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Interval-valued intuitionistic fuzzy two-sided matching model considering level of automation
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NSTL
Elsevier
Over the past few decades, personnel–position matching (PPM) has garnered increasing attention from scholars. In recent years, with intelligent robots facilitating intelligent production, a new form of problem—personnel–machine position matching (PMPM)—has been derived from PPM. In this study, an interval-valued intuitionistic fuzzy two-sided matching model considering level of automation (LOA) is proposed to solve the PMPM problem in an intelligent production line from the perspective of position homogeneity. The first issue to be considered in this solution is the uncertainty of preference information resulting from the decision-makers’ cognition bias or limitations. By proposing a novel score function based on the centroid method and technique for order preference by similarity to an ideal solution (TOPSIS), this issue is addressed in the information evaluation phase. Another important issue lies in the LOA, which adjusts the degree of human–machine participation. In this study, the classical two-sided matching model was improved by considering the LOA. Furthermore, to maximise the matching satisfaction of multiple sides (personnel, intelligent robots and positions), a multi-objective decision-making model is established. Afterwards, the model is transformed into a single objective model using the combined satisfaction analysis method, which is introduced to produce the final optimisation results in this modelling process. A case is presented to illustrate the practicality of the interval-valued intuitionistic fuzzy two-sided matching model considering LOA, and the results indicate that this model can solve the PMPM problem in an intelligent production line.
Centroid methodInterval-valued intuitionistic fuzzy setsLOATOPSISTwo-sided matching model
Liang Z.-C.、Yang Y.、Liao S.-G.
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College of Mechanical Engineering Chongqing University
School of Software Engineering Chongqing University of Posts and Telecommunications
NSTL
Elsevier
