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Ron S. Kenett

Personal Details

First Name:Ron
Middle Name:S.
Last Name:Kenett
Suffix:
RePEc Short-ID:pke139
[This author has chosen not to make the email address public]
http://www.kpa-group.com
POBox 2525, Raanana 43100, Israel

Affiliation

(14%) Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche
Università degli Studi di Torino

Torino, Italy
http://www.esomas.unito.it/
RePEc:edi:dstorit (more details at EDIRC)

(1%) Ekonomska fakuteta
Univerza v Ljubljani

Ljubljana, Slovenia
http://www.ef.uni-lj.si/
RePEc:edi:feuljsi (more details at EDIRC)

(60%) Samuel Neaman Institute, Technion

http://www.neaman.org.il/EN/Ron-Kenett
Israel, Haifa

Research output

as
Jump to: Working papers Articles

Working papers

  1. Elisa Luciano & Matteo Cattaneo & Ron Kenett, 2023. "Adversarial AI in Insurance: Pervasiveness and Resilience," Papers 2301.07520, arXiv.org.
  2. Silvia Figini & Ron Kenett & SILVIA SALINI, 2010. "Integrating Operational and Financial Risk Assessments," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1099, Universitá degli Studi di Milano.
  3. Silvia FIGINI & Ron S. KENETT & Silvia SALINI, 2010. "Integrating operational and financial risk assessments," Departmental Working Papers 2010-002, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  4. Silvia Salini & Ron Kenett, 2008. "Relative Linkage Disequilibrium: A New measure for association rules," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1069, Universitá degli Studi di Milano.
  5. Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-033, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  6. Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-33, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

Articles

  1. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
  2. Behnaz Amerirad & Matteo Cattaneo & Ron S. Kenett & Elisa Luciano, 2023. "Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies," Risks, MDPI, vol. 11(1), pages 1-17, January.
  3. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
  4. Ron S. Kenett & Abraham Rubinstein, 2021. "Generalizing research findings for enhanced reproducibility: an approach based on verbal alternative representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4137-4151, May.
  5. Ron S. Kenett, 2020. "A review of data science in business and industry and a future view by G. Vicario and S. Coleman," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 30-32, January.
  6. Ron Kenett, 2019. "A review of: The class of CUB models: statistical foundations, inferential issues and empirical evidence by Domenico Piccolo and Rosaria Simone," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 457-458, September.
  7. Kenett Ron S. & Shmueli Galit, 2016. "From Quality to Information Quality in Official Statistics," Journal of Official Statistics, Sciendo, vol. 32(4), pages 867-885, December.
  8. Ron S. Kenett & Galit Shmueli, 2015. "A special issue on: Actual impact and future perspectives on stochastic modelling in business and industry," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 1-2, January.
  9. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.
  10. Muslima Zahan & Ron S. Kenett, 2013. "Modeling and Forecasting Energy Consumption in the Manufacturing Industry in South Asia," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 87-98.
  11. Ron S. Kenett & Shelemyahu Zacks, 2013. "Industrial statistics applications in the semiconductor industry: some examples," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(4), pages 319-333, July.
  12. Ron S. Kenett, 2012. "‘The COM‐Poisson model for count data: a survey of methods and applications’ by K. Sellers, S. Borle and G. Shmueli," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(2), pages 117-121, March.
  13. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
  14. Ron S. Kenett & Silvia Salini, 2011. "Rejoinder to ‘Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 484-486, September.
  15. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
  16. Ron S. Kenett, 2009. "‘Post‐financial meltdown: What do the services industries need from us now?’ by Roger W. Hoerl and Ronald D. Snee: Discussion 2," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(5), pages 527-531, September.
  17. Ron Kenett, 2006. "On the planning and design of sample surveys," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(4), pages 405-415.
  18. R. Kenett & P. Thyregod, 2006. "Aspects of statistical consulting not taught by academia," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(3), pages 396-411, August.
  19. Agnihothri, Saligrama R. & Kenett, Ron S., 1995. "The impact of defects on a process with rework," European Journal of Operational Research, Elsevier, vol. 80(2), pages 308-327, January.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-33, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

    Cited by:

    1. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    2. Leonelli, Manuele & Varando, Gherardo, 2024. "Robust learning of staged tree models: A case study in evaluating transport services," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    3. Donata Marasini & Piero Quatto & Enrico Ripamonti, 2017. "Inferential confidence intervals for fuzzy analysis of teaching satisfaction," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1513-1529, July.
    4. Di Pietro, Laura & Guglielmetti Mugion, Roberta & Musella, Flaminia & Renzi, Maria Francesca & Vicard, Paola, 2017. "Monitoring an airport check-in process by using Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 235-247.
    5. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    6. Mandhani, Jyoti & Nayak, Jogendra Kumar & Parida, Manoranjan, 2020. "Interrelationships among service quality factors of Metro Rail Transit System: An integrated Bayesian networks and PLS-SEM approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 320-336.
    7. Massimo Florio, 2021. "Knowledge creation: new frontiers for public investment," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(3), pages 379-386, September.
    8. P. Berchialla & S. Snidero & A. Stancu & C. Scarinzi & R. Corradetti & D. Gregori, 2012. "Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 867-874, September.
    9. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    10. Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
    11. Flaminia Musella & Paola Vicard, 2015. "Object-oriented Bayesian networks for complex quality management problems," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 115-133, January.
    12. Massimo Florio & Francesco Giffoni & Anna Giunta & Emanuela Sirtori, 2018. "Big science, learning, and innovation: evidence from CERN procurement," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(5), pages 915-936.
    13. Zhang, Ya & Zhao, Hai & He, Xuan & Pei, Fan-Dong & Li, Guang-Guang, 2016. "Bayesian prediction of earthquake network based on space–time influence domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 138-149.
    14. Pier Ferrari & Silvia Salini, 2011. "Complementary Use of Rasch Models and Nonlinear Principal Components Analysis in the Assessment of the Opinion of Europeans About Utilities," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 53-69, April.
    15. E. Cene & F. Karaman, 2015. "Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1572-1590, July.

Articles

  1. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.

    Cited by:

    1. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.

  2. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.

    Cited by:

    1. Inbal Yahav & Galit Shmueli, 2014. "Outcomes matter: estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores," Annals of Operations Research, Springer, vol. 216(1), pages 101-128, May.
    2. Pierpaolo D’Urso & Vincenzina Vitale, 2020. "Bayesian Networks Model Averaging for Bes Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(3), pages 897-919, October.
    3. Ron S. Kenett & Abraham Rubinstein, 2021. "Generalizing research findings for enhanced reproducibility: an approach based on verbal alternative representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4137-4151, May.
    4. Pierpaolo D’Urso & Vincenzina Vitale, 2021. "Modeling Local BES Indicators by Copula-Based Bayesian Networks," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 823-847, February.
    5. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    6. Galit Shmueli, 2020. "Discussion on “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow," Biometrics, The International Biometric Society, vol. 76(2), pages 561-563, June.
    7. Rosaria Simone, 2023. "Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 79-105, April.
    8. Biemer Paul & Trewin Dennis & Bergdahl Heather & Japec Lilli, 2014. "A System for Managing the Quality of Official Statistics," Journal of Official Statistics, Sciendo, vol. 30(3), pages 381-415, September.
    9. Mahsa Ashouri & Kate Cai & Furen Lin & Galit Shmueli, 2018. "Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan," Service Science, INFORMS, vol. 10(1), pages 58-75, March.
    10. Kenett Ron S. & Shmueli Galit, 2016. "From Quality to Information Quality in Official Statistics," Journal of Official Statistics, Sciendo, vol. 32(4), pages 867-885, December.
    11. Askitas, Nikos, 2016. "Big Data Is a Big Deal But How Much Data Do We Need?," IZA Discussion Papers 9988, Institute of Labor Economics (IZA).
    12. Coleman Shirley Y., 2016. "Data-Mining Opportunities for Small and Medium Enterprises with Official Statistics in the UK," Journal of Official Statistics, Sciendo, vol. 32(4), pages 849-865, December.
    13. Paola Zola & Paulo Cortez & Costantino Ragno & Eugenio Brentari, 2019. "Social Media Cross-Source and Cross-Domain Sentiment Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1469-1499, September.
    14. Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
    15. Ruojing Zhang & Marta Indulska & Shazia Sadiq, 2019. "Discovering Data Quality Problems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(5), pages 575-593, October.

  3. Muslima Zahan & Ron S. Kenett, 2013. "Modeling and Forecasting Energy Consumption in the Manufacturing Industry in South Asia," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 87-98.

    Cited by:

    1. Köppelová, J. & Jindrová, A., 2017. "Comparative Study of Short-Term Time Series Models: Use of Mobile Telecommunication Services in CR Regions," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(01), March.
    2. Irina A. Firsova & Dinara G. Vasbieva & Nikolay N. Kosarenko & Maria A. Khvatova & Lev R. Klebanov, 2019. "Energy Consumption Forecasting for Power Supply Companies," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 1-6.

  4. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.

    Cited by:

    1. Paolo Castelnovo & Martina Dal Molin, 2021. "The learning mechanisms through public procurement for innovation: The case of government‐funded basic research organizations," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(3), pages 411-446, September.
    2. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    3. Pedro Costa & Helena Rodrigues, 2024. "The ever-changing business of e-commerce-net benefits while designing a new platform for small companies," Review of Managerial Science, Springer, vol. 18(9), pages 2507-2545, September.
    4. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    5. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    6. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    7. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    8. Violetta Simonacci & Michele Gallo, 2017. "Statistical tools for student evaluation of academic educational quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 565-579, March.
    9. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    10. Hamed Taherdoost & Mitra Madanchian, 2021. "Empirical Modeling of Customer Satisfaction for E-Services in Cross-Border E-Commerce," Post-Print hal-03741849, HAL.
    11. Jaiswal, Rachana & Gupta, Shashank & Tiwari, Aviral Kumar, 2024. "Big data and machine learning-based decision support system to reshape the vaticination of insurance claims," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    12. Antonino Mario Oliveri & Gabriella Polizzi & Anna Maria Parroco, 2019. "Measuring Tourist Satisfaction Through a Dual Approach: The 4Q Methodology," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 361-382, November.

  5. Ron S. Kenett & Silvia Salini, 2011. "Rejoinder to ‘Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 484-486, September.

    Cited by:

    1. Paolo Castelnovo & Martina Dal Molin, 2021. "The learning mechanisms through public procurement for innovation: The case of government‐funded basic research organizations," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(3), pages 411-446, September.
    2. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    3. Pedro Costa & Helena Rodrigues, 2024. "The ever-changing business of e-commerce-net benefits while designing a new platform for small companies," Review of Managerial Science, Springer, vol. 18(9), pages 2507-2545, September.
    4. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    5. Hamed Taherdoost & Mitra Madanchian, 2021. "Empirical Modeling of Customer Satisfaction for E-Services in Cross-Border E-Commerce," Post-Print hal-03741849, HAL.
    6. Jaiswal, Rachana & Gupta, Shashank & Tiwari, Aviral Kumar, 2024. "Big data and machine learning-based decision support system to reshape the vaticination of insurance claims," Technological Forecasting and Social Change, Elsevier, vol. 209(C).

  6. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    See citations under working paper version above.
  7. Ron Kenett, 2006. "On the planning and design of sample surveys," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(4), pages 405-415.

    Cited by:

    1. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.

  8. R. Kenett & P. Thyregod, 2006. "Aspects of statistical consulting not taught by academia," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(3), pages 396-411, August.

    Cited by:

    1. Adrian Bowman, 2007. "Interdisciplinary research: the importance of learning other people’s language," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 91(4), pages 361-365, December.
    2. Ian G. McHale & Philip A. Scarf & David E. Folker, 2012. "On the Development of a Soccer Player Performance Rating System for the English Premier League," Interfaces, INFORMS, vol. 42(4), pages 339-351, August.
    3. Estrada, Samantha & Floren, Michael & Harding, Justin & Wroblewski, Matthew, 2021. "What is your research question? A mixed methods evaluation of an academic statistical consulting center," Evaluation and Program Planning, Elsevier, vol. 89(C).
    4. Ron S. Kenett, 2009. "‘Post‐financial meltdown: What do the services industries need from us now?’ by Roger W. Hoerl and Ronald D. Snee: Discussion 2," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(5), pages 527-531, September.

  9. Agnihothri, Saligrama R. & Kenett, Ron S., 1995. "The impact of defects on a process with rework," European Journal of Operational Research, Elsevier, vol. 80(2), pages 308-327, January.

    Cited by:

    1. Jaber, Mohamad Y. & Guiffrida, Alfred L., 2004. "Learning curves for processes generating defects requiring reworks," European Journal of Operational Research, Elsevier, vol. 159(3), pages 663-672, December.
    2. Jaber, Mohamad Y. & Guiffrida, Alfred L., 2008. "Learning curves for imperfect production processes with reworks and process restoration interruptions," European Journal of Operational Research, Elsevier, vol. 189(1), pages 93-104, August.
    3. Bazan, Ehab & Jaber, Mohamad Y. & Zanoni, Simone & Zavanella, Lucio E., 2014. "Vendor Managed Inventory (VMI) with Consignment Stock (CS) agreement for a two-level supply chain with an imperfect production process with/without restoration interruptions," International Journal of Production Economics, Elsevier, vol. 157(C), pages 289-301.
    4. Modak, Nikunja Mohan & Panda, Shibaji & Sana, Shib Sankar, 2016. "Three-echelon supply chain coordination considering duopolistic retailers with perfect quality products," International Journal of Production Economics, Elsevier, vol. 182(C), pages 564-578.
    5. Y H Kang & S S Kim & H J Shin, 2010. "A dispatching algorithm for parallel machines with rework processes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 144-155, January.
    6. Ashoke Kumar Bera & Dipak Kumar Jana, 2017. "Multi-item imperfect production inventory model in Bi-fuzzy environments," OPSEARCH, Springer;Operational Research Society of India, vol. 54(2), pages 260-282, June.
    7. Sarker, Bhaba R. & Jamal, A.M.M. & Mondal, Sanjay, 2008. "Optimal batch sizing in a multi-stage production system with rework consideration," European Journal of Operational Research, Elsevier, vol. 184(3), pages 915-929, February.
    8. Jaber, M.Y. & Bonney, M. & Moualek, I., 2009. "An economic order quantity model for an imperfect production process with entropy cost," International Journal of Production Economics, Elsevier, vol. 118(1), pages 26-33, March.
    9. Yuan-Shyi Peter Chiu & Chih-An Katherine Lin & Huei-Hsin Chang & Victoria Chiu, 2010. "Mathematical modelling for determining economic batch size and optimal number of deliveries for EPQ model with quality assurance," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 16(4), pages 373-388, July.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (1) 2023-02-20
  2. NEP-CMP: Computational Economics (1) 2023-02-20
  3. NEP-ECM: Econometrics (1) 2009-06-17
  4. NEP-NET: Network Economics (1) 2009-06-17

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