Human experts usually utilize words of linguistic (L-) variables (var) to evaluate the criteria of a given project. However, linguistic words are not typically mathematical and computational objects. A multi-criteria decision-making (MDCM) method is developed in this work, that utilizes hedge algebras (HA) for processing L-var, which can handle both their qualitative and quantitative semantics, with the crisp TOPSIS method. This integration employs the numerical semantics of linguistic terms to evaluate and rank alternatives according to their distances from the negative and positive ideal solutions. The study also illustrates the effectiveness and feasibility of the proposed method through a specific application example. The results show that this integrated approach not only handles ambiguous and uncertain evaluations but also improves objectivity and reliability in decision-making. This proposed method is particularly advantageous and useful for tender evaluation boards to assess in terms of linguistic words.