Abstract
With the frequent occurrence of emergency events, decision‐making (DM) plays an increasingly significant role in coping with them and has become an important and the challenging research focus recently. It is critical for decision makers to make accurate and reasonable emergency judgments in a short period as poor decisions can result in enormous economic losses and an unstable social order. As a consequence, this work offers a new DM approach based on novel distance and similarity measures using q‐rung linear Diophantine fuzzy (q‐RLDF) information to assure that DM problems may be addressed successfully and fast. One of the useful methods for determining the degree of similarity between the objects is the similarity measure. In this paper, we propose some new q‐rung linear Diophantine fuzzy (q‐ROLDF) distances and similarity measures. The Jaccard similarity measure, exponential similarity measure, and cosine and cotangent function‐based similarity measures are proposed for q‐LDFSs. The defined similarity measures are applied to the logistics and supply chain management problem, and the results are discussed. A comparison of new similarity measures is developed, and the proposed work’s advantages are discussed.
Highlights
Models of knowledge-based decision-making attract substantial attention in industry and academia
Based on q-rung linear Diophantine fuzzy sets (q-RLDFSs), we proposed another form of similarity measures by considering the function of membership degree (MD), nonmembership degree (NMD), and reference parameters (RPs) in q-RLDFSs
For the q-RLDFSs, we presented a family of similarity measures such as Jaccard SMs, exponential SMs, and cosine and cotangent SMs between q-RLDFSs by considering MD, NMD, and RPs. e concept of the qth power of reference parameters will provide a more versatile and efficient basis for fuzzy system modeling and decision-making under uncertainty, covering the space of existing structures as well as the space of MD, NMD, and RPs
Summary
Models of knowledge-based decision-making attract substantial attention in industry and academia. DSSs have some huge benefits in decision-making by helping decision makers in their challenges and improve the effectiveness of the decisionmaking process [1]. For the purpose of logistics support, the better selection of logistic providers (LPs) is important with the growth of the supply chain theories. Computer and technical experts have made great efforts to systematic processes of decision-making in the engineering and manufacturing industries [3]. Zha et al [4], for example, developed a compromise decision-supporting issue approach and the fuzzy synthetic decision model (FSDM). Implementation of the DSS of multicriteria decision-making (MCDM) in supply chains is a constant challenge [5,6,7].
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