Pub Date : 2023-10-29DOI: 10.1080/21681015.2023.2264288
Sidharath Joshi, Huynh Trung Luong
{"title":"A two-stage capacity reservation contract model with backup sourcing considering supply side disruptions","authors":"Sidharath Joshi, Huynh Trung Luong","doi":"10.1080/21681015.2023.2264288","DOIUrl":"https://doi.org/10.1080/21681015.2023.2264288","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"233 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1080/21681015.2023.2270990
Xiaohui Shu, Jinqi Song, Qinli Lei, Yangkuo Li
ABSTRACTIn this study, an analytical framework of non-linear co-integration theory is applied to empirically analyze the Chinese money demand function data from Q1 1994 to Q3 2017. The results show that it neglects non-linearities in all variables and that M1 and M2 have long-run nonlinear equilibrium relationships with real GDP, interest rate, inflation rate, the effective exchange rate of RMB, and trade dependence. Further, an enhanced BP neural network is applied to estimate the long-run equilibrium equation, and it is found that the marginal coefficients of M2 are more stable than those of M1. It is suggested that although China was much less dependent on foreign trade since the 2008 financial crisis, the RMB-USD exchange rate and financial variables outside China had a slight impact on China’s money demand because of its closed financial market so that the financial crisis had little effect on China’s financial system.KEYWORDS: Non-linear co-integrationrank testrange testenhanced BP neural networkmoney demand functionstability Disclosure statementThe authors have no relevant financial or non-financial interests to disclose.Notes1. Data on money demand-related variables from 1994 Q1 to 2017 Q3, real and nominal effective exchange rates were obtained from the BIS website, GDP, interest rates, money supply, and RMB-USD exchange rate were obtained from the CEI database.Additional informationFundingThis work was supported by the 2023 Hunan Natural Science Foundation Joint Fund Project: Research on Key Technologies for Measuring, Enhancing, and Visualizing the Competitiveness of Hunan Huaihua International Inland Port under the RCEP Framework (2023JJ50459).Notes on contributorsXiaohui ShuXiaohui Shu is a professor of business administration at Huaihua University. His main research areas of interest is in Economic Statistics, Time Series Analysis, and Regional Economic Development. He has published many academic articles in peer-reviewed recommended journals and has led multiple fund projects.Jinqi SongJinqi Song is an associate professor at Jiangxi Normal University. His research interests include economic statistics, time series analysis, and their applications. He has published multiple academic articles in peer-reviewed journals and has also led several funded research projects.Qinli LeiQinli Lei is a professor in the Department of Statistics at the School of Economics, Jinan University. His primary research interests include sampling surveys, statistical analysis methods, and economic growth. He has published over 50 academic articles in peer-reviewed journals. Additionally, he has successfully led and completed multiple national and provincial research projects, and has also authored more than 10 books and university textbooks.Yangkuo LiYangkuo Li graduated from Jishou University in 2019 with a master's degree. His research interests include statistical modeling and big data analysis. He has done a lot of empirical research in the fields of e
{"title":"Nonlinear cointegration analysis of China’s money demand function stability","authors":"Xiaohui Shu, Jinqi Song, Qinli Lei, Yangkuo Li","doi":"10.1080/21681015.2023.2270990","DOIUrl":"https://doi.org/10.1080/21681015.2023.2270990","url":null,"abstract":"ABSTRACTIn this study, an analytical framework of non-linear co-integration theory is applied to empirically analyze the Chinese money demand function data from Q1 1994 to Q3 2017. The results show that it neglects non-linearities in all variables and that M1 and M2 have long-run nonlinear equilibrium relationships with real GDP, interest rate, inflation rate, the effective exchange rate of RMB, and trade dependence. Further, an enhanced BP neural network is applied to estimate the long-run equilibrium equation, and it is found that the marginal coefficients of M2 are more stable than those of M1. It is suggested that although China was much less dependent on foreign trade since the 2008 financial crisis, the RMB-USD exchange rate and financial variables outside China had a slight impact on China’s money demand because of its closed financial market so that the financial crisis had little effect on China’s financial system.KEYWORDS: Non-linear co-integrationrank testrange testenhanced BP neural networkmoney demand functionstability Disclosure statementThe authors have no relevant financial or non-financial interests to disclose.Notes1. Data on money demand-related variables from 1994 Q1 to 2017 Q3, real and nominal effective exchange rates were obtained from the BIS website, GDP, interest rates, money supply, and RMB-USD exchange rate were obtained from the CEI database.Additional informationFundingThis work was supported by the 2023 Hunan Natural Science Foundation Joint Fund Project: Research on Key Technologies for Measuring, Enhancing, and Visualizing the Competitiveness of Hunan Huaihua International Inland Port under the RCEP Framework (2023JJ50459).Notes on contributorsXiaohui ShuXiaohui Shu is a professor of business administration at Huaihua University. His main research areas of interest is in Economic Statistics, Time Series Analysis, and Regional Economic Development. He has published many academic articles in peer-reviewed recommended journals and has led multiple fund projects.Jinqi SongJinqi Song is an associate professor at Jiangxi Normal University. His research interests include economic statistics, time series analysis, and their applications. He has published multiple academic articles in peer-reviewed journals and has also led several funded research projects.Qinli LeiQinli Lei is a professor in the Department of Statistics at the School of Economics, Jinan University. His primary research interests include sampling surveys, statistical analysis methods, and economic growth. He has published over 50 academic articles in peer-reviewed journals. Additionally, he has successfully led and completed multiple national and provincial research projects, and has also authored more than 10 books and university textbooks.Yangkuo LiYangkuo Li graduated from Jishou University in 2019 with a master's degree. His research interests include statistical modeling and big data analysis. He has done a lot of empirical research in the fields of e","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"45 3.4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/21681015.2023.2270985
Saleh AlBaiti, Naser Nawayseh, Ali Cheaitou
ABSTRACTProductivity and concerns regarding the well-being of workers exposed to vibrations stand as significant topics within labor-intensive sectors. In particular, this study contributes to the existing research by analyzing the problem with linkages among worker skill level, production rates, and vibration exposure. A bi-objective mixed integer linear programming model was employed to optimize both productivity and the exposure to hand-arm vibration in the manufacturing workplace. A sensitivity analysis was carried out to examine the impact of key parameters on the trade-off between productivity and vibration exposure. The results demonstrate the model’s effectiveness in determining the best job rotation schedules by achieving optimal productivity and vibration exposure for low and medium problem sizes. Moreover, the numerical case study points out that strengthening the workforce by adding more proficient skilled workers can maintain a good level of productivity with a decreased likelihood of excessive vibration exposure.KEYWORDS: Job rotationhand–arm vibrationworkforce schedulingoptimizationergonomics Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsSaleh AlBaitiSaleh AlBaiti is a Research Assistant in the Sustainable Engineering Asset Management Research Group (SEAM), University of Sharjah, Sharjah, United Arab Emirates. He received his bachelor’s degree in Electrical and Electronics Engineering from University of Sharjah, United Arab Emirates and obtained his master’s degree in Engineering Management from the same university. His current interests are focused on optimization, vibration, ergonomics, and artificial intelligence.Naser NawaysehNaser Nawayseh is currently a Professor at the Department of Mechanical and Nuclear Engineering at the University of Sharjah, United Arab Emirates. He obtained his PhD in human responses to vibration from the Institute of Sound and Vibration Research (ISVR) at the University of Southampton in the United Kingdom. After his PhD, he worked as a Research Fellow for three years at ISVR where he was involved in several European and International projects. He then moved to the Gulf Region for an academic position. He is a member of the American Society of Mechanical Engineers (ASME) and the European Society of Biomechanics. His research interests are in the areas of biodynamic responses to vibration, postural stability and seating dynamics.Ali CheaitouAli Cheaitou is Associate Professor in Industrial Engineering and Engineering Management, and Coordinator of SEAM Research Group, University of Sharjah, United Arab Emirates. Previously, he served as Chairman of the Department of Industrial Engineering and Engineering Management between 2018 and 2022 and as Coordinator of the M.Sc. and Ph.D. programs in Engineering Management between 2013 and 2017 at the University of Sharjah. Prior to joining the University of Sharjah, Ali Cheaitou worke
{"title":"Optimizing worker productivity and the exposure to hand-arm vibration: a skill-based job rotation model","authors":"Saleh AlBaiti, Naser Nawayseh, Ali Cheaitou","doi":"10.1080/21681015.2023.2270985","DOIUrl":"https://doi.org/10.1080/21681015.2023.2270985","url":null,"abstract":"ABSTRACTProductivity and concerns regarding the well-being of workers exposed to vibrations stand as significant topics within labor-intensive sectors. In particular, this study contributes to the existing research by analyzing the problem with linkages among worker skill level, production rates, and vibration exposure. A bi-objective mixed integer linear programming model was employed to optimize both productivity and the exposure to hand-arm vibration in the manufacturing workplace. A sensitivity analysis was carried out to examine the impact of key parameters on the trade-off between productivity and vibration exposure. The results demonstrate the model’s effectiveness in determining the best job rotation schedules by achieving optimal productivity and vibration exposure for low and medium problem sizes. Moreover, the numerical case study points out that strengthening the workforce by adding more proficient skilled workers can maintain a good level of productivity with a decreased likelihood of excessive vibration exposure.KEYWORDS: Job rotationhand–arm vibrationworkforce schedulingoptimizationergonomics Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsSaleh AlBaitiSaleh AlBaiti is a Research Assistant in the Sustainable Engineering Asset Management Research Group (SEAM), University of Sharjah, Sharjah, United Arab Emirates. He received his bachelor’s degree in Electrical and Electronics Engineering from University of Sharjah, United Arab Emirates and obtained his master’s degree in Engineering Management from the same university. His current interests are focused on optimization, vibration, ergonomics, and artificial intelligence.Naser NawaysehNaser Nawayseh is currently a Professor at the Department of Mechanical and Nuclear Engineering at the University of Sharjah, United Arab Emirates. He obtained his PhD in human responses to vibration from the Institute of Sound and Vibration Research (ISVR) at the University of Southampton in the United Kingdom. After his PhD, he worked as a Research Fellow for three years at ISVR where he was involved in several European and International projects. He then moved to the Gulf Region for an academic position. He is a member of the American Society of Mechanical Engineers (ASME) and the European Society of Biomechanics. His research interests are in the areas of biodynamic responses to vibration, postural stability and seating dynamics.Ali CheaitouAli Cheaitou is Associate Professor in Industrial Engineering and Engineering Management, and Coordinator of SEAM Research Group, University of Sharjah, United Arab Emirates. Previously, he served as Chairman of the Department of Industrial Engineering and Engineering Management between 2018 and 2022 and as Coordinator of the M.Sc. and Ph.D. programs in Engineering Management between 2013 and 2017 at the University of Sharjah. Prior to joining the University of Sharjah, Ali Cheaitou worke","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135267238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1080/21681015.2023.2265925
Fatemeh Hirbod, Tourandokht Karimi, Zahra Mohammadnazari, Masoud Rabbani, Amir Aghsami
ABSTRACTIn the realm of municipal operations, the effective management of municipal solid waste (MSW) stands out as a pivotal undertaking. It necessitates substantial allocations of fixed and variable resources and financial investments. The bulk of these expenditures are associated with the operational facets encompassing waste collection, transportation, and disposal. This research delves into the examination of multiple Disposal Location Arc Routing Problems (LARP) while considering vehicle capacity limitations and the incorporation of waste segregation. The LARP model is designed to identify the optimal locations for depots and three waste disposal sites. The optimization objectives and constraints applied to the LARP model are geared toward enhancing waste collection efficiency and minimizing costs. Additionally, a triangular fuzzy parameter is introduced to represent the demand. To put this model to the test, a real-world case study in the UK is explored to evaluate its performance and practicality. Finally, a series of sensitivity analyses are conducted, offering valuable managerial insights under varying conditions. The inclusion of waste segregation in this waste management model holds considerable significance for managers. This is particularly relevant because it proposes a more effective strategy for waste management when dealing with diverse types of waste.KEYWORDS: Location arc routing problemsmunicipal disposal siteswaste collectionwaste segregationmathematical modelfuzzy Disclosure statementNo potential conflict of interest was reported by the authors.Availability of data and materialDue to the nature of this research, data is available within the text.Additional informationFundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.Notes on contributorsFatemeh HirbodFathemeh Hirbod is an MSc in Industrial Engineering at the School of Industrial Engineering, College of Engineering, University of Tehran. Her main scientific interests include operations research, waste management, healthcare optimization, mathematical modeling.Tourandokht KarimiTourandokht Karimi is an MSc in Industrial Engineering at the School of Industrial Engineering, College of Engineering, University of Tehran. Her main scientific interests include operations research, machine learning, waste management, mathematical modeling.Zahra MohammadnazariZahra Mohammadnazari is currently an assistant lecturer and PhD candidate at Coventry Business College- School of strategy and leadership, Coventry University, United Kingdom. She has several papers in international journals such as Environment, Development and Sustainability, International Journal of Hospital Research, Journal of Ambient Intelligence and Humanized Computing, Multimedia Tools and Applications, etc. Her main scientific interests include operations research, multi-sided platform, machine learning, mathematical modeling, organizational assessm
{"title":"Municipal solid waste management using multiple disposal location-arc routing and waste segregation approach: a real-life case study in England","authors":"Fatemeh Hirbod, Tourandokht Karimi, Zahra Mohammadnazari, Masoud Rabbani, Amir Aghsami","doi":"10.1080/21681015.2023.2265925","DOIUrl":"https://doi.org/10.1080/21681015.2023.2265925","url":null,"abstract":"ABSTRACTIn the realm of municipal operations, the effective management of municipal solid waste (MSW) stands out as a pivotal undertaking. It necessitates substantial allocations of fixed and variable resources and financial investments. The bulk of these expenditures are associated with the operational facets encompassing waste collection, transportation, and disposal. This research delves into the examination of multiple Disposal Location Arc Routing Problems (LARP) while considering vehicle capacity limitations and the incorporation of waste segregation. The LARP model is designed to identify the optimal locations for depots and three waste disposal sites. The optimization objectives and constraints applied to the LARP model are geared toward enhancing waste collection efficiency and minimizing costs. Additionally, a triangular fuzzy parameter is introduced to represent the demand. To put this model to the test, a real-world case study in the UK is explored to evaluate its performance and practicality. Finally, a series of sensitivity analyses are conducted, offering valuable managerial insights under varying conditions. The inclusion of waste segregation in this waste management model holds considerable significance for managers. This is particularly relevant because it proposes a more effective strategy for waste management when dealing with diverse types of waste.KEYWORDS: Location arc routing problemsmunicipal disposal siteswaste collectionwaste segregationmathematical modelfuzzy Disclosure statementNo potential conflict of interest was reported by the authors.Availability of data and materialDue to the nature of this research, data is available within the text.Additional informationFundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.Notes on contributorsFatemeh HirbodFathemeh Hirbod is an MSc in Industrial Engineering at the School of Industrial Engineering, College of Engineering, University of Tehran. Her main scientific interests include operations research, waste management, healthcare optimization, mathematical modeling.Tourandokht KarimiTourandokht Karimi is an MSc in Industrial Engineering at the School of Industrial Engineering, College of Engineering, University of Tehran. Her main scientific interests include operations research, machine learning, waste management, mathematical modeling.Zahra MohammadnazariZahra Mohammadnazari is currently an assistant lecturer and PhD candidate at Coventry Business College- School of strategy and leadership, Coventry University, United Kingdom. She has several papers in international journals such as Environment, Development and Sustainability, International Journal of Hospital Research, Journal of Ambient Intelligence and Humanized Computing, Multimedia Tools and Applications, etc. Her main scientific interests include operations research, multi-sided platform, machine learning, mathematical modeling, organizational assessm","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/21681015.2023.2263009
Amir Zarinchang, Kevin Lee, Iman Avazpour, Jun Yang, Dongxing Zhang, George K. Knopf
Smart warehouses require software-based decision-making tools to manage the receiving, storing, and picking of products. A major challenge in achieving efficient operations is deciding where to store products associated with incoming orders. The storage location assignment problem (SLAP) is more complex in large-size warehouses due to several functional objectives and numerous possible shelving solutions. This paper introduces an artificial intelligence algorithm that seeks to find an acceptable solution to SLAP with presented linear and nonlinear objective functions. The near-optimal technique exploits basin-hopping and simulated-annealing algorithms to find a solution when considering four functional objectives including worker safety, which has not been optimized using similar approaches. The algorithm is experimentally evaluated, and results demonstrate that reasonablely achieved solutions are comparable to those obtained by well-known existing solvers. Furthermore, the problem could be solved with non-linear objectives which is beyond the commercial solvers’ like SCIP capability.
{"title":"Adaptive warehouse storage location assignment with considerations to order-picking efficiency and worker safety","authors":"Amir Zarinchang, Kevin Lee, Iman Avazpour, Jun Yang, Dongxing Zhang, George K. Knopf","doi":"10.1080/21681015.2023.2263009","DOIUrl":"https://doi.org/10.1080/21681015.2023.2263009","url":null,"abstract":"Smart warehouses require software-based decision-making tools to manage the receiving, storing, and picking of products. A major challenge in achieving efficient operations is deciding where to store products associated with incoming orders. The storage location assignment problem (SLAP) is more complex in large-size warehouses due to several functional objectives and numerous possible shelving solutions. This paper introduces an artificial intelligence algorithm that seeks to find an acceptable solution to SLAP with presented linear and nonlinear objective functions. The near-optimal technique exploits basin-hopping and simulated-annealing algorithms to find a solution when considering four functional objectives including worker safety, which has not been optimized using similar approaches. The algorithm is experimentally evaluated, and results demonstrate that reasonablely achieved solutions are comparable to those obtained by well-known existing solvers. Furthermore, the problem could be solved with non-linear objectives which is beyond the commercial solvers’ like SCIP capability.","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/21681015.2023.2262467
Shady El Jalbout, Samira Keivanpour
ABSTRACTDemand for electronic products is growing, as is the volume of waste electrical and electronic equipment (WEEE). To reduce their environmental impact, particularly during their end-of-life, it is important to apply eco-design practices such as design for disassembly (DFD) and design for recycling (DFR) from the beginning of their development. However, these strategies are not systematically implemented by manufacturers due to several challenges, such as the complexity of the methods, the uncertainty and variability of the materials and components, and the lack of knowledge on DFD and DFR. This study aims to develop a body of knowledge (BOK) for DFD and DFR of electronic products to fill this gap. A systematic comparison of different BOKs has led to the proposal of a BOK composed of four main parts: Areas of Knowledge, Tools and Techniques, Skills and Abilities, and Terminology. The proposed framework was applied to lithium-ion batteries (LIBs) as an example of electronic products that require high-tech solutions for their efficient and sustainable management. This approach is essential for high-tech products, as they often contain valuable and scarce materials that need to be recovered and reused in a circular economy. The results showed that the BOK was an effective tool in supporting the sustainable development of batteries.KEYWORDS: Body of knowledgedesign for disassemblydesign for recyclinghigh-tech productslithium-ion batteries Disclosure statementNo potential conflict of interest was reported by the author(s).Acronyms BOK=Body of KnowledgeCAD=Computer-Aided designDFD=Design for DisassemblyDFR=Design for RecyclingEEE=Electrical and Electronic EquipmentEoL=End of LifeEV=Electric VehicleLCA=Life Cycle AssessmentLCC=Life Cycle CostLIB=Lithium-Ion BatteryPLM=Product Lifecycle ManagementTEA=Techno-Economic AssessmentWEEE=Waste Electrical and Electronic EquipmentAdditional informationFundingThe authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) for this research project. FundingThe authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) for this research project [grant number RGPIN-2020-05565].Notes on contributorsShady El JalboutShady El Jalbout is an engineer and a business developer with a background in mechanical and industrial engineering. He holds a bachelor’s degree in mechanical engineering and two master’s degrees, one in mechanical engineering and another in industrial engineering. He recently graduated from the professional master’s program in industrial engineering at Polytechnique Montreal. He currently works in the business development of high-tech and innovative products. His research interests include technology management, sustainability, high-tech product development and business models.Samira KeivanpourSamira Keivanpour is an assistant professor in the Department of Math
摘要电子产品的需求日益增长，废弃电子电气设备(WEEE)的数量也在不断增加。为了减少它们对环境的影响，特别是在它们的生命周期结束时，从它们的开发开始就应用生态设计实践，如拆卸设计(DFD)和回收设计(DFR)，这一点很重要。然而，由于一些挑战，例如方法的复杂性，材料和组件的不确定性和可变性，以及缺乏对DFD和DFR的了解，制造商并没有系统地实施这些策略。本研究旨在建立电子产品DFD和DFR的知识体系(BOK)，以填补这一空白。通过对不同教材的系统比较，我们提出了由四个主要部分组成的教材:知识领域、工具和技术、技能和能力以及术语。该框架被应用于锂离子电池(LIBs)，作为电子产品的一个例子，需要高科技解决方案来实现其高效和可持续的管理。这种方法对于高科技产品至关重要，因为它们通常包含有价值和稀缺的材料，需要在循环经济中回收和再利用。结果表明，BOK是支持电池可持续发展的有效工具。关键词:知识体系拆解设计高科技产品回收设计锂离子电池披露声明作者未报告潜在利益冲突。首字母缩略词BOK=知识体系ecad =计算机辅助设计dfd =拆解设计dfr =回收设计eee =电气和电子设备ol =生命周期结束ev =电动汽车elca =生命周期评估lcc =生命周期成本lib =锂离子电池plm =产品生命周期管理tea =技术经济评估weee =废弃电气和电子设备附加信息资金作者感谢来自自然科学与工程研究委员会的资金支持加拿大国家科学研究委员会(NSERC)资助本研究项目。作者感谢加拿大自然科学与工程研究委员会(NSERC)对本研究项目的财政支持[批准号:RGPIN-2020-05565]。作者简介shady El JalboutShady El Jalbout是一名工程师和业务开发人员，拥有机械和工业工程背景。他拥有机械工程学士学位和两个硕士学位，一个是机械工程学位，另一个是工业工程学位。他最近从蒙特利尔理工大学工业工程专业硕士课程毕业。他目前从事高科技和创新产品的业务开发。他的研究兴趣包括技术管理、可持续发展、高科技产品开发和商业模式。Samira Keivanpour是加拿大montracei理工大学数学与工业工程系的助理教授。她研究供应链和物流管理的可持续解决方案，重点关注报废产品处理、循环制造和工业4.0技术的集成。
{"title":"Development of a body of knowledge for design for disassembly and recycling of high-tech products: a case study on lithium-ion batteries","authors":"Shady El Jalbout, Samira Keivanpour","doi":"10.1080/21681015.2023.2262467","DOIUrl":"https://doi.org/10.1080/21681015.2023.2262467","url":null,"abstract":"ABSTRACTDemand for electronic products is growing, as is the volume of waste electrical and electronic equipment (WEEE). To reduce their environmental impact, particularly during their end-of-life, it is important to apply eco-design practices such as design for disassembly (DFD) and design for recycling (DFR) from the beginning of their development. However, these strategies are not systematically implemented by manufacturers due to several challenges, such as the complexity of the methods, the uncertainty and variability of the materials and components, and the lack of knowledge on DFD and DFR. This study aims to develop a body of knowledge (BOK) for DFD and DFR of electronic products to fill this gap. A systematic comparison of different BOKs has led to the proposal of a BOK composed of four main parts: Areas of Knowledge, Tools and Techniques, Skills and Abilities, and Terminology. The proposed framework was applied to lithium-ion batteries (LIBs) as an example of electronic products that require high-tech solutions for their efficient and sustainable management. This approach is essential for high-tech products, as they often contain valuable and scarce materials that need to be recovered and reused in a circular economy. The results showed that the BOK was an effective tool in supporting the sustainable development of batteries.KEYWORDS: Body of knowledgedesign for disassemblydesign for recyclinghigh-tech productslithium-ion batteries Disclosure statementNo potential conflict of interest was reported by the author(s).Acronyms BOK=Body of KnowledgeCAD=Computer-Aided designDFD=Design for DisassemblyDFR=Design for RecyclingEEE=Electrical and Electronic EquipmentEoL=End of LifeEV=Electric VehicleLCA=Life Cycle AssessmentLCC=Life Cycle CostLIB=Lithium-Ion BatteryPLM=Product Lifecycle ManagementTEA=Techno-Economic AssessmentWEEE=Waste Electrical and Electronic EquipmentAdditional informationFundingThe authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) for this research project. FundingThe authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) for this research project [grant number RGPIN-2020-05565].Notes on contributorsShady El JalboutShady El Jalbout is an engineer and a business developer with a background in mechanical and industrial engineering. He holds a bachelor’s degree in mechanical engineering and two master’s degrees, one in mechanical engineering and another in industrial engineering. He recently graduated from the professional master’s program in industrial engineering at Polytechnique Montreal. He currently works in the business development of high-tech and innovative products. His research interests include technology management, sustainability, high-tech product development and business models.Samira KeivanpourSamira Keivanpour is an assistant professor in the Department of Math","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.1080/21681015.2023.2260384
Zahran Abd Elnaby, Amal Zaher, Ragab K. Abdel-Magied, Heba I. Elkhouly
ABSTRACTThis research integrates machine learning (ML) and Six Sigma’s Define, Measure, Analyze, Improve, and Control (DMAIC) methodology to address these issues. The study details the selection and utilization of ML techniques, including Linear Regression (LR), Artificial Neural Network (ANN), Decision Tree (DT), K-nearest neighbors (KNN), and Cluster Analysis (CA). Implemented at the Innovative Plastic Manufacturing Company in Egypt, this research enhances the consistency of plastic bottle production by addressing issues such as surface marks, flashes, bubbles, and variations in liter capacity. Integrating Six Sigma with ML techniques reduces the average defect rate from approximately 67.8%. It elevates the Sigma level from 3.14 to 4.30, reducing material over-consumption costs from 5% to 1.7% of total manufacturing expenses. Notably, the KNN model achieves the best results for defect testing, with an R-squared value of 98.8%. These methodologies lead to cost reduction, increased competitiveness, and improved product quality when implemented.KEYWORDS: Six Sigmaqualityplastic manufacturingmachine learningDMAICvariabilityplastic fittings Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.Abbreviation PET=Polyethylene terephthalateLSS=Lean Six SigmaML=Machine LearningKNN=k-nearest neighborsDL=deep learningAI=Artificial intelligenceBPNN=back-propagation neural networkSVR=support vector regressionPSO=algorithm to optimize the process parametersDMAIC=Define, Measure, Analyze, Improve, and ControlSIPOC=Suppliers, Input, Process, Output,CustomerDPMO=defects per million opportunitiesPCA=principal component analysisPCIs=process capability indicesDPO=Defects per OpportunityPPM=Parts per MillionLR=Linear regressionDT=Decision treesCA=Cluster Analysis
{"title":"Improving plastic manufacturing processes with the integration of Six Sigma and machine learning techniques: a case study","authors":"Zahran Abd Elnaby, Amal Zaher, Ragab K. Abdel-Magied, Heba I. Elkhouly","doi":"10.1080/21681015.2023.2260384","DOIUrl":"https://doi.org/10.1080/21681015.2023.2260384","url":null,"abstract":"ABSTRACTThis research integrates machine learning (ML) and Six Sigma’s Define, Measure, Analyze, Improve, and Control (DMAIC) methodology to address these issues. The study details the selection and utilization of ML techniques, including Linear Regression (LR), Artificial Neural Network (ANN), Decision Tree (DT), K-nearest neighbors (KNN), and Cluster Analysis (CA). Implemented at the Innovative Plastic Manufacturing Company in Egypt, this research enhances the consistency of plastic bottle production by addressing issues such as surface marks, flashes, bubbles, and variations in liter capacity. Integrating Six Sigma with ML techniques reduces the average defect rate from approximately 67.8%. It elevates the Sigma level from 3.14 to 4.30, reducing material over-consumption costs from 5% to 1.7% of total manufacturing expenses. Notably, the KNN model achieves the best results for defect testing, with an R-squared value of 98.8%. These methodologies lead to cost reduction, increased competitiveness, and improved product quality when implemented.KEYWORDS: Six Sigmaqualityplastic manufacturingmachine learningDMAICvariabilityplastic fittings Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.Abbreviation PET=Polyethylene terephthalateLSS=Lean Six SigmaML=Machine LearningKNN=k-nearest neighborsDL=deep learningAI=Artificial intelligenceBPNN=back-propagation neural networkSVR=support vector regressionPSO=algorithm to optimize the process parametersDMAIC=Define, Measure, Analyze, Improve, and ControlSIPOC=Suppliers, Input, Process, Output,CustomerDPMO=defects per million opportunitiesPCA=principal component analysisPCIs=process capability indicesDPO=Defects per OpportunityPPM=Parts per MillionLR=Linear regressionDT=Decision treesCA=Cluster Analysis","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136280193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/21681015.2023.2259385
Faiza Hamdi, Laila Messaoudi, Jalel Euchi
ABSTRACTDue to globalization and the new characteristics of the business, companies face various challenges to ensure their continuity and competitive advantages. COVID-19 pandemic can be an extreme event that will eventually force many businesses and all industries to redesign and transform their global supply chain model? Challenges concerning mainly reducing the operating cost which is based on selecting the optimal suppliers to provide a reliable product. This study contributes to solving a supplier selection problem under disruption risk due to the lack of literature reviews with a lack of multi-methodological perspective for the fuzzy stochastic notions and quantitative techniques for the quantification of risk alternatives. Prior studies are neglecting to consider the value of risk and prefer to discover chances for optimizing anticipated costs or profits. This study proposed a fuzzy stochastic goal programming approach for selecting the optimal supplier under disruption risk. The proposed model incorporates multiple criteria such as capacity, stochastic demand, and probability of disturbance. The problem of stochastic combinatorial optimization obtained is presented as a program of fuzzy random aim by integrating techniques of value at risk and conditional risk value. Numeric samples and calculation results are included. The results of the models help the decision-maker to optimize the selection of suppliers in the event of a disturbance risk problem by an estimated value at risk and by simultaneously minimizing the conditional value of the risk and demonstrate the efficacy and acceptability of the created risk-averse technique as well as the effects of risk factors on our model behavior.KEYWORDS: Screening supplierrisk of disturbancefuzzy stochastic objectiveConditional value of riskrisk aversion Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsFaiza HamdiFaiza Hamdi is an Assistant Professor at the College of Business, University of Jeddah in Saudi Arabia, where she is an integral member of the Supply Chain Management Department. Dr. HAMDI brings a diverse academic background to her role, having earned a Ph.D. in Quantitative Methods from the University of Sfax, Tunisia, and another in Industrial Engineering from the University of Toulouse, France. Her academic pursuits align closely with her research interests, which encompass a broad spectrum of subjects within the field of Supply Chain Management (SCM). Dr. HAMDI's expertise extends to areas such as logistics, optimization, inventory management, simulation, lean manufacturing, and green supply chain practices. Currently, her research is particularly focused on the intricate realms of optimization in supply chain management and the dynamic landscape of risk management within this domain.Laila MessaoudiLaila Messaoudi serves as an Assistant Professor at Gabes University in Tunisia. She earned her Ph.D. in quant
{"title":"A fuzzy stochastic goal programming for selecting suppliers in case of potential disruption","authors":"Faiza Hamdi, Laila Messaoudi, Jalel Euchi","doi":"10.1080/21681015.2023.2259385","DOIUrl":"https://doi.org/10.1080/21681015.2023.2259385","url":null,"abstract":"ABSTRACTDue to globalization and the new characteristics of the business, companies face various challenges to ensure their continuity and competitive advantages. COVID-19 pandemic can be an extreme event that will eventually force many businesses and all industries to redesign and transform their global supply chain model? Challenges concerning mainly reducing the operating cost which is based on selecting the optimal suppliers to provide a reliable product. This study contributes to solving a supplier selection problem under disruption risk due to the lack of literature reviews with a lack of multi-methodological perspective for the fuzzy stochastic notions and quantitative techniques for the quantification of risk alternatives. Prior studies are neglecting to consider the value of risk and prefer to discover chances for optimizing anticipated costs or profits. This study proposed a fuzzy stochastic goal programming approach for selecting the optimal supplier under disruption risk. The proposed model incorporates multiple criteria such as capacity, stochastic demand, and probability of disturbance. The problem of stochastic combinatorial optimization obtained is presented as a program of fuzzy random aim by integrating techniques of value at risk and conditional risk value. Numeric samples and calculation results are included. The results of the models help the decision-maker to optimize the selection of suppliers in the event of a disturbance risk problem by an estimated value at risk and by simultaneously minimizing the conditional value of the risk and demonstrate the efficacy and acceptability of the created risk-averse technique as well as the effects of risk factors on our model behavior.KEYWORDS: Screening supplierrisk of disturbancefuzzy stochastic objectiveConditional value of riskrisk aversion Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsFaiza HamdiFaiza Hamdi is an Assistant Professor at the College of Business, University of Jeddah in Saudi Arabia, where she is an integral member of the Supply Chain Management Department. Dr. HAMDI brings a diverse academic background to her role, having earned a Ph.D. in Quantitative Methods from the University of Sfax, Tunisia, and another in Industrial Engineering from the University of Toulouse, France. Her academic pursuits align closely with her research interests, which encompass a broad spectrum of subjects within the field of Supply Chain Management (SCM). Dr. HAMDI's expertise extends to areas such as logistics, optimization, inventory management, simulation, lean manufacturing, and green supply chain practices. Currently, her research is particularly focused on the intricate realms of optimization in supply chain management and the dynamic landscape of risk management within this domain.Laila MessaoudiLaila Messaoudi serves as an Assistant Professor at Gabes University in Tunisia. She earned her Ph.D. in quant","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/21681015.2023.2257211
Kun He
ABSTRACTThe distribution center is a transit place for goods in the logistics network, used to achieve the distribution of goods. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness due to the low temperature or ultra-low temperature requirements of transport objects. Aiming at the problems of high cost and low efficiency of cold chain distribution center location, a new location model of cold chain distribution center is developed. The Affinity Propagation (AP) clustering algorithm is used to simplify location selection. And combine the binary semantics with entropy to reduce the subjectivity of the binary semantics in the process of experiment. The results show that the location selection using the research method is optimal, and the problem that multiple secondary distribution centers are the same retailer will not appear. The research method is more objective and scientific for the location of cold chain distribution centers.Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness. Distribution center is the transfer place of goods in the logistics network, which plays an important role in the logistics supply chain system. A new location model of cold chain distribution center is developed. Using Affinity Propagation (AP) clustering algorithm to simplify location selection and combining binary semantics with entropy method can further improve the objectivity of influencing factor index weights. The results show that the problem of multiple secondary distribution centers providing logistics services for the same retailer will not occur in the research method, and the location selection results are optimal. And the logistics cost can be reduced by 0.042%. This study improves the distribution efficiency, enhances the customer experience of cold chain logistics distribution, and provides certain technology and reference value for the development of cold chain logistics distribution.KEYWORDS: AP clustering algorithmcostdistribution centercold chain logisticssite selection Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKun HeKun He, a teacher at Chuzhou Polytechnic, specializing in the field of economics.
{"title":"AP clustering algorithm for analysis of the impact of cold chain distribution center location on logistics costs","authors":"Kun He","doi":"10.1080/21681015.2023.2257211","DOIUrl":"https://doi.org/10.1080/21681015.2023.2257211","url":null,"abstract":"ABSTRACTThe distribution center is a transit place for goods in the logistics network, used to achieve the distribution of goods. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness due to the low temperature or ultra-low temperature requirements of transport objects. Aiming at the problems of high cost and low efficiency of cold chain distribution center location, a new location model of cold chain distribution center is developed. The Affinity Propagation (AP) clustering algorithm is used to simplify location selection. And combine the binary semantics with entropy to reduce the subjectivity of the binary semantics in the process of experiment. The results show that the location selection using the research method is optimal, and the problem that multiple secondary distribution centers are the same retailer will not appear. The research method is more objective and scientific for the location of cold chain distribution centers.Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness. Distribution center is the transfer place of goods in the logistics network, which plays an important role in the logistics supply chain system. A new location model of cold chain distribution center is developed. Using Affinity Propagation (AP) clustering algorithm to simplify location selection and combining binary semantics with entropy method can further improve the objectivity of influencing factor index weights. The results show that the problem of multiple secondary distribution centers providing logistics services for the same retailer will not occur in the research method, and the location selection results are optimal. And the logistics cost can be reduced by 0.042%. This study improves the distribution efficiency, enhances the customer experience of cold chain logistics distribution, and provides certain technology and reference value for the development of cold chain logistics distribution.KEYWORDS: AP clustering algorithmcostdistribution centercold chain logisticssite selection Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKun HeKun He, a teacher at Chuzhou Polytechnic, specializing in the field of economics.","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/21681015.2023.2256962
Roberto Rosario Corsini
ABSTRACTThis paper addresses the dynamics of a two-product closed-loop supply chain with realistic assumptions on production capacity constraints. The closed-loop supply chain is also subject to unpredictable disruptions, which lead to the non-stationarity of customer demand. The factory employs a production control policy to decide the product type to be processed. We propose a novel production control policy, named the Adaptive Hedging Corridor Policy, which makes decisions on production capacity based on the demand evolution. The proposed strategy is compared with well-known production control policies widely used in literature, such as DDMRP. Simulation results demonstrate the benefits of implementing an adaptive production control as it enables the closed-loop supply chain to enhance customer service level and bullwhip effect. Additionally, a sensitivity analysis is provided to assess the influence of experimental factors on the performance. The analysis highlights the significance of return flows and manufacturing operations for the closed-loop supply chain.KEYWORDS: supply chain dynamicsdisruptionchangeoverproduction controlbullwhipDDMRP Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Università di Catania [PIACERI 2020/22 – GOSPEL / 59722022261].Notes on contributorsRoberto Rosario CorsiniRoberto Rosario Corsini, PhD, is a postdoctoral researcher in Technology and Manufacturing Systems at the University of Catania (Italy). He holds a PhD in Complex Systems for Physical, Socio-economics, and Life Sciences and a Master’s degree in Management Engineering from the University of Catania. His professional background includes roles as Production Planner and Healthcare Management Engineer. His research activities deal with the application of AI frameworks, optimization techniques, and simulation models for Manufacturing Systems, Supply Chains, and Healthcare Systems
{"title":"Adaptive production control of two-product closed-loop supply chain dynamics under disruptions","authors":"Roberto Rosario Corsini","doi":"10.1080/21681015.2023.2256962","DOIUrl":"https://doi.org/10.1080/21681015.2023.2256962","url":null,"abstract":"ABSTRACTThis paper addresses the dynamics of a two-product closed-loop supply chain with realistic assumptions on production capacity constraints. The closed-loop supply chain is also subject to unpredictable disruptions, which lead to the non-stationarity of customer demand. The factory employs a production control policy to decide the product type to be processed. We propose a novel production control policy, named the Adaptive Hedging Corridor Policy, which makes decisions on production capacity based on the demand evolution. The proposed strategy is compared with well-known production control policies widely used in literature, such as DDMRP. Simulation results demonstrate the benefits of implementing an adaptive production control as it enables the closed-loop supply chain to enhance customer service level and bullwhip effect. Additionally, a sensitivity analysis is provided to assess the influence of experimental factors on the performance. The analysis highlights the significance of return flows and manufacturing operations for the closed-loop supply chain.KEYWORDS: supply chain dynamicsdisruptionchangeoverproduction controlbullwhipDDMRP Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Università di Catania [PIACERI 2020/22 – GOSPEL / 59722022261].Notes on contributorsRoberto Rosario CorsiniRoberto Rosario Corsini, PhD, is a postdoctoral researcher in Technology and Manufacturing Systems at the University of Catania (Italy). He holds a PhD in Complex Systems for Physical, Socio-economics, and Life Sciences and a Master’s degree in Management Engineering from the University of Catania. His professional background includes roles as Production Planner and Healthcare Management Engineer. His research activities deal with the application of AI frameworks, optimization techniques, and simulation models for Manufacturing Systems, Supply Chains, and Healthcare Systems","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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