Journal of Industrial and Production Engineering最新文献

{"title":"Workshop layout optimization method based on sparrow search algorithm: a new approach","authors":"Shenghao Bi, Liangshan Shao, Jiatian Zheng, Rui Yang","doi":"10.1080/21681015.2024.2302630","DOIUrl":"https://doi.org/10.1080/21681015.2024.2302630","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"25 4","pages":""},"PeriodicalIF":4.5,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443114","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}

{"title":"On the power and robustness of phase I nonparametric Shewhart-type charts using sequential normal scores","authors":"G. Hernández-Zamudio, V. Tercero-Gómez, W. J. Conover, L. Benavides-Vázquez, M. Beruvides","doi":"10.1080/21681015.2023.2292114","DOIUrl":"https://doi.org/10.1080/21681015.2023.2292114","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"11 4","pages":""},"PeriodicalIF":4.5,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173514","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}

{"title":"Sustainable planning and design for eco-industrial parks using integrated multi-objective optimization and fuzzy analytic hierarchy process","authors":"Niroot Wattanasaeng, K. Ransikarbum","doi":"10.1080/21681015.2023.2292106","DOIUrl":"https://doi.org/10.1080/21681015.2023.2292106","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"133 2","pages":""},"PeriodicalIF":4.5,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138978595","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}

{"title":"Analysis of the BP neural network comprehensive competitiveness evaluation model for the development evaluation of B2B E-commerce enterprises","authors":"Yunting Tuo","doi":"10.1080/21681015.2023.2288956","DOIUrl":"https://doi.org/10.1080/21681015.2023.2288956","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"6 3","pages":""},"PeriodicalIF":4.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621358","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}

{"title":"Product lifecycle management system implementation and market responsiveness: the role of partner management capability","authors":"Yi-Ming Tai, Yu-Chung Tsao, Tsung-Hui Chen","doi":"10.1080/21681015.2023.2282583","DOIUrl":"https://doi.org/10.1080/21681015.2023.2282583","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"10 3","pages":""},"PeriodicalIF":4.5,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139269522","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}

{"title":"Financial management early warning model of enterprise circular economy based on chaotic particle swarm optimization algorithm","authors":"Jinlan Jiao","doi":"10.1080/21681015.2023.2280184","DOIUrl":"https://doi.org/10.1080/21681015.2023.2280184","url":null,"abstract":"","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"48 3","pages":""},"PeriodicalIF":4.5,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139268351","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}

{"title":"Cost-based hybrid flow shop scheduling with uniform machine optimization using an improved tiki-taka algorithm","authors":"Mohd Fadzil Faisae Ab Rashid, Muhammad Ammar Nik Mu’tasim","doi":"10.1080/21681015.2023.2276108","DOIUrl":"https://doi.org/10.1080/21681015.2023.2276108","url":null,"abstract":"ABSTRACTCost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a comprehensive cost-based HFSS model, encompassing electricity, labor, maintenance, and penalty costs. Next, the Tiki-Taka Algorithm (TTA) is improved by increasing the exploration capability to optimize the problem. The cost-based HFSS model and TTA algorithm have been tested using benchmark and case study problems. The results indicated that the TTA consistently outperforms other algorithms. It delivers the best mean fitness and better solution distribution. In industrial contexts, the TTA able to reduces costs by 2.8% to 12.0% compared to other approaches. This holistic cost-based HFSS model empowers production planners to make more informed decisions. Furthermore, the improved TTA shows promise for broader applicability in various combinatorial optimization domains.KEYWORDS: Hybrid flow shop schedulingproduction costtiki-taka algorithmcost optimization AcknowledgmentsThe authors would like to acknowledge Universiti Malaysia Pahang for funding this research under the UMP Grant RDU223017.Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe datasets generated during and/or analyzed during the current study are available as follow:(i) Computational experiment purpose: Carlier, J., & Neron, E. (2000). An Exact Method for Solving the Multi-Processor Flow-Shop. RAIRO-Oper. Res., 34(1), 1–25. https://doi.org/10.1051/ro:2000103.(ii) Practical application data: Data available on request from the authors.Supplemental materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/21681015.2023.2276108Additional informationFundingThe work was supported by the Universiti Malaysia Pahang [RDU223017].Notes on contributorsMohd Fadzil Faisae Ab RashidDr. Mohd Fadzil Faisae Ab. Rashid is currently an Associate Professor at the Faculty of Mechanical & Automotive Engineering Technology, University Malaysia Pahang. He received a Bachelor’s Degree in Mechanical (Industry) from Universiti Teknologi Malaysia in 2003, a Master of Engineering (Manufacturing) from Universiti Malaysia Pahang in 2007, and a Ph.D. in Manufacturing System Optimization from Cranfield University, the United Kingdom in 2013. His research interests are in engineering optimization, particularly focusing on manufacturing systems, metaheuristics, and discrete event simulation techniques.Muhammad Ammar Nik Mu’tasimMr. Muhammad Ammar Nik Mu’tasim is a lecturer at the Faculty of Mechanical & Automotive Engineering Technology, University Malaysia Pahang. He holds a Bachelor's degree in Mechanical Engineering from Universiti Malaysia Pahang, as well as a Master of Engineering in Mechanical Engineering from Universit","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":" 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242567","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}

{"title":"An artificial intelligence transformation model – pod redesign of photomasks in semiconductor manufacturing","authors":"Shu-Kai S. Fan, Ming-Shen Chen, Chia-Yu Hsu, You-Jin Park","doi":"10.1080/21681015.2023.2279101","DOIUrl":"https://doi.org/10.1080/21681015.2023.2279101","url":null,"abstract":"ABSTRACTThis paper proposes a new enterprise intelligentization framework, by making the transition from process transformation to artificial intelligence (AI) transformation. The novel transformation framework can be decomposed into the conceptual model of AI strategic planning, the procedural model, the operational model, and the analytics model. For leading-edge microchip production, a new AI transformation project regarding the reticle SMIF pod (RSP) transport system designed by a medium-sized semiconductor tool vendor in Taiwan is presented. The technical advantages, gained from the implementation of the presented AI transformation project, over the existing RSP systems are manifold. The throughput and yield rate significantly increase on a semiconductor-fabrication-plant basis. The clean room construction costs less by approximately 3 million dollars per FAB, mainly attributed to the redesigned automatic optical inspection flow. The proposed model-based framework proves to be a viable tool from the process transformation to the AI transformation in the semiconductor manufacturing.KEYWORDS: Process transformationAI transformationassisted intelligencesemiconductor manufacturingreticle SMIF pod (RSP) Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsShu-Kai S. FanShu-Kai S. Fan received the Ph.D. degree in Industrial, Manufacturing and Systems Engineering from the University of Texas at Arlington in 1996. He is currently a professor in the Department of Industrial Engineering and Management, National Taipei University of Technology (NTUT), Taiwan, R.O.C. Dr. Fan now serves as Editor-in-Chief of Engineering Optimization published by Taylor and Francis. His research interests include quality engineering, image processing, big data analytics, machine/deep learning, and advanced process control of semiconductor manufacturing.Ming-Shen ChenMing-Shen Chen received his M.S. degree in Information and Financial Management from National Taipei University of Technology (NTUT), Taiwan, R.O.C. His research interests include advanced process control of semiconductor manufacturing, and deep learning applications in industry. He now works in Stek Co. Ltd as the general manager.Chia-Yu HsuChia-Yu Hsu is a professor in the Department of Industrial Management, National Taiwan University of Science and Technology (NTUST). He received B.S. in Statistics from National Chung Kung University (2002), M.S. in Industrial Engineering and Engineering Management from National Tsing Hua University (2004) and Ph.D. in Industrial Engineering and Engineering Management from National Tsing Hua University (2009). His current research interests include big data analytics, machine learning & deep learning, manufacturing intelligence, defect inspection, fault detection, time series data analysis and predictive maintenance.You-Jin ParkYou-Jin Park is currently a professor in the Department of Industrial Eng","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342035","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}

{"title":"Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach","authors":"Sachin B. Khot, S. Thiagarajan","doi":"10.1080/21681015.2023.2269926","DOIUrl":"https://doi.org/10.1080/21681015.2023.2269926","url":null,"abstract":"ABSTRACTEfforts to merge sustainability and resilience within the automotive industry’s supply chain models have proven challenging. This paper proposes a novel non-linear closed-loop supply chain management framework tailored to the tire industry supply chain from the automotive sector to address the issue of exploring interrelationships. Framework employs trapezoidal linguistic cubic fuzzy Z-score technique for order of preference by similarity to the ideal solution ranking approach to prioritize resilience strategies to maintain sustainability performance during sudden disturbances. Furthermore, Gaussian fuzzy optimization-based non-linear model predictive control acts as a feedback controller to integrate sustainability and resilience by providing a stable output based on the objective function related to sustainability dimensions. An experimental study assesses the impact of resilience strategies on total supply chain costs, highlighting significant cost savings. Adopting strategies like multiple sourcing, information sharing, and improved design quality of the supply chain keeps total expected costs optimal for various sustainability levels.KEYWORDS: Resilience strategysustainable supply chainclosed-loop supply chainnon-linear model predictive controlfuzzy optimal control Abbreviations=DescriptionNLCLSCM=Non-linear closed loop supply chain managementSC=Supply ChainSCM=Supply Chain ManagementTOPSIS=Technique for Order of Preference by Similarity to Ideal SolutionTLCF-ZTOPSIS=Trapezoidal Linguistic Cubic Fuzzy Z-score Technique for Order of Preference by Similarity to Ideal SolutionGFO-NMPC=Gaussian Fuzzy Optimization-based Non-Linear Model Predictive ControlCO2=Carbon dioxideRS=Resilient strategyMILP=Mixed Integer Linear ProgrammingDEMATEL=Decision-Making Trial and Evaluation LaboratoryDMU=Decision Making UnitClosed-loop SC=Closed-loop Supply ChainDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSachin B. KhotSachin B. Khot is a Ph.D student at Vellore Institute of Technology, Vellore, India. He is Masters in Industrial Engineering from National Institute of Technology, Tiruchirappalli. He is currently working as Assistant professor at Rajarambapu Institute of Technology, Rajaramnagar, India. He has 2 years of industrial experience and around 10 years of academic experience. He is teaching Industrial Engineering, Supply Chain Management, Total Quality Management and Additive Manufacturing to the UG students. He has also taught Supply Chain Management to PG Students. He has guided 10 UG Projects and 3 PG Projects. He has research interests in Supply Chain Management, productivity improvement, decision making under uncertainty, risk management in supply chain and engineering education etc.S. ThiagarajanS. Thiagarajan is a Professor in the Department of Manufacturing Engineering, School of Mechanical Engineering, VIT University, Vellore, Tamilnadu, India. He has aroun","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"377 1-3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326515","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}

{"title":"Exploring IoT adoption and operational efficiency within the international chilled beverages industry","authors":"Niazy Kioufi, Weifeng Chen","doi":"10.1080/21681015.2023.2270994","DOIUrl":"https://doi.org/10.1080/21681015.2023.2270994","url":null,"abstract":"ABSTRACTResearch on the adoption of IoT technology in the global chilled beverages industry is limited. The outbreak of the COVID-19 pandemic had a substantial impact on chilled beverage sales and disrupted the operations of organizations within this industry. In order to navigate the challenges posed by this “new normal,” the implementation of IoT adoption strategies becomes increasingly vital. We conducted semi-structured interviews with 19 leaders representing 14 chilled beverage companies across five continents, following the Gioia methodology. Our study findings shed light on the critical role of operational efficiency and organizational capability in the successful adoption of IoT technology. By focusing on these aspects, our study aims to contribute to the seamless integration of IoT technology into the chilled beverages industry. To align with this goal, we propose an innovative IoT adoption framework tailored to support managers in navigating this understudied topic.KEYWORDS: Operational efficienciesbottler operationsInternet of thingstechnology adoptionCovid-19organizational capability Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsNiazy KioufiNiazy Kioufi is the CEO and founder of eyya.com, a London-based IoT solutions company. He holds a Ph.D. from Brunel University London, an MBA from London Metropolitan University, and an MSc. in Computing from North London University. His research primarily centers around IoT adoption and Dynamic Capabilities. With a wealth of experience in software and technology startups, Niazy has occupied diverse leadership roles, including CEO, COO, and VP of Professional Services. His areas of expertise encompass Internet of Things (IoT), eBusiness, document automation, business process automation, eCommerce, business strategy, and consulting services.Weifeng ChenWeifeng Chen is a Reader at Brunel Business School, Brunel University London. His expertise lies in technology adoption, business model innovation, digitalization, social transformation, and sustainability. He earned his Ph.D. from Brunel University London. Dr. Chen's research primarily explores the influence of disruptive technologies like Artificial Intelligence and Blockchain on innovative business models and ecosystem co-creation within sustainable global value chains. He also investigates contemporary challenges in international strategic innovation management and the management of international talents for new products, services, and applications. He has been involved in collaborative research projects with organizations such as the United Nations, the EU, and the British Council, working across various industries and public sector entities both in the UK and internationally. Furthermore, he has offered consultancy services in the realm of business model innovation and optimization. His research findings have been published in esteemed journals, including the Journa","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":"52 11-12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271814","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}