CoChat
Sign Up
Cited by 85

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry

Wang, Zhiguo, Hao, Hao, Gao, Feng, Zhang, Qian, Zhang, Ji, Zhou, Yanjun·2019·Journal of Cleaner Production
DOI 10.1016/j.jclepro.2018.12.329
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.jclepro.2018.12.329

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 55

  1. Azevedo, 2011, The influence of green practices on supply chain performance: a case study approach, Transport. Res. E Logist. Transport. Rev., 47, 850, 10.1016/j.tre.2011.05.017 · DOI
  2. Banker, 1984, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manag. Sci., 30, 1078, 10.1287/mnsc.30.9.1078 · DOI
  3. Barker, 2011, A multicriteria decision making model for reverse logistics using analytical hierarchy process, Omega, 39, 558, 10.1016/j.omega.2010.12.002 · DOI
  4. Bernroider, 2007, A method using weight restrictions in data envelopment analysis for ranking and validity issues in decision making, Comput. Oper. Res., 34, 2637, 10.1016/j.cor.2005.10.005 · DOI
  5. Bhattacharjee, 2017, Selection of optimal aluminum alloy using TOPSIS method under fuzzy environment, J. Intell. Fuzzy Syst., 32, 871, 10.3233/JIFS-161049 · DOI
  6. Bian, 2013, DEA ranking method based upon virtual envelopment frontier and TOPSIS, Systems Engineering-Theory & Practice, 33, 482
  7. Brown, 1961, The fundamental theorem of exponential smoothing, Oper. Res., 9, 673, 10.1287/opre.9.5.673 · DOI
  8. Cegan, 2017, Trends and applications of multi-criteria decision analysis in environmental sciences: literature review, Environment Systems and Decisions, 37, 123, 10.1007/s10669-017-9642-9 · DOI
  9. Chen, 2009, A DEA-TOPSIS method for multiple criteria decision analysis in emergency management, J. Syst. Sci. Syst. Eng., 18, 489, 10.1007/s11518-009-5120-3 · DOI
  10. Chen, 2018, Assessment of a regional flood disaster indicator via an entropy weighting method, Nat. Hazards Rev., 19, 10.1061/(ASCE)NH.1527-6996.0000280 · DOI
  11. Chitnis, 2016, Efficiency ranking method using DEA and TOPSIS (ERM-DT): case of an Indian bank, Benchmarking Int. J., 23, 165, 10.1108/BIJ-09-2013-0093 · DOI
  12. Coban, 2018, Municipal solid waste management via multi-criteria decision making methods: a case study in Istanbul, Turkey, J. Clean. Prod., 180, 159, 10.1016/j.jclepro.2018.01.130 · DOI
  13. Govindan, 2017, A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus, J. Clean. Prod., 142, 371, 10.1016/j.jclepro.2016.03.126 · DOI
  14. Hao, 2017, Fifth profit source: commercial value and mode of reverse logistics in China, Logistics Technology, 36, 47
  15. Hwang, C.-L., Yoon, K., Multiple Attribute Decision Making Methods and Applications a State-of-the-art Survey. Springer, Berlin, Heidelberg, pp. 1-531.
  16. Hwang, 2014, Sustainability, new economics and policy: greening pathway for the auto industry, Int. J. Technol. Manag. Sustain. Dev., 13, 3, 10.1386/tmsd.13.1.3_1 · DOI
  17. Jahan, 2012, A target-based normalization technique for materials selection, Mater. Des., 35, 647, 10.1016/j.matdes.2011.09.005 · DOI
  18. Jahan, 2010, Material screening and choosing methods – a review, Mater. Des., 31, 696, 10.1016/j.matdes.2009.08.013 · DOI
  19. Komoto, 2011, Analyzing supply chain robustness for OEMs from a life cycle perspective using life cycle simulation, Int. J. Prod. Econ., 134, 447, 10.1016/j.ijpe.2009.11.017 · DOI
  20. Kou, 2014, Evaluation of clustering algorithms for financial risk analysis using MCDM methods, Inf. Sci., 275, 1, 10.1016/j.ins.2014.02.137 · DOI
  21. Kurth, 2017, Trends and applications of multi-criteria decision analysis: use in government agencies, Environment Systems and Decisions, 37, 134, 10.1007/s10669-017-9644-7 · DOI
  22. Li, 1999, A multiple criteria approach to data envelopment analysis, Eur. J. Oper. Res., 115, 507, 10.1016/S0377-2217(98)00130-1 · DOI
  23. Mani, 2013, Simulation and analysis for sustainable product development, Int. J. Life Cycle Assess., 18, 1129, 10.1007/s11367-012-0538-0 · DOI
  24. MEI-juan, 2015, vol. 23, 156
  25. Mela, 2012, Comparative study of multiple criteria decision making methods for building design, Adv. Eng. Inf., 26, 716, 10.1016/j.aei.2012.03.001 · DOI
  26. Mousavi-Nasab, 2017, A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Mater. Des., 121, 237, 10.1016/j.matdes.2017.02.041 · DOI
  27. Mousavi-Nasab, 2018, A new multi-criteria decision making approach for sustainable material selection problem: a critical study on rank reversal problem, J. Clean. Prod., 182, 466, 10.1016/j.jclepro.2018.02.062 · DOI
  28. Mulliner, 2016, Comparative analysis of MCDM methods for the assessment of sustainable housing affordability, Omega-International Journal of Management Science, 59, 146, 10.1016/j.omega.2015.05.013 · DOI
  29. Mulliner, 2016, Comparative analysis of MCDM methods for the assessment of sustainable housing affordability, Omega, 59, 146, 10.1016/j.omega.2015.05.013 · DOI
  30. Oltean-Dumbrava, 2016, Towards a more sustainable surface transport infrastructure: a case study of applying multi criteria analysis techniques to assess the sustainability of transport noise reducing devices, J. Clean. Prod., 112, 2922, 10.1016/j.jclepro.2015.09.096 · DOI
  31. Olugu, 2012, An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry, Expert Syst. Appl., 39, 375, 10.1016/j.eswa.2011.07.026 · DOI
  32. Olugu, 2011, Development of key performance measures for the automobile green supply chain, Resour. Conserv. Recycl., 55, 567, 10.1016/j.resconrec.2010.06.003 · DOI
  33. Opricovic, 2004, Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, Eur. J. Oper. Res., 156, 445, 10.1016/S0377-2217(03)00020-1 · DOI
  34. Özcan, 2011, Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem, Expert Syst. Appl., 38, 9773, 10.1016/j.eswa.2011.02.022 · DOI
  35. Peng, 2013, Material selection using PROMETHEE combined with analytic network process under hybrid environment, Mater. Des., 47, 643, 10.1016/j.matdes.2012.12.058 · DOI
  36. Rakhshan, 2017, Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method, J. Oper. Res. Soc., 68, 906, 10.1057/s41274-017-0237-0 · DOI
  37. Rezaee, 2018, Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange, Physica A, 489, 78, 10.1016/j.physa.2017.07.017 · DOI
  38. Schultmann, 2006, Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry, Eur. J. Oper. Res., 171, 1033, 10.1016/j.ejor.2005.01.016 · DOI
  39. Shabanpour, 2017, Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks, J. Clean. Prod., 142, 1098, 10.1016/j.jclepro.2016.08.147 · DOI
  40. Shafii, 2016, Performance analysis of hospital managers using fuzzy AHP and fuzzy TOPSIS: Iranian experience, Global J. Health Sci., 8, 137
  41. Stewart, 1996, Relationships between data envelopment analysis and multicriteria decision analysis, J. Oper. Res. Soc., 47, 654, 10.1057/jors.1996.77 · DOI
  42. Sun, 2014, Intelligent multicriteria decision support system for systems design, J. Aircraft, 51, 216, 10.2514/1.C032296 · DOI
  43. Tao, 2017, Multi criteria decision making based on efficiency measurement and empirical study: DEA-TOPSIS integrated method, Chinese Journal of Management Science, 25, 153
  44. Tian, 2012, Energy evaluation method and its optimization models for process planning with stochastic characteristics: a case study in disassembly decision-making, Comput. Ind. Eng., 63, 553, 10.1016/j.cie.2011.08.011 · DOI
  45. Tian, 2014, Sustainable design for automotive products: Dismantling and recycling of end-of-life vehicles, Waste Manag., 34, 458, 10.1016/j.wasman.2013.11.005 · DOI
  46. Wadhwa, 2009, Flexible decision modeling of reverse logistics system: a value adding MCDM approach for alternative selection, Robot. Comput. Integrated Manuf., 25, 460, 10.1016/j.rcim.2008.01.006 · DOI
  47. Wang, 2018, Evaluation of Urban circular economy development: an empirical research of 40 cities in China, J. Clean. Prod., 180, 876, 10.1016/j.jclepro.2018.01.089 · DOI
  48. Wang, 2016, A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design, Inf. Sci., 345, 27, 10.1016/j.ins.2016.01.076 · DOI
  49. Wang, 2009, On rank reversal in decision analysis, Math. Comput. Model., 49, 1221, 10.1016/j.mcm.2008.06.019 · DOI
  50. Wei-dong, 2015, Canthe introduction of TOPSIS in risk early-warning model improve the accuracy of early-warning?-A case study from listed companies of manufacturing industry, Chinese Journal of Management Science, 23, 96
  51. Wu, 2011, Ranking approach of cross-efficiency based on improved TOPSIS technique, J. Syst. Eng. Electron., 22, 604, 10.3969/j.issn.1004-4132.2011.04.008 · DOI
  52. Zavadskas, 2014, State of art surveys of overviews on mcdm/madm methods, Technol. Econ. Dev. Econ., 20, 165, 10.3846/20294913.2014.892037 · DOI · Paper page
  53. Zeydan, 2009, A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach, Int. J. Prod. Res., 47, 4327, 10.1080/00207540802662870 · DOI
  54. Zeydan, 2011, A combined methodology for supplier selection and performance evaluation, Expert Syst. Appl., 38, 2741, 10.1016/j.eswa.2010.08.064 · DOI
  55. Zhu, 2017, China's regional natural resource allocation and utilization: a DEA-based approach in a big data environment, J. Clean. Prod., 142, 809, 10.1016/j.jclepro.2016.02.100 · DOI

Cited by

Sorted by citations

See all 85

Metadata

Authors
Wang, Z., Hao, H., Gao, F., Zhang, Q., Zhang, J., & Zhou, Y.
Venue
Journal of Cleaner Production
Publisher
Elsevier BV
Published
2019-03
Volume
214
Pages
730-737
ISSN
0959-6526
DOI
10.1016/j.jclepro.2018.12.329
Cited by
85
References
2
Language
en
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • Research Center of Resource Recycling Science and Engineering
  • Shanghai Polytechnic University
  • Gaoyuan Discipline of Shanghai – Environmental Science and Engineering

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review