Refine your search
Collections
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kadadevaramath, Akarsha
- Hybrid MCDM Approaches for Sustainable Supplier Selection from the Economic and Environmental Aspects
Abstract Views :84 |
PDF Views:0
Authors
Affiliations
1 Research Scholar Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, Karnataka, India., IN
2 Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, Karnataka, India., IN
3 Mechanical Engineer, Intel India Pvt. Ltd Bangalore, Karnataka, India., IN
1 Research Scholar Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, Karnataka, India., IN
2 Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, Karnataka, India., IN
3 Mechanical Engineer, Intel India Pvt. Ltd Bangalore, Karnataka, India., IN
Source
Journal of Mines, Metals and Fuels, Vol 71, No 2 (2023), Pagination: 240-246Abstract
Supply chain design is essential while considering environmental and economic issues in order to prevent negative environmental impacts induced by increasing levels of industrialization. A novel sustainable supply chain design strategy is offered in this research to address the trade-offs between environmental and economical concerns. One of the most important operational tasks in the construction of a green SCM is the identification of sustainable suppliers. Although many studies have considered economic criteria like cost, quality and lead time to select suppliers, just a few have taken environmental and social factors into account. This study suggests a number of integrated Multi-Criteria Decision Making (MCDM) methods for choosing and assessing environmentally and economically responsible suppliers. Various approaches of MCDM such as SAW, WASPAS, TOPSIS, and GRA are used in the suggested methodology.Keywords
Supplier Selection, Green, Mcdm, Supply Chain.References
- Büyüközkan, G and Çifçi, G. (2012a): A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP, and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications,39(3),3000– 3011.doi:10.1016/j.eswa.2011.08.1
- Dou, Y., Zhu, Q. and Sarkis, J. (2014): Evaluating green supplier development programmes with a grey analytical network process-based methodology. European Journal of Operational Research, 233(2), 420–431. doi:10.1016/j.ejor.2013.03.004
- Fikri Dweiri, Sameer Kumar and Vipul Jain (2016): Corrigendum to “Designing an integrated AHP based decision support system for supplier selection in the automotive industry” Expert Systems with Applications 62 (2016) 273–283, Expert Systems with Applications, Volume 72, 15 April 2017, Pages 467-468
- Huseyin Selcuk Kilic, Ahmet Selcuk Yalcin, (2020): Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection, Applied Soft Computing, Volume 93, August 2020, 106371
- Jing Li, Hong Fang and Wenyan Song (2019): Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach, Journal of Cleaner Production, Volume 222, 10 June 2019, Pages 606-621
- Mohammed Ahmed and Harris, Irina and Govindan Kannan, (2019): A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation, International Journal of Production Economics,Elsevier, vol. 217(C), pages 171-184
- Mona Najar, Vazifehdan Soroush and Avakh Darestani (2019): Green Logistics Outsourcing Employing Multi-Criteria Decision Making and Quality Function Deployment in the Petrochemical Industry, The Asian Journal of Shipping and Logistics, Volume 35, Issue 4, December 2019, Pages 243-254
- Peng, J. (2012): Research on the optimization of green suppliers based on AHP and GRA. Journal of Information and Computational Science, 9(1), 173– 182
- Samet Güner, Halil Ibrahim Cebeci, (2016): Multi-Criteria Decision Making Techniques for Green Supply Chain Management: A Literature Review, Ethics and Sustainability Global Supply Chain Management, IGI Global book series Advances in Logistics, Operations, and Management Science, (ALOMS) (ISSN: 2327-350X; eISSN: 2327-3518)
- Sanjay Kumar, Saurabh Kumar, Asim Gopal Barman, (2018): Supplier selection using fuzzy TOPSIS multi-criteria model for a small scale steel manufacturing unit, Procedia Computer Science,Volume 133, 2018, Pages 905-912
- Serap Akcan , Meral Güldeº (2019): Integrated Multicriteria Decision-Making Methods to Solve Supplier Selection Problem: A Case Study in a Hospital, Journal of Healthcare Engineering, Volume 2019, Article ID 5614892, https://doi.org/10.1155/2019/ 5614892
- Seyed Amin, Seyed Haeri, Jafar Rezaei (2019): A grey-based green supplier selection model for uncertain environments, Journal of Cleaner Production, Volume 221, 1 June 2019, Pages 768-784
- Tsui, C.W. and Wen, U.P. (2012): Developing the Green Supplier Selection Procedure Based on Analytical Hierarchy Process and Outranking Methods. In International Conference on Industrial Engineering and Operations Management, (pp.3-6).
- Yan-Kai Fu, (2019): An integrated approach to catering supplier selection using AHP-ARAS-MCGP methodology, Journal of Air Transport Management, Volume 75, March 2019, Pages 164-169
- Yazdani, M. (2014): An integrated MCDM approach to green supplier selection. International Journal of Industrial Engineering Computations, 5(3), 443–458, doi:10.5267/j.ijiec.2014.3.003
- Yu, Q., Hou, F. (2016): An approach for green supplier selection in the automobile manufacturing industry, Kybernetes, 45(4), 571–588.doi:10.1108/K-01-2015-0034
- An Approach for Sub Selecting Variables that have Higher Influence on the Outcome in Developing Predictive Model using Staff Turnover
Abstract Views :155 |
PDF Views:0
Authors
Mohan Sangli
1,
Rajeshwar S Kadadevaramath
2,
Jerin Joseph
1,
Akarsha Kadadevaramath
3,
Immanuel Edinbarough
4
Affiliations
1 Research Scholars, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, IN
2 Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, IN
3 Engineer, Intel India Pvt. Ltd., Bangalore, Karnataka, IN
4 Professor and Head, Engg. Tech, Industrial and Manufacturing EnggDept, The University of Texas, US
1 Research Scholars, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, IN
2 Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, IN
3 Engineer, Intel India Pvt. Ltd., Bangalore, Karnataka, IN
4 Professor and Head, Engg. Tech, Industrial and Manufacturing EnggDept, The University of Texas, US
Source
Journal of Mines, Metals and Fuels, Vol 71, No 6 (2023), Pagination: 857-866Abstract
Predictive models are built by learning the combined effects of several independent variables that directly or indirectly influence the outcome. H. Response or dependent variable. In practice, data collection has data on a large number of independent variables that are outcome-sensitive and may or may not be related to the outcome. Some independent variables have a large impact on the results, while others may have little or no impact on the results. The presence of some independent variables that are irrelevant to the outcome can affect the performance of the predictive model. In this context, it is desirable and essential to identify the independent variables that most influence the forecast model to keep it lean and efficient. In this work, we used a dataset containing employee turnover rates and explored how to identify a subset of outcome-sensitive variables, thus eliminating variables that hinder the development of effective predictive models. By partially selectively influencing the independent variables, we developed lean and efficient predictive models that enabled us to act on an actionable subset of the variables to reduce staff turnover, thereby improving corporate save effort and cost.Keywords
Predictive model, Sensitive parameter, Dimensionality.References
- Aerts, Stein, et al. (2006): “Gene prioritization through genomic data fusion.” Nature biotechnology 24.5: 537.
- André Altmann”, †, Laura Tolo ¸si”,†, Oliver Sander‡ and Thomas Lengauer. (2010): “Permutation importance: a corrected feature importance measure” Vol.26 no.10, pages 1340–1347 doi:10.1093/ bioinformatics/btq134.
- Andrea Bommert, Xudong Sun, Bernd Bischl, JörgRahnenführer, Michel Lang (2020): “Benchmark for filter methods forfeature selection in high-dimensional classification data. Computational Statistics & Data Analysis Volume 143, March 2020, 106839.
- Breiman, Leo, et al. (1984): Book “Classification and regression trees. Belmont, CA: Wadsworth.” International Group: 432.
- Chehata, Nesrine, Li Guo, and Clément Mallet. (2009): “Airborne lidar feature selection for urban classification using random forests.” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38. Part 3: W8.
- Chen, Tianqi, and Carlos Guestrin. (2016): “Xgboost: A scalable tree boosting system.” Proceedings of the 22nd acmsigkdd international conference on knowledge discovery and data mining. ACM.
- Definition of Algorithm. https://www.merriamwebster. com/dictionary/algorithm.
- Díaz-Uriarte, Ramón, and Sara Alvarez De Andres. “Gene selection and classification of microarray data using random forest.” BMC bioinformatics 7.1 (2006): 3. (2021).
- Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. (2010): “Regularization paths for generalized linear models via coordinate descent.” Journal of statistical software 33.1: 1.
- Griffith, Obi L. Melck, Adrienne, Steven JM Wiseman, Sam M. Jones, and S. M. Wiseman. (2006): “Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers.” Journal of Clinical Oncology 24.31: 5043-5051.
- Geurts, Pierre, Damien Ernst, and Louis Wehenkel. (2006): “Extremely randomized trees.” Machine learning 63.1: 3-42.
- Guyon, Isabelle, and André Elisseeff. (2003): “An introduction to variable and feature selection.” Journal of machine learning research 3. Mar (2003): 1157-1182.
- Hans, Chris.(2009): “Bayesian lasso regression.” Biometrika 96.4 : 835-845.
- Hoerl, Arthur E., and Robert W. Kennard. (1970): “Ridge regression: Biased estimation for nonorthogonal problems.” Technometrics 12.1: 55-67.
- Kolde, Raivo, et al. (2012): “Robust rank aggregation for gene list integration and meta-analysis.” Bioinformatics 28.4: 573-580.
- Liaw, Andy, and Matthew Wiener. “Classification and regression by randomForest.” R news 2.3 (2002): 18- 22. Predrag Radivojac1, Zoran Obradovic2, A. Keith Dunker1, and Slobodan Vucetic2; J.-F. Boulicaut et al “Feature Selection Filters Based on the Permutation Test”. (Eds.): ECML 2004, LNAI 3201, pp. 334–346, 2004. © Springer-Verlag Berlin Heidelberg 2004
- Menze, Bjoern H., et al. (2009): “A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.” BMC bioinformatics 10.1: 213.
- Molnar, Christoph. 2019: “Interpretable machine learning. A Guide for Making Black Box Models Explainable”, https://christophm.github.io/ interpretable-ml-book/.
- Xing, Eric P., Michael I. Jordan, and Richard M. Karp. (2001): “Feature selection for high-dimensional genomic microarray data.” ICML. Vol.1.