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q-Rung Orthopair Fuzzy TOPSIS Method for Green Supplier Selection Problem
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Supply chain management is to improve competitive stress. In today’s world, competitive terms and customer sense have altered in favor of an environmentalist manner. As a result of this, green supplier selection has become a very important topic. In the green supplier selection approach, agility, lean process, sustainability, environmental sensitivity, and durability are pointed. Like the classical supplier selection problems, environmental criteria generally emphasize green supplier selection. However, these two problem approaches are different from each other in terms of carbon footprint, water consumption, environmental and recycling applications. Due to the problem structure, a resolution is defined that includes an algorithm based on q-Rung Orthopair Fuzzy (q-ROF) TOPSIS method. Brief information about q-ROF sets is given before the methodology of the q-ROF model is introduced. By using the proposed method and q-ROF sets, an application was made with today’s uncertain conditions. In the conclusion part, a comparison is made with classical TOPSIS, Intuitionistic Fuzzy TOPSIS and q-ROF TOPSIS methodology. As a result, more stable and accurate results are obtained with q-ROF TOPSIS.
Title: q-Rung Orthopair Fuzzy TOPSIS Method for Green Supplier Selection Problem
Description:
Supply chain management is to improve competitive stress.
In today’s world, competitive terms and customer sense have altered in favor of an environmentalist manner.
As a result of this, green supplier selection has become a very important topic.
In the green supplier selection approach, agility, lean process, sustainability, environmental sensitivity, and durability are pointed.
Like the classical supplier selection problems, environmental criteria generally emphasize green supplier selection.
However, these two problem approaches are different from each other in terms of carbon footprint, water consumption, environmental and recycling applications.
Due to the problem structure, a resolution is defined that includes an algorithm based on q-Rung Orthopair Fuzzy (q-ROF) TOPSIS method.
Brief information about q-ROF sets is given before the methodology of the q-ROF model is introduced.
By using the proposed method and q-ROF sets, an application was made with today’s uncertain conditions.
In the conclusion part, a comparison is made with classical TOPSIS, Intuitionistic Fuzzy TOPSIS and q-ROF TOPSIS methodology.
As a result, more stable and accurate results are obtained with q-ROF TOPSIS.
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