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Classical, Rule-Based and Fuzzy Methods in Multi-Criteria Decision Analysis (MCDA) for Life Cycle Assessment

  • Andrzej MaciołEmail author
  • Bogdan Rębiasz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

In every case of analysis of Life Cycle Assessment (LCA), there is the problem of comparing repeatedly contradictory criteria related to various types of impact factor. Traditional methods of LCA analysis are not capable of implementing such comparisons. This is a problem for multi-criteria evaluation. The analogy between the LCA and MCDM methodologies and the description of LCA as an MCDM problem for resolving the trade-offs between multiple environmental objectives are discussed in this study. The objective of the study is evaluation of opportunities of the use of knowledge-based methods to aggregate LCA results. We compare the results obtained with knowledge-based methods with results from a variety of specialized multi-criteria methods. The research used two classical multi–criteria decision making methods analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS), conventional (crisp) reasoning method and Mamdani’s fuzzy inference method. Classical rule-based approach flattens the results of assessments that practically are not suitable for LCA. The obtained results demonstrate that among the knowledge-based methods, crisp reasoning does not give satisfactory results. Mamdani’s method, AHP method and TOPSIS method allow diversity in the assessment but there are not solutions to assess the quality of these valuations.

Keywords

Environmental indicators Life-Cycle Assessment (LCA) Multi-criteria decision analysis (MCDA) Rule-based MCDA Fuzzy reasoning in MCDA Light-duty vehicles 

References

  1. 1.
    Nemry, F., Leduc, G., Mongelli, I., Uihlein, A.: Environmental Improvement of Passenger Cars (IMPRO-car) (2008). http://www.jrc.es/publications/pub.cfm?id=1564
  2. 2.
    Messagie, M., Macharis, C., Van Mierlo, J.: Key outcomes from life cycle assessment of vehicles, a state of the art literature review. In: Electric Vehicle Symposium and Exhibition (EVS27), 2013 World, pp. 1–9 (2013)Google Scholar
  3. 3.
    Messagie, M., Boureima, F.-S., Coosemans, T., Macharis, C., Van Mierlo, J.: A Range-based vehicle life cycle assessment incorporating variability in the environmental assessment of different vehicle technologies and fuels. Energies 7(3), 1467–1482 (2014)CrossRefGoogle Scholar
  4. 4.
    Bauer, C., Hofer, J., Althaus, H.-J., Del Duce, A., Simons, A.: The environmental performance of current and future passenger vehicles: life cycle assessment based on a novel scenario analysis framework. Appl. Energy 157, 871–883 (2015)CrossRefGoogle Scholar
  5. 5.
    Domingues, R., Marques, P., Garcia, R., Freire, F., Dias, L.C.: Applying multi-criteria decision analysis to the life-cycle assessment of vehicles. J. Clean. Prod. 107, 749–759 (2015)CrossRefGoogle Scholar
  6. 6.
    Miettinen, P., Hämäläinen, R.P.: How to benefit from decision analysis in environmental life cycle assessment (LCA). Eur. J. Oper. Res. 102(2), 279–294 (1997)CrossRefGoogle Scholar
  7. 7.
    Chevalier, J., Rousseaux, P.: Classification in LCA: building of a coherent family of criteria. Int. J. Life Cycle Assess. 4(6), 352–356 (1999)CrossRefGoogle Scholar
  8. 8.
    Benoit, V., Rousseaux, P.: Aid for aggregating the impacts in Life Cycle assessment. Int. J. Life Cycle Assess. 8(2), 74–82 (2003)CrossRefGoogle Scholar
  9. 9.
    Gaudreault, C., Samson, R., Stuart, P.: Implications of choices and interpretation in LCA for multi-criteria process design: de-inked pulp capacity and cogeneration at a paper mill case study. J. Clean. Prod. 17(17), 1535–1546 (2009)CrossRefGoogle Scholar
  10. 10.
    Narayanan, D., Zhang, Y., Mannan, M.S.: Engineering for Sustainable Development (ESD) in bio-diesel production. Process Saf. Environ. Prot. 85(5), 349–359 (2007)CrossRefGoogle Scholar
  11. 11.
    Perimenis, A., Walimwipi, H., Zinoviev, S., Müller-Langer, F., Miertus, S.: Development of a decision support tool for the assessment of biofuels. Energy Policy 39(3), 1782–1793 (2011)CrossRefGoogle Scholar
  12. 12.
    Bouwman, M.E., Moll, H.C.: Environmental analyses of land transportation systems in The Netherlands. Transp. Res. Part D Transp. Environ. 7(5), 331–345 (2002)CrossRefGoogle Scholar
  13. 13.
    Tan, R.R., Culaba, A.B., Purvis, M.R.I.: POLCAGE 1.0-a possibilistic life-cycle assessment model for evaluating alternative transportation fuels. Environ. Model Softw. 19(10), 907–918 (2004)CrossRefGoogle Scholar
  14. 14.
    Zhou, Z., Jiang, H., Qin, L.: Life cycle sustainability assessment of fuels. Fuel 86(1–2), 256–263 (2007)CrossRefGoogle Scholar
  15. 15.
    Safaei Mohamadabadi, H., Tichkowsky, G., Kumar, A.: Development of a multi-criteria assessment model for ranking of renewable and non-renewable transportation fuel vehicles. Energy 34(1), 112–125 (2009)CrossRefGoogle Scholar
  16. 16.
    Rogers, K., Seager, T.P.: Environmental decision-making using life cycle impact assessment and stochastic multiattribute decision analysis: a case study on alternative transportation fuels. Environ. Sci. Technol. 43(6), 1718–1723 (2009)CrossRefGoogle Scholar
  17. 17.
    Elghali, L., Cowell, S.J., Begg, K.G., Clift, R.: Support for sustainable development policy decisions - a case study from highway maintenance. Int. J. Life Cycle Assess. 11(1), 29–39 (2006)Google Scholar
  18. 18.
    Prado-Lopez, V., Seager, T.P., Chester, M., Laurin, L., Bernardo, M., Tylock, S.: Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA). Int. J. Life Cycle Assess. 19(2), 405–416 (2014)CrossRefGoogle Scholar
  19. 19.
    Rębiasz, Macioł, A.: Comparison of classical multi-criteria decision making methods with fuzzy rule-based methods on the example of investment projects evaluation BT. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies: Proceedings of the 7th KES International Conference on Intelligent Decis, pp. 549–561. Springer, Cham (2015)Google Scholar
  20. 20.
    Guinée, J., Heijungs, R., Huppes, G., Kleijn, R., de Koning, A., van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H.A., de Bruijn, H., van Duin, R., Huijbregts, M.A.J., Gorree, M.: Handbook on Life Cycle Assessment. Operational Guide to the ISO Standards. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  21. 21.
    Dahlbo, H., Koskela, S., Pihkola, H., Nors, M., Federley, M., Seppälä, J.: Comparison of different normalised LCIA results and their feasibility in communication. Int. J. Life Cycle Assess. 18(4), 850–860 (2013)CrossRefGoogle Scholar
  22. 22.
    Dias, L.C., Domingues, A.R.: On multi-criteria sustainability assessment: spider-gram surface and dependence biases. Appl. Energy 113, 159–163 (2014)CrossRefGoogle Scholar
  23. 23.
    Myllyviita, T., Leskinen, P., Seppälä, J.: Impact of normalisation, elicitation technique and background information on panel weighting results in life cycle assessment. Int. J. Life Cycle Assess. 19(2), 377–386 (2014)CrossRefGoogle Scholar
  24. 24.
    Huppes, G., van Oers, L.: Background review of existing weighting approaches in life Cycle Impact Assessment (LCIA) (2011). http://publications.jrc.ec.europa.eu/repository/handle/JRC67215
  25. 25.
    Stranddorf, H.K., Hoffmann, L., Schmidt, A.: Impact categories, normalisation and weighting in LCA (2005)Google Scholar
  26. 26.
    EEA: Annual European Union greenhouse gas inventory 1990–2014 and inventory report (2016). http://www.eea.europa.eu/publications/annual-european-union-greenhouse-gas. Accessed 01 Oct 2016
  27. 27.
    EEA: European Union emission inventory report 1990–2014 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP) (2016). http://www.eea.europa.eu/publications/lrtap-emission-inventory-report-2016. Accessed 20 Oct 2016
  28. 28.
    Saaty, T.L.: The Analytic Hierarchy Process: Planning, Priority Setting. McGraw-Hill International Book, Resource Allocation, New York; London (1980)zbMATHGoogle Scholar
  29. 29.
    Olson, L.: Decision Aids for Selection Problems. Springer, New York (1996)CrossRefGoogle Scholar
  30. 30.
    Olson, L.: Comparison of weights in TOPSIS models. Math. Comput. Model. 40(7–8), 721–727 (2004)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Shih, H.-S., Shyur, H.-J., Lee, E.S.: An extension of TOPSIS for group decision making. Math. Comput. Model. 45(7), 801–813 (2007)CrossRefGoogle Scholar
  32. 32.
    Adamcsek, E.: The Analytic Hierarchy Process and its Generalizations. Eötvöos Loránd University (2008)Google Scholar
  33. 33.
    Coyle, G.: The Analytic Hierarchy Process (AHP). Practical Strategy (2004)Google Scholar
  34. 34.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)zbMATHGoogle Scholar
  35. 35.
    Parsons, S.: Current approaches to handling imperfect information in data and knowledge bases. Knowl. Data Eng. IEEE Trans. 8, 353–372 (1996)CrossRefGoogle Scholar
  36. 36.
    Pelzer, E., Fortino, G., Bockstaller, C., Angevin, F., Lamine, C., Moonen, C., Vasileiadis, V., Guérin, D., Guichard, L., Reau, R., Messéan, A.: Assessing innovative cropping systems with DEXiPM, a qualitative multi-criteria assessment tool derived from DEXi. Ecol. Indic. 18, 171–182 (2012)CrossRefGoogle Scholar
  37. 37.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975)CrossRefGoogle Scholar
  38. 38.
    Maciol, A., Rebiasz, B.: Advanced Methods in Investment Projects Evaluation. AGH University of Science and Technology Press, Krakow (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of ManagementAGH University of Science and TechnologyKrakowPoland

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