This paper considers a multi-objective variant of vehicle routing problems, in which the customer demands are supposed to be triangular fuzzy numbers and the objective functions are also disrupted by fuzziness. However, the propagation of fuzzy demands to the objectives can affect the reliability of generated solutions. To this end, we propose a robustness approach to deal with fuzziness in the multi-objective context. The new approach tries to achieve robust routes that minimize two fuzzy-valued objectives, the total traveled distance and total tardiness time. Additionally, we suggest to refine our previously proposed algorithms for integrating robustness. An experimental study based on a Monte-Carlo simulation is finally carried out to evaluate the obtained results.
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