A QFD-Based Hesitant Fuzzy Linguistic Linear Assignment Method Fusing with Consistency Ratio for the Customer-Oriented Reverse Logistics Provider Selection

With the acceleration of urbanization, a multitude of activities such as construction, reconstruction, expansion, and demolition generate copious amounts of construction waste. This not only leads to substantial waste but also poses potential hazards in terms of pollution and safety. Given the inherent uncertainty and complexity of these activities, the selection of an appropriate reverse logistics provider (RLP) emerges as a crucial determinant for the successful implementation of transportation, repackaging, storage, maintenance, and redistribution operations. However, extant RLP selection studies exhibit a prominent shortcoming, namely, an insufficient emphasis on customer requirements and a failure to adequately address the flexible and imprecise linguistic evaluations employed by decision-makers, as well as the restricted pairwise comparison preferences. Therefore, a customer-oriented RLP selection approach based on the quality function deployment (QFD) and the hesitant fuzzy linguistic linear assignment method is proposed. Initially, hesitant fuzzy linguistic term sets (HFLTSs) are utilized to enable decision-makers to express their assessments. Subsequently, a hesitant fuzzy linguistic best worst method is devised to ascertain the weights of customer requirements, wherein a novel consistency measurement is introduced to bridge the input and output results. Moreover, a new ranking method associated with the proposed possibility-based comparison relation for HFLTSs is developed to identify the optimal RLP. Finally, to validate the effectiveness of the proposed method, an RLP selection problem within a civil construction project is examined. The obtained valid results are further corroborated through sensitivity analysis and comparative analysis, thereby enhancing the credibility and applicability of the proposed method for potential application in practical scenarios.