Sensory Evaluation Model with Unbalanced Linguistic Information

Abstract

The evaluation processes are used for quality inspection, marketing and other fields in industrial companies. This contribution focuses in sensory evaluation where the evaluated items are assessed according to the knowledge acquired via human senses by a panel of experts. In these evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach [1] has provided successful results modeling such a type of information. Usually evaluation processes based on linguistic approaches use symmetrical and uniformly distributed linguistic term sets in order to express their preferences about the evaluated objects. However, there exist problems whose assessments need that one side of the scale overweight the other one, it means an unbalanced linguistic scale. In this contribution we present a sensory evaluation model that manages frameworks with unbalanced linguistic information.

Probabilistic Hybrid Linguistic Approaches for Multiple Attribute Group Decision Making with Decision Hesitancy and the Prioritization of Attribute Relationships

“…Step I-4. Obtain the overall group aggregation results of each alternative r j ( j = 1, 2, 3, 4) by Equation (28). For brevity, the details of r j ( j = 1, 2, 3, 4) are omitted here.…”

“…However, when it comes to more complicated MAGDM problems, where problem structures are ill-defined for fuzzy quantification, Zadeh [25] has advocated the usage of linguistic variables for decision makers to qualitatively express their uncertain preferences. To this end, because of the merit in eliciting decision maker assessments more directly and precisely, linguistic variables have been continuously extended to accommodate various scenarios, such as unbalanced linguistic variables [26][27][28][29], uncertain linguistic variables [30][31][32][33], intuitionistic uncertain variables [34,35], hesitant fuzzy uncertain linguistic variables [15], and hesitant fuzzy unbalanced linguistic variables [36], among others. Although the above extensions of linguistic variables are very effective in situations where decision makers can approximate the most precise linguistic label in accordance with their assessment, Rodríguez et al [37] revealed that decision makers are often cognitively irresolute among several possible linguistic labels.…”

“…As can be seen, these studies all only allow decision makers to depict their cognitive models through balanced linguistic terms sets (BLTS) [25] which are uniformly and symmetrically distributed. However, field investigations [26,28] have revealed that decision makers usually tend to utilize nonuniform or asymmetrical linguistic term sets, referred to as unbalanced linguistic term sets (UBLTSs) [27], for expressing their assessments appropriately and flexibly. Additionally, when trying to obtain linguistic expressions that closely follow decision maker cognitive models, Rodríguez et al [47] and Wang [48] have advocated for the adoption of consecutive and nonconsecutive comparative linguistic expressions.…”

Tackling Complexity in Green Contractor Selection for Mega Infrastructure Projects: A Hesitant Fuzzy Linguistic MADM Approach with considering Group Attitudinal Character and Attributes’ Interdependency

“…Although researches in both contractor selection literature and MADM literature have presented rich approaches for application to green contractor selection problems, there is still a gap with three facets to be further investigated concurrently. First, most of existing hesitant fuzzy linguistic expression tools were based on rigidly uniform or symmetrical linguistic label sets [55,56]; however, practical studies [57,58] have revealed that decision-makers are inclined to express their complicate assessments more precisely and objectively by use of non-uniform or asymmetric linguistic term set, that is, the unbalanced linguistic term set (ULTS) [59]. Second, under complicate decision-making environments, interdependency among attributes is a serious issue that needs to be addressed [60].…”

Multi-attribute group decision making methods with proportional 2-tuple linguistic assessments and weights

“…For example, Cabrerizo et al 6,7 presented a consensus-based group decision making with unbalanced linguistic terms. Martínez et al 32 applied the model with unbalanced linguistic terms to sensory evaluation. Herrera-Viedma and López-Herrera 28 developed a model of information retrieval system with unbalanced fuzzy linguistic information.…”