuhan, China, in 2008, and the doctoral degree in digital material forming from Huazhong University of Science and Technology, Wuhan, China, in 2014. He is currently an associate professor with the School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China. His","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":" 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241465","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}