Pub Date : 2023-09-29DOI: 10.1080/00207543.2023.2263088
Omid Abdolazimi, Mir Saman Pishvaee, Mohammad Shafiee, Davood Shishebori, Junfeng Ma, Sarah Entezari
AbstractDuring COVID-19, blood demand exceeded pre-pandemic levels due to reduced donations, causing shortages. Given the severe shortage, it's crucial to optimise blood use, prevent shortages, minimise wastage, and reduce unnecessary transfusions in all hospitalised patients. Designing a reliable blood supply chain network (BSCN) is an effective solution, especially for COVID-19 patients. This strategic decision significantly impacts emergency management performance. An efficient and reliable blood supply chain requires the consideration of multiple factors, including scarceness and perishability of blood, simultaneously. However, existing studies have not addressed all relevant factors in an integrated blood supply chain, and this paper aims to bridge this gap. Furthermore, an efficient Benders Decomposition based heuristic approach is proposed to solve the model. The solution approach has been compared with a set of commonly used meta-heuristic algorithms, including the red deer algorithm (RDA), tree growth algorithm (TGA), and genetic algorithm (GA). The outcomes illustrate that the proposed heuristic approach can solve small and large-size problems in significantly less CPU time than the other proposed solution approaches. For large-size problems, it can reduce the average CPU time by about 80% compared to TGA, about 80% compared to GA, and about 83% compared to RDA. A real case study has been implemented to validate the proposed mathematical model and solution method. The sensitivity analysis has been conducted to validate the significance of the model's parameters; consequently, several managerial insights have been derived.KEYWORDS: Supply chain managementCOVID-19Heuristic/meta-heuristic algorithmsBenders decomposition algorithm Data Availability StatementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsOmid AbdolazimiOmid Abdolazimi received his MSc degree in Industrial Engineering from the School of Engineering at Kharazmi University in 2018. His current research interests include logistics and supply chain management and robust optimisation. He has published papers in international journals, including the Journal of Cleaner Production, Neural Computing and Applications, and the like. Now, he is a Ph.D. student at Mississippi State University in the USA. In his Ph.D. study, his research focus is on operations research principles and implementation-related research. He will participate in vessel-drone multi-modal transportation network development and optimisation and truck-drone-related disaster management.Mir Saman PishvaeeMir Saman Pishvaee received his Ph.D. in Industrial Engineering from the University of Tehran and is an Associate Professor at the Iran University of Science and Technology (IUST). He has published over 12
{"title":"Blood supply chain configuration and optimization under the COVID-19 using benders decomposition based heuristic algorithm","authors":"Omid Abdolazimi, Mir Saman Pishvaee, Mohammad Shafiee, Davood Shishebori, Junfeng Ma, Sarah Entezari","doi":"10.1080/00207543.2023.2263088","DOIUrl":"https://doi.org/10.1080/00207543.2023.2263088","url":null,"abstract":"AbstractDuring COVID-19, blood demand exceeded pre-pandemic levels due to reduced donations, causing shortages. Given the severe shortage, it's crucial to optimise blood use, prevent shortages, minimise wastage, and reduce unnecessary transfusions in all hospitalised patients. Designing a reliable blood supply chain network (BSCN) is an effective solution, especially for COVID-19 patients. This strategic decision significantly impacts emergency management performance. An efficient and reliable blood supply chain requires the consideration of multiple factors, including scarceness and perishability of blood, simultaneously. However, existing studies have not addressed all relevant factors in an integrated blood supply chain, and this paper aims to bridge this gap. Furthermore, an efficient Benders Decomposition based heuristic approach is proposed to solve the model. The solution approach has been compared with a set of commonly used meta-heuristic algorithms, including the red deer algorithm (RDA), tree growth algorithm (TGA), and genetic algorithm (GA). The outcomes illustrate that the proposed heuristic approach can solve small and large-size problems in significantly less CPU time than the other proposed solution approaches. For large-size problems, it can reduce the average CPU time by about 80% compared to TGA, about 80% compared to GA, and about 83% compared to RDA. A real case study has been implemented to validate the proposed mathematical model and solution method. The sensitivity analysis has been conducted to validate the significance of the model's parameters; consequently, several managerial insights have been derived.KEYWORDS: Supply chain managementCOVID-19Heuristic/meta-heuristic algorithmsBenders decomposition algorithm Data Availability StatementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsOmid AbdolazimiOmid Abdolazimi received his MSc degree in Industrial Engineering from the School of Engineering at Kharazmi University in 2018. His current research interests include logistics and supply chain management and robust optimisation. He has published papers in international journals, including the Journal of Cleaner Production, Neural Computing and Applications, and the like. Now, he is a Ph.D. student at Mississippi State University in the USA. In his Ph.D. study, his research focus is on operations research principles and implementation-related research. He will participate in vessel-drone multi-modal transportation network development and optimisation and truck-drone-related disaster management.Mir Saman PishvaeeMir Saman Pishvaee received his Ph.D. in Industrial Engineering from the University of Tehran and is an Associate Professor at the Iran University of Science and Technology (IUST). He has published over 12","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199153","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 : 2023-09-28DOI: 10.1080/00207543.2023.2260896
Sen Xue, Chuanhou Gao
AbstractThis paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management.Keywords: Logisticswarehouseorder batchingmixed integer linear programmingMonte Carlo methodstatistical methods Data availability statementThe data that support the findings of this study are available from the corresponding author Chuanhou Gao, upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was funded by the National Nature Science Foundation of China [grant numbers 12071428, 62111530247 and 12320101001], and the Zhejiang Provincial Natural Science Foundation of China [grant number LZ20A010002].Notes on contributorsSen XueSen Xue received the B.S. degree in Information and Computing Science from Zhejiang University, Hangzhou, China in 2020. He is currently working toward the Ph.D. degree in Operations Research in the School of Mathematical Sciences, Zhejiang University, Hangzhou, China. His research interests include Integer Programming, machine learning and logistics.Chuanhou GaoChuanhou Gao received the B.Sc. degrees in Chemical Engineering from Zhejiang University of Technology, China, in 1998, and the Ph.D. degrees in Operational Research and Cybernetics from Zhejiang University, China, in 2004. From June 2004 until May 2006, he was a Postdoctor in the Department of Control Science and Engineering at Zhejiang University. Since June 2006, he has joined the Department of Mathematics at Zhejiang University, where he is currently a Full Professor. He was a visiting scholar at Carnegie Mellon University from Oct. 2011 to Oct. 2012. His research interests are in the areas of optimisation, chemical reaction network theory, machine learning, and thermodynamic process control. He is an associate editor of IEEE Transactions on Automatic Control and of International Journal of Adaptive Control and Signal Pr
{"title":"Use statistical analysis to approximate integrated order batching problem","authors":"Sen Xue, Chuanhou Gao","doi":"10.1080/00207543.2023.2260896","DOIUrl":"https://doi.org/10.1080/00207543.2023.2260896","url":null,"abstract":"AbstractThis paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management.Keywords: Logisticswarehouseorder batchingmixed integer linear programmingMonte Carlo methodstatistical methods Data availability statementThe data that support the findings of this study are available from the corresponding author Chuanhou Gao, upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was funded by the National Nature Science Foundation of China [grant numbers 12071428, 62111530247 and 12320101001], and the Zhejiang Provincial Natural Science Foundation of China [grant number LZ20A010002].Notes on contributorsSen XueSen Xue received the B.S. degree in Information and Computing Science from Zhejiang University, Hangzhou, China in 2020. He is currently working toward the Ph.D. degree in Operations Research in the School of Mathematical Sciences, Zhejiang University, Hangzhou, China. His research interests include Integer Programming, machine learning and logistics.Chuanhou GaoChuanhou Gao received the B.Sc. degrees in Chemical Engineering from Zhejiang University of Technology, China, in 1998, and the Ph.D. degrees in Operational Research and Cybernetics from Zhejiang University, China, in 2004. From June 2004 until May 2006, he was a Postdoctor in the Department of Control Science and Engineering at Zhejiang University. Since June 2006, he has joined the Department of Mathematics at Zhejiang University, where he is currently a Full Professor. He was a visiting scholar at Carnegie Mellon University from Oct. 2011 to Oct. 2012. His research interests are in the areas of optimisation, chemical reaction network theory, machine learning, and thermodynamic process control. He is an associate editor of IEEE Transactions on Automatic Control and of International Journal of Adaptive Control and Signal Pr","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387095","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 : 2023-09-28DOI: 10.1080/00207543.2023.2260495
Giovanni Paolo Carlo Tancredi, Eleonora Bottani, Giuseppe Vignali
AbstractNowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated.KEYWORDS: Digital twinproduction planning and controlfood processesprocess controlIndustry 4.0 Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData that support the findings of this study are available on request to the Corresponding author, prof. G. Vignali.Notes1 https://www.ni.com/it-it/shop/labview.html.2 https://www.bornemann.com/en-US/Home/.3 https://www.rtautomation.com/technologies/modbus-rtu/.4 http://www.kimo.it/wp-content/uploads/2016/10/24025.pdf.5 https://www.aec-smd.it/prodotto/m60sh86-to0512xxxc.6 https://www.aec-smd.it/product/smd1104lie.7 https://www.modbus.org/docs/PI_MBUS_300.pdf.8 https://www.ni.com/it-it/innovations/white-papers/14/the-modbus-protocol-in-depth.html.Additional informationNotes on contributorsGiovanni Paolo Carlo TancrediGiovanni Paolo Carlo Tancredi is Ph.D. Student in Industrial Engineering at the University of Parma. He graduated in Mechanical Engineering at the University of Parma in 2020, and began his research career, as a Research Fellow, with ‘Analysis and implementation in the field of advanced solutions on machines and assemblies of machines for the improvement of safety conditions at work’. His current field of research concerns the Digital Twin for data analysis and monitoring of production systems.Eleonora BottaniEleonora Bottani is full professor of Industrial Logistics at the Department of Engineering and Architecture of the University of Parma since November 2019. She graduated (with distinction) in Industrial Engineering and Management in 2002 and got her Ph.D. in Industrial Engineering in 2006, both at the University of Parma, where she currently has numerous academic duties. From a scientific point of view, she is active in research primarily related to logistics and supply chain management topics; secondary topics refer to the ind
{"title":"Digital twin-enabled process control in the food industry: proposal of a framework based on two case studies","authors":"Giovanni Paolo Carlo Tancredi, Eleonora Bottani, Giuseppe Vignali","doi":"10.1080/00207543.2023.2260495","DOIUrl":"https://doi.org/10.1080/00207543.2023.2260495","url":null,"abstract":"AbstractNowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated.KEYWORDS: Digital twinproduction planning and controlfood processesprocess controlIndustry 4.0 Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData that support the findings of this study are available on request to the Corresponding author, prof. G. Vignali.Notes1 https://www.ni.com/it-it/shop/labview.html.2 https://www.bornemann.com/en-US/Home/.3 https://www.rtautomation.com/technologies/modbus-rtu/.4 http://www.kimo.it/wp-content/uploads/2016/10/24025.pdf.5 https://www.aec-smd.it/prodotto/m60sh86-to0512xxxc.6 https://www.aec-smd.it/product/smd1104lie.7 https://www.modbus.org/docs/PI_MBUS_300.pdf.8 https://www.ni.com/it-it/innovations/white-papers/14/the-modbus-protocol-in-depth.html.Additional informationNotes on contributorsGiovanni Paolo Carlo TancrediGiovanni Paolo Carlo Tancredi is Ph.D. Student in Industrial Engineering at the University of Parma. He graduated in Mechanical Engineering at the University of Parma in 2020, and began his research career, as a Research Fellow, with ‘Analysis and implementation in the field of advanced solutions on machines and assemblies of machines for the improvement of safety conditions at work’. His current field of research concerns the Digital Twin for data analysis and monitoring of production systems.Eleonora BottaniEleonora Bottani is full professor of Industrial Logistics at the Department of Engineering and Architecture of the University of Parma since November 2019. She graduated (with distinction) in Industrial Engineering and Management in 2002 and got her Ph.D. in Industrial Engineering in 2006, both at the University of Parma, where she currently has numerous academic duties. From a scientific point of view, she is active in research primarily related to logistics and supply chain management topics; secondary topics refer to the ind","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387005","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 : 2023-09-26DOI: 10.1080/00207543.2023.2262043
Chin-Yuan Tseng, Junxuan Li, Li-Hsiang Lin, Kan Wang, Chelsea C. White III, Ben Wang
AbstractDecentralized manufacturing has the benefits of fast fulfillment, reducing risks of distant delivery, and improving patient access to personalised regenerative medicine (PRM). Implementing the decentralised PRM manufacturing system successfully needs a capacity planning strategy involving inventory replenishment, capacity allocation, and demand sharing to mitigate the impacts of supplier disruption and satisfy demand with a high service level. However, existing methods for generating optimal capacity planning policies for such PRM systems require knowing the distributions of the supplier disruption and demand uncertainty, which is usually unknown for PRM supply chains. This study proposes a data-driven approach that can learn effective capacity planning policy under various manufacturing circumstances without knowing the exact distributions. The proposed approach utilises a production simulation model and a deep reinforcement learning method. Case study results demonstrate that the proposed method can outperform existing methods when ground-truth demand forecasts differ from priori estimations. The results also support that the proposed method not only can be applied in regenerative medicine but also in many other sectors.Keywords: Regenerative medicinedecentralised manufacturing systemreinforcement learningdynamic capacity planningsupply disruptiondemand uncertainty 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, C.-Y. Tseng, upon reasonable request.Additional informationFundingThe authors acknowledge that the research was supported by the BioFabUSA of Advanced Regenerative Manufacturing Institute [grant number T0171]. In addition, the simulation environment developed in this study is based on the concept depicted in work supported by the National Science Foundation [grant number EEC-1648035].Notes on contributorsChin-Yuan TsengChin-Yuan Tseng received his Ph.D. in Industrial Engineering with a specialisation in System Informatics and Control and a minor in Machine Learning from the Georgia Institute of Technology in 2023. His research focuses on simulation and AI for production systems and supply chain integration.Junxuan LiJunxuan Li is a senior scientist lead at Microsoft Business Emerging Technology, applying state-of-art Operation Research (OR) and Large Language Models (LLM) methodologies to business applications, e.g., ERP and CRM systems. Junxuan received his Ph.D. in Operations Research from Georgia Tech with a minor in AI, concentrating on sequential decision-making and dynamic control. The main application areas include smart supply chains, intelligent manufacturing, intelligent healthcare, field services, transportation, and e-commerce.Li-Hsiang LinLi-Hsiang Lin serves as an Assistant Professor in the Department of Mathematics and Statistics at Georgia State University. H
{"title":"Deep reinforcement learning approach for dynamic capacity planning in decentralised regenerative medicine supply chains","authors":"Chin-Yuan Tseng, Junxuan Li, Li-Hsiang Lin, Kan Wang, Chelsea C. White III, Ben Wang","doi":"10.1080/00207543.2023.2262043","DOIUrl":"https://doi.org/10.1080/00207543.2023.2262043","url":null,"abstract":"AbstractDecentralized manufacturing has the benefits of fast fulfillment, reducing risks of distant delivery, and improving patient access to personalised regenerative medicine (PRM). Implementing the decentralised PRM manufacturing system successfully needs a capacity planning strategy involving inventory replenishment, capacity allocation, and demand sharing to mitigate the impacts of supplier disruption and satisfy demand with a high service level. However, existing methods for generating optimal capacity planning policies for such PRM systems require knowing the distributions of the supplier disruption and demand uncertainty, which is usually unknown for PRM supply chains. This study proposes a data-driven approach that can learn effective capacity planning policy under various manufacturing circumstances without knowing the exact distributions. The proposed approach utilises a production simulation model and a deep reinforcement learning method. Case study results demonstrate that the proposed method can outperform existing methods when ground-truth demand forecasts differ from priori estimations. The results also support that the proposed method not only can be applied in regenerative medicine but also in many other sectors.Keywords: Regenerative medicinedecentralised manufacturing systemreinforcement learningdynamic capacity planningsupply disruptiondemand uncertainty 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, C.-Y. Tseng, upon reasonable request.Additional informationFundingThe authors acknowledge that the research was supported by the BioFabUSA of Advanced Regenerative Manufacturing Institute [grant number T0171]. In addition, the simulation environment developed in this study is based on the concept depicted in work supported by the National Science Foundation [grant number EEC-1648035].Notes on contributorsChin-Yuan TsengChin-Yuan Tseng received his Ph.D. in Industrial Engineering with a specialisation in System Informatics and Control and a minor in Machine Learning from the Georgia Institute of Technology in 2023. His research focuses on simulation and AI for production systems and supply chain integration.Junxuan LiJunxuan Li is a senior scientist lead at Microsoft Business Emerging Technology, applying state-of-art Operation Research (OR) and Large Language Models (LLM) methodologies to business applications, e.g., ERP and CRM systems. Junxuan received his Ph.D. in Operations Research from Georgia Tech with a minor in AI, concentrating on sequential decision-making and dynamic control. The main application areas include smart supply chains, intelligent manufacturing, intelligent healthcare, field services, transportation, and e-commerce.Li-Hsiang LinLi-Hsiang Lin serves as an Assistant Professor in the Department of Mathematics and Statistics at Georgia State University. H","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719302","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 : 2023-09-26DOI: 10.1080/00207543.2023.2262616
Hanghao Cui, Xinyu Li, Liang Gao, Chunjiang Zhang
AbstractDistributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively.KEYWORDS: Distributed hybrid flow shop with unrelated parallel machinesMulti-objective scheduling considering total tardinessImproved multi-population genetic algorithmGreedy job insertion inter-factory neighbourhoodsRapid evaluation method for inter-factory neighbourhoodsSub-regional coevolution among multiple populations Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the National Natural Science Foundation of China under Grant 51825502 and U21B2029.Notes on contributorsHanghao CuiHanghao Cui received the master’s degree in industrial engineering from Huazhong University of Science and Technology, Wuhan, China, 2021. He is currently pursuing the Ph.D. degree in mechanical engineering with the Huazhong University of Science and Technology. His research interests are in intelligent optimisation algorithms and their application to shop scheduling.Xinyu LiXinyu Li received the Ph.D. degree in industrial engineering from Huazhong University of Science and Technology (HUST), China, 2009. He is a Professor of the Department of Industrial & Manufacturing Systems Engineering, State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science & Engineering, HUST. He had published more than 100 refereed papers. His research interests include intelligent scheduling, machine learning etc.Liang GaoLiang Gao received the Ph.D. degree in mechatronic engineering from Huazhong University of
摘要分布式制造正逐渐成为未来的发展趋势。激烈的市场竞争使得制造企业关注生产效率和产品交付。混合流水车间调度问题是制造业中常见的问题。考虑到同一阶段机器的差异性，以最小完工时间和总延迟为条件，研究了具有不相关并行机器的多目标分布式混合流水车间调度问题。针对MODHFSP-UPM，提出了一种改进的多种群遗传算法(IMPGA)。邻域结构是基于元启发式求解算法的关键。为了保证局部搜索的效率，设计了贪婪工作插入工厂间邻域和相应的移动评估方法。为了提高IMPGA的优化能力和稳定性，分别设计了多种群间的分区域协同进化和基于概率抽样的再初始化过程。在计算实验中，随机生成120个实例(包括相同比例的中型和大规模问题)。IMPGA在所有指标(扩散、代距离和倒代距离)上表现最好，明显优于现有的MODHFSP-UPM高效算法。最后，该方法有效地解决了一个聚酯薄膜制造案例，使完工时间和总延误时间分别减少了40%和60%。关键词:不相关并行机的分布式混合流水车间考虑总延迟的多目标调度改进多种群遗传算法贪婪作业插入厂间邻域厂间邻域快速评价方法多种群间的分区域协同进化披露声明作者未报道潜在利益冲突。项目资助:国家自然科学基金项目(no . 51825502, no . U21B2029)。崔尚豪，博士，2021年毕业于华中科技大学工业工程专业，硕士学位。他目前在华中科技大学攻读机械工程博士学位。主要研究方向为智能优化算法及其在车间调度中的应用。李新宇，2009年毕业于华中科技大学工业工程专业，获博士学位。现任华中科技大学机械科学与工程学院工业与制造系统工程系、数字化制造装备与技术国家重点实验室教授。他发表了100多篇论文。主要研究方向为智能调度、机器学习等。高亮，2002年毕业于华中科技大学机电工程专业，获博士学位。现任华中科技大学机械科学与工程学院工业与制造系统工程系、数字化制造装备与技术国家重点实验室教授。他发表了470多篇论文。主要研究方向为运筹学与优化、调度、大数据、机器学习等。高教授是《工业与生产工程》杂志《群与进化计算》的副主编。他是IET协同与智能制造的联合主编。张春江，2011年获华中科技大学工业工程学士学位，2016年获华中科技大学工业工程博士学位。现任华中科技大学讲师。他目前的研究兴趣包括进化算法、约束优化、多目标优化及其在生产调度中的应用。
{"title":"Multi-population genetic algorithm with greedy job insertion inter-factory neighbourhoods for multi-objective distributed hybrid flow-shop scheduling with unrelated-parallel machines considering tardiness","authors":"Hanghao Cui, Xinyu Li, Liang Gao, Chunjiang Zhang","doi":"10.1080/00207543.2023.2262616","DOIUrl":"https://doi.org/10.1080/00207543.2023.2262616","url":null,"abstract":"AbstractDistributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively.KEYWORDS: Distributed hybrid flow shop with unrelated parallel machinesMulti-objective scheduling considering total tardinessImproved multi-population genetic algorithmGreedy job insertion inter-factory neighbourhoodsRapid evaluation method for inter-factory neighbourhoodsSub-regional coevolution among multiple populations Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the National Natural Science Foundation of China under Grant 51825502 and U21B2029.Notes on contributorsHanghao CuiHanghao Cui received the master’s degree in industrial engineering from Huazhong University of Science and Technology, Wuhan, China, 2021. He is currently pursuing the Ph.D. degree in mechanical engineering with the Huazhong University of Science and Technology. His research interests are in intelligent optimisation algorithms and their application to shop scheduling.Xinyu LiXinyu Li received the Ph.D. degree in industrial engineering from Huazhong University of Science and Technology (HUST), China, 2009. He is a Professor of the Department of Industrial & Manufacturing Systems Engineering, State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science & Engineering, HUST. He had published more than 100 refereed papers. His research interests include intelligent scheduling, machine learning etc.Liang GaoLiang Gao received the Ph.D. degree in mechatronic engineering from Huazhong University of ","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718987","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 : 2023-09-26DOI: 10.1080/00207543.2023.2262050
Dhruv Patel, Chandan Kumar Sahu, Rahul Rai
AbstractProcess automation and mass customisation requirements of modern manufacturing systems are driven by artificial intelligence (AI). As AI derives decisions from data, securing the data against tampering is crucial to prevent ensuing operational risks. Additionally, manufacturing systems necessitate collaboration, transparency, and trust among participants while preserving a competitive advantage. Thus, we position blockchain, an enabler of transparent and secure operations, as a security solution for AI-assisted manufacturing systems. In this conceptual viewpoint paper, we present a framework to integrate blockchain in AI-assisted manufacturing systems. We highlight the special needs of manufacturing BCs over generic BCs. We delineate the ways in which manufacturing can be a beneficiary of the synergy between AI and BC. We discuss how BC and AI can accelerate early-phase product design, collaboration, and manufacturing processes and secure supply chains against counterfeit products and for ethical consumerism. Lastly, we identify the needs of modern manufacturing systems and cite a few examples of organisational failures to underscore the importance of security while delineating the significant challenges in adopting blockchain-based solutions in the manufacturing industry.Keywords: Blockchainindustry 4.0machine learningmanufacturingsecurity Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statement (DAS)Data sharing is not applicable to this article as no new data were created or analysed in this study.Notes1 https://www.adidas.com/us/creatorsclub2 https://www.nike.com/nike-by-you3 https://cloudnc.com/4 https://www.xometry.com/5 https://www.skuchain.com/6 https://www.adidas.com/us/creatorsclub7 https://www.nike.com/nike-by-youAdditional informationNotes on contributorsDhruv PatelDhruv Patel received his Master of Science degree in Mechanical Engineering from State University of New York at Buffalo, USA, in 2021. His thesis research focused on ‘Blockchain Based Secure Machine Learning Model Integration For Collaborative Manufacturing’.He is currently working as Data Process Engineer at Radiometer America in California, USA. In this role, he analyses complex production datasets to improve processes and product reliability working along with the R&D team. He specialises in the analysis and interpretation of intricate data sets derived from diverse production processes and equipment. His expertise lies in harnessing the power of manufacturing insights and data analytics to propose enhancements in processes, modifications in measurement techniques, and design refinements, all aimed at elevating product manufacturability and reliability. His work involves close collaboration with cross-functional teams, including Quality, Research and Development, and Production, to identify and implement valuable opportunities for enhancement.Chandan Kumar SahuChandan Kumar Sahu is currently pursuing his Ph.
{"title":"Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing","authors":"Dhruv Patel, Chandan Kumar Sahu, Rahul Rai","doi":"10.1080/00207543.2023.2262050","DOIUrl":"https://doi.org/10.1080/00207543.2023.2262050","url":null,"abstract":"AbstractProcess automation and mass customisation requirements of modern manufacturing systems are driven by artificial intelligence (AI). As AI derives decisions from data, securing the data against tampering is crucial to prevent ensuing operational risks. Additionally, manufacturing systems necessitate collaboration, transparency, and trust among participants while preserving a competitive advantage. Thus, we position blockchain, an enabler of transparent and secure operations, as a security solution for AI-assisted manufacturing systems. In this conceptual viewpoint paper, we present a framework to integrate blockchain in AI-assisted manufacturing systems. We highlight the special needs of manufacturing BCs over generic BCs. We delineate the ways in which manufacturing can be a beneficiary of the synergy between AI and BC. We discuss how BC and AI can accelerate early-phase product design, collaboration, and manufacturing processes and secure supply chains against counterfeit products and for ethical consumerism. Lastly, we identify the needs of modern manufacturing systems and cite a few examples of organisational failures to underscore the importance of security while delineating the significant challenges in adopting blockchain-based solutions in the manufacturing industry.Keywords: Blockchainindustry 4.0machine learningmanufacturingsecurity Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statement (DAS)Data sharing is not applicable to this article as no new data were created or analysed in this study.Notes1 https://www.adidas.com/us/creatorsclub2 https://www.nike.com/nike-by-you3 https://cloudnc.com/4 https://www.xometry.com/5 https://www.skuchain.com/6 https://www.adidas.com/us/creatorsclub7 https://www.nike.com/nike-by-youAdditional informationNotes on contributorsDhruv PatelDhruv Patel received his Master of Science degree in Mechanical Engineering from State University of New York at Buffalo, USA, in 2021. His thesis research focused on ‘Blockchain Based Secure Machine Learning Model Integration For Collaborative Manufacturing’.He is currently working as Data Process Engineer at Radiometer America in California, USA. In this role, he analyses complex production datasets to improve processes and product reliability working along with the R&D team. He specialises in the analysis and interpretation of intricate data sets derived from diverse production processes and equipment. His expertise lies in harnessing the power of manufacturing insights and data analytics to propose enhancements in processes, modifications in measurement techniques, and design refinements, all aimed at elevating product manufacturability and reliability. His work involves close collaboration with cross-functional teams, including Quality, Research and Development, and Production, to identify and implement valuable opportunities for enhancement.Chandan Kumar SahuChandan Kumar Sahu is currently pursuing his Ph.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719301","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 : 2023-09-25DOI: 10.1080/00207543.2023.2262051
Timur Narbaev, Öncü Hazir, Balzhan Khamitova, Sayazhan Talgat
Project managers need reliable predictive analytics tools to make effective project intervention decisions throughout the project life cycle. This study uses Machine learning (ML) to enhance the reliability in project cost forecasting. A XGBoost forecasting model is developed and computational experiments are conducted using real data of 110 projects representing 1268 cost data points. The developed model performs better than some Earned value management (EVM), ML (Random forest, Support vector regression, LightGBM, and CatBoost), and non-linear growth (Gompertz and Logistic) models. The model produces more accurate estimates at the early, middle, and late stages of the project execution, allowing for early warning signals for more effective cost control. In addition, it shows more accurate estimates in most projects tested, suggesting consistency when repeatedly used in practice. Project forecasting studies mainly used ML to estimate the project duration; a few ML studies estimated the project cost at the project’s conceptual stage. This study uses real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle.
{"title":"A machine learning study to improve the reliability of project cost estimates","authors":"Timur Narbaev, Öncü Hazir, Balzhan Khamitova, Sayazhan Talgat","doi":"10.1080/00207543.2023.2262051","DOIUrl":"https://doi.org/10.1080/00207543.2023.2262051","url":null,"abstract":"Project managers need reliable predictive analytics tools to make effective project intervention decisions throughout the project life cycle. This study uses Machine learning (ML) to enhance the reliability in project cost forecasting. A XGBoost forecasting model is developed and computational experiments are conducted using real data of 110 projects representing 1268 cost data points. The developed model performs better than some Earned value management (EVM), ML (Random forest, Support vector regression, LightGBM, and CatBoost), and non-linear growth (Gompertz and Logistic) models. The model produces more accurate estimates at the early, middle, and late stages of the project execution, allowing for early warning signals for more effective cost control. In addition, it shows more accurate estimates in most projects tested, suggesting consistency when repeatedly used in practice. Project forecasting studies mainly used ML to estimate the project duration; a few ML studies estimated the project cost at the project’s conceptual stage. This study uses real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135864998","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 : 2023-09-25DOI: 10.1080/00207543.2023.2246783
Eric H. Grosse, Fabio Sgarbossa, Cecilia Berlin, W. Patrick Neumann
Industry 4.0 was presented more than a decade ago as the fourth industrial revolution, aiming to significantly raise the level of sophistication of interconnected technologies and thus increase manufacturing industries’ profits. However, because the technology-driven narrow focus of Industry 4.0 on performance and profit fails to explain how to increase prosperity for all the stakeholders involved, the European Commission has introduced the concept of Industry 5.0. This vision overcomes the weaknesses of Industry 4.0 by paying explicit attention to outcomes for humans in the system and establishing an environment to create human-centric, resilient, and sustainable systems. Considering these developments, this position paper and editorial introducing the special issue of the International Journal of Production Research elaborates on the transition from Industry 4.0 to 5.0 through 10 papers focusing on the human-centric pillar of Industry 5.0 and its impacts on production and logistics system design and management. This work presents guidance for a more systemic approach needed in future research: to include empirically grounded works and data-driven multimethod approaches that consider diversity in system operators and human factors demands holistically in order to incorporate ethical implications missing from Industry 4.0 – in the pursuit of Industry 5.0 systems.
{"title":"Human-centric production and logistics system design and management: transitioning from Industry 4.0 to Industry 5.0","authors":"Eric H. Grosse, Fabio Sgarbossa, Cecilia Berlin, W. Patrick Neumann","doi":"10.1080/00207543.2023.2246783","DOIUrl":"https://doi.org/10.1080/00207543.2023.2246783","url":null,"abstract":"Industry 4.0 was presented more than a decade ago as the fourth industrial revolution, aiming to significantly raise the level of sophistication of interconnected technologies and thus increase manufacturing industries’ profits. However, because the technology-driven narrow focus of Industry 4.0 on performance and profit fails to explain how to increase prosperity for all the stakeholders involved, the European Commission has introduced the concept of Industry 5.0. This vision overcomes the weaknesses of Industry 4.0 by paying explicit attention to outcomes for humans in the system and establishing an environment to create human-centric, resilient, and sustainable systems. Considering these developments, this position paper and editorial introducing the special issue of the International Journal of Production Research elaborates on the transition from Industry 4.0 to 5.0 through 10 papers focusing on the human-centric pillar of Industry 5.0 and its impacts on production and logistics system design and management. This work presents guidance for a more systemic approach needed in future research: to include empirically grounded works and data-driven multimethod approaches that consider diversity in system operators and human factors demands holistically in order to incorporate ethical implications missing from Industry 4.0 – in the pursuit of Industry 5.0 systems.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135865016","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 : 2023-09-22DOI: 10.1080/00207543.2023.2252108
Likun Wang, Zi Wang, Peter Kendall, Kevin Gumma, Alison Turner, Svetan Ratchev
Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application.
{"title":"Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction","authors":"Likun Wang, Zi Wang, Peter Kendall, Kevin Gumma, Alison Turner, Svetan Ratchev","doi":"10.1080/00207543.2023.2252108","DOIUrl":"https://doi.org/10.1080/00207543.2023.2252108","url":null,"abstract":"Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060550","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 : 2023-09-21DOI: 10.1080/00207543.2023.2258225
Xiuli Liu, Rui Xiong, Sandun C. Perera, Pibin Guo
AbstractWater and energy consumption and carbon emissions caused by humans have vital impacts on the natural environment. Due to the dramatic global environmental pollution problem, understanding the relationship between these factors is emerging as an important approach to realising sustainable development. However, with the strengthening of interregional trade links, it is difficult to manage and evaluate their relationship. Therefore, from the perspective of regional and industrial sectors, using the multiregional input-output (MRIO) model and social network analysis (SNA), our research explores an innovative analytical methodology to evaluate the characteristics of the energy-water-carbon spatial network, and a scheme is proposed to improve it. The results demonstrate that the characteristics of water scarcity and energy enrichment could lead to a net inflow of the water footprint and a net outflow of the energy and carbon footprints. Moreover, traditional high-energy-consumption industrial sectors contribute significantly to the energy and carbon footprints. The energy-water-carbon spatial network correlation is low and unstable, and it lacks rationalisation and balance in resource-based areas. Network-based energy-water-carbon research provides more insights toward understanding the carbon emission reduction responsibilities of industrial supply chains. Our findings provide a reference for reducing the energy-water-carbon footprint and achieving the carbon reduction goal of China.KEYWORDS: Collaborative managementenergy-water-carbon footprintmultiregional input–output analysissocial network analysisspatial network AcknowledgmentsWe also thank anonymous commentators and editors for their helpful suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article.Additional informationFundingThis research was supported by the National Natural Science Foundation of China (Grant No. 42001257 and 71874119), the Research on Philosophy and Social Sciences in Shanxi Province's Colleges and Universities (Grant No. 20210115), and the Major decision-making consulting projects in Shanxi Province in 2022 (Grant No. 2005).Notes on contributorsXiuli LiuXiuli Liu is a Professor in the Research Institute of Resource-based Economics at Shanxi University of Finance and Economics (Taiyuan, China). She received her Ph.D. in Natural Geography from Northwest Normal University (Gansu, China) in June 2013. Her research interests include regional economic management, energy ecology, and resource-based economic transformation. In this paper, she is mainly responsible for conceptualization, writing – review, and editing.Rui XiongRui Xiong is a graduate student in the Research Institute of Resource-based Economics at Shanxi University of Finance and Economics (Taiyuan, China). Her research interests include reso
摘要人类活动造成的水、能源消耗和碳排放对自然环境有着重要的影响。由于全球环境污染问题严重，了解这些因素之间的关系正成为实现可持续发展的重要途径。然而，随着区域间贸易联系的加强，很难管理和评价它们之间的关系。为此，本研究从区域和产业的角度出发，运用多区域投入产出(MRIO)模型和社会网络分析(SNA)，探索了一种创新的评价能源-水-碳空间网络特征的分析方法，并提出了改进方案。结果表明，水资源短缺和能源富集的特征导致了水足迹的净流入和能源足迹和碳足迹的净流出。此外，传统的高能耗工业部门对能源和碳足迹的贡献很大。资源型地区能源-水-碳空间网络相关性低且不稳定，缺乏理性化和平衡性。基于网络的能源-水-碳研究为理解工业供应链的碳减排责任提供了更多的见解。研究结果可为中国减少能源-水-碳足迹，实现碳减排目标提供参考。关键词:协同管理能源-水-碳足迹多区域投入产出分析社会网络分析空间网络致谢我们也感谢匿名评论者和编辑提出的有益建议。披露声明作者未报告潜在的利益冲突。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。本研究得到国家自然科学基金项目(批准号:42001257和71874119)、山西省高校哲学社会科学研究项目(批准号:20210115)和山西省2022年重大决策咨询项目(批准号:2005)的资助。作者简介刘秀丽，山西财经大学(太原)资源经济研究所教授。2013年6月毕业于西北师范大学自然地理专业，获博士学位。主要研究方向为区域经济管理、能源生态、资源型经济转型。在本文中，她主要负责构思、审稿和编辑工作。熊锐，山西财经大学(太原)资源经济研究所研究生。主要研究方向为资源环境与区域可持续发展、能源生态、资源型经济转型。在本文中，她主要负责文章的审稿和编辑工作。Sandun C. Perera，他是内华达大学里诺分校商学院商业分析和运营副教授。他的研究重点是运营管理、供应链管理、医疗保健运营管理中的颠覆性技术，以及运营与业务中其他功能领域之间的接口。在这篇论文中，他主要负责监督、撰稿和编辑。郭丕斌，山西经济管理学院教授。主要从事科技创新、区域发展、能源技术创新研究。中国山西省名师，山西省学术技术带头人。在本文中，他的贡献是提出方法。
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