Summary
Fuzzy set theory is well-suited for modelling imprecision inherent to industrial reality. Whilst a stochastic approach implies the difficult determination of probability informations, fuzzy sets can be understood as imprecise probability distributions. We first recall that the area compensation method for the comparison of two fuzzy numbers is consistent with this framework. We derive then a comparison index in the same interpretative context.
We exemplify the use of the comparison tools for solving multi-objective linear programming problems with imprecision: MOFAC (which stands for Multi-Objective Fuzzy Area Compensation), in a context where the relevant information is assumed as imprecise probabilities. Finally, we perform a comparison with well-known methods, STRANGE and FLIP, in order to exhibit the main features of the proposed approach.
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Fortemps, P., Teghem, J. (2000). Multi-Objective Fuzzy Linear Programming: The MOFAC Method. In: Fodor, J., De Baets, B., Perny, P. (eds) Preferences and Decisions under Incomplete Knowledge. Studies in Fuzziness and Soft Computing, vol 51. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1848-2_2
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DOI: https://doi.org/10.1007/978-3-7908-1848-2_2
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