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How To Handle Alternatives Based On The Type
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Syed Amirali Hosseini 1 , Sarfraz Hashemkhani Zolfani 2 , *, Paulius Skakauskas 3 , Alireza Fallapour 4 and Sara Saberi 5
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Department of Mobile Engineering and Railway Transport, Faculty of Transport Engineering, Vilnius Gediminas Technical University, 10105 Vilnius, Lithuania
Received: December 2, 2021 / Revised: December 17, 2021 / Accepted: December 20, 2021 / Published: December 23, 2021
Choosing the most sustainable supplier is an important issue for resource and supply chain managers. However, due to the high degree of uncertainty inherent in real projects, creating a decision tree in a clear or ambiguous environment may not give managers accurate or reliable results. For this reason, it is better to evaluate potential suppliers in an Interval Type-2 Fuzzy (IT2F) environment to better deal with this ambiguity. This study uses the similarity to ideal solution (TOPSIS) model using the example of Atieh Sazan. For this, an improved combined IT2F Best Worst Method (BWM) and IT2F methods for order preference, i.e. IT2FBWM was used to obtain the weights. Decisive. IT2FTOPSIS was used to rank potential suppliers based on the Hamming distance measure. In both stages, expert opinion in the form of IT2F linguistic terms was used to weight the criteria and obtain the relative importance of alternatives in terms of evaluation criteria. After receiving the final results, the proposed model was tested using Analytical Hierarchical Process (AHP) and Simple Additive Weighting (SAW) approaches for separate criteria weighting instead of BWM. After running both new models, it was found that the final rating was similar to the final rating of the proposed model, indicating the reliability and accuracy of the results obtained. In addition, it is concluded that the elastic criteria of “restructuring” and “redundancy”, and not the Iranian system of the construction industry, are the most decisive measures to select the best suppliers.
Flexible choice of suppliers; Fuzzy set of interval type-2; Multi-criteria decision making (MCDM); BMW; choosing a reliable TOPSIS supplier; Fuzzy set of interval type-2; Multi-criteria decision making (MCDM); BMW; Topsis
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Supply chain management (SCM) is one of the most interesting topics among allied professionals due to its role in improving the efficiency and revenue of various organizations [1, 2]. The problem of choosing suppliers is one of the most important problems in SCM [3, 4], since the barrier to finding the best supplier of raw materials had a huge negative impact on the revenue of nine different companies from China, Pakistan, Sri Lanka. and India during the Covid-19 pandemic . Choosing the most appropriate helps companies provide products at a reasonable price with a predetermined price . The globalization of operations in various fields has turned the process of sourcing and selecting suppliers into a complex process that can be influenced by political, legal and cultural aspects . As a rule, since supplier selection is efficient throughout the supply chain, the sustainability of this process should always be considered in order to reduce the vulnerability of the supply chain .
In general terms, stability is understood as the ability of a system to return to its original state or to a better state after the occurrence of disturbances . As a result, different types of disruptions can occur in sustainable supply chains due to economic, social and cultural aspects, to name a few. These failures can negatively impact critical factors including revenue, efficiency, quality, and competitiveness . In other words, resilience is a form of managing the detection of potential violations, contributing to the ability to control these identified violations and return to the original state . Meanwhile, developing a resilient supply chain can keep industries safe from disruption .
In addition, a robust sourcing structure has been successfully developed using a fuzzy ordinal priority approach to assist purchasing managers at a kitchenware company in Jiangsu, China during the Covid-19 pandemic . In this regard, it is necessary to formulate an appropriate set of criteria, since the selection of the best criteria is an integral part of the evaluation process [14, 15]. It should be noted that the criteria should be localized according to case studies [16, 17].
When choosing a supplier, many aspects of uncertainty are associated with formal criteria, goals, system behavior and, most importantly, with the choice of decision makers . Since the preferences of decision makers are described in subjective linguistic terms, clear numerical values do not give accurate results. Therefore, attention was paid to the theory of fuzzy sets to solve the problem of linguistic uncertainty. However, type 1 fuzzy sets are not suitable for noise models . Since words mean different things to different people, they are indefinite. There are two types of linguistic ambiguity: intra ambiguity, which refers to the uncertainty about a person’s sound, and inter ambiguity, which is an uncertainty associated with a group of people . As a rule, type 1 fuzzy sets have some drawbacks, including misinterpretation of the use of terms, and sometimes the presence of noisy data from expert groups . Thus, Zadeh  proposed fuzzy sets of type 2 for solving these problems. Type 2 fuzzy sets are defined by two membership functions: a primary membership function (PMF) and a secondary membership function (SMF). Interval Type 2 Fuzzy Sets (IT2FS) are the most commonly used Type 2 Fuzzy Sets because their computational complexity is lower than conventional Type 2 Fuzzy Sets (GT2FS). Therefore, they are more applicable to real control problems . In type 2 fuzzy sets, a word can be modeled using interval type 2 fuzzy sets that efficiently handle linguistic ambiguity. For comparison, a type 1 fuzzy set has a two-dimensional membership function, and a type 2 fuzzy set membership function is three-dimensional. The third dimension represents additional degrees of freedom for probabilities in models with uncertainty . Of course, it should be noted that a new fuzzy set has recently been introduced, called the (3, 2)-fuzzy set, which may be suitable for solving more complex problems .
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As a rule, the selection of suppliers consists of two main stages: (1) determining the level of importance of the evaluation criteria and (2) evaluating the performance of suppliers and ranking alternatives. The first step uses several methods such as AHP, ANP and SAW. In the second stage, other methods were used, including All Criteria Optimization and Commercial Research (VIKOR), TOPSIS and Fuzzy Inference System (FIS).
The MCDM methods used in the first stage of supplier selection have complex calculations, which reduces their practical application, especially when it is necessary to add/remove several supplier options . To solve this problem, a useful MCDM approach is the best-worst method (BWM), which was first developed by Rezai . The advantage of this method is a small number of calculations associated with a low level of inconsistency and obtaining the relative weight of the evaluation criteria through pairwise comparisons . This method provides higher accuracy than traditional methods because it only requires the priority of the best criteria over other criteria and the priority of other criteria over the worst [28, 29]. For the second stage of supplier selection, the TOPSIS method is a common method used by researchers to rank alternatives [19, 30, 31, 32]. The TOPSIS method is more flexible than other MCDM ranking methods and can reduce its inherent uncertainty and additional calculations in a complex case study, leading to only conclusive results . Another advantage of this method is its compatibility with the IT2F environment, which is not found in other MCDM ranking methods such as VICOR or even other decision methods. Therefore, a reliable IT2F decision system can be developed based on the TOPSIS methodology.
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