ORIGINAL ARTICLE An integrated intuitionistic fuzzy set and stochastic multi-criteria acceptability analysis approach for supplier selection Ahmet I ˙ lbas ¸ 1 • Atilla Gu ¨ rdere 1 • Fatih Emre Boran 2 Received: 16 April 2022 / Accepted: 30 September 2022 Ó The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract One of the challenging and complicate processes in supply chain management is to select best supplier from the set of suppliers since they have direct and indirect effects on the core functions of the companies. The supplier selection problem is generally accepted as multi-criteria decision-making (MCDM) since it includes conflict criteria which include uncertain and ambiguous data. Therefore, fuzzy set and its extensions such as intuitionistic fuzzy set have been integrated with MCDM methods in past two decades as they have great capability to handle uncertainty. In this study, hybrid method based on intuitionistic fuzzy preference relation (IFPR) and stochastic multi-criteria acceptability analysis (SMAA-2) is intro- duced to select best supplier. In the first stage, IFPR is exploited to obtain the criteria weights using the preference of decision-makers (DMs). SMAA-2 method has been utilized to rank alternatives which are rated based on the individual preferences of DMs. The proposed method has been implemented to the selection of the most suitable supplier for effective purchasing the vehicle rental service needed by the armed forces considering the eight criteria. Furthermore, sensitivity analysis has been conducted under eight different scenarios obtained by changing the criteria weights. The results illustrate that IFPR-SMAA-2 method is more capable of discriminating in ranking of alternatives and provides more reliable solutions. Keywords Stochastic multi-criteria acceptability analysis  Preference relation  Intuitionistic fuzzy set  Supplier selection 1 Introduction One of the critical activities people and companies face in the real world is decision-making problems. They are defined as the selecting best one among various options for a particular purpose or purposes. The main idea of com- panies is that achieving the procurement process effec- tively. One of the most critical parts of procurement management is the selection of suppliers as it is included many conflicting criteria on the decision-makers’ (DMs’) knowledge which is often uncertain. The selection of suppliers is a multi-criteria decision- making (MCDM) problem. In the literature, research on problem solving with MCDM methods is encountered in many areas and supplier selection has been applied in many different fields. In these studies, many alternatives have been considered in the selection of suppliers to provide the highest benefit [1]. Many of these studies are based on 23 criteria. These criteria were revealed as a result of the survey conducted by Dickson [1] with 273 companies’ purchasing managers to determine the supplier evaluation criteria which were evaluated in order of importance. Weber et al. [2] examined seventy-four works which were published between 1966 and 1990 and categorized the reported methods in three groups: First one was linear weighting, second group was statistical / probability approaches, and the last group was mathematical pro- gramming models for supplier evaluation. It is claimed that the important criteria were delivery time, product quality and price in supplier evaluation. Wilson [3] emphasized that supplier selection criteria have relatively changed over the last decades of the twentieth century. Von- derembse and Tracey [4] implied that the criteria used in & Fatih Emre Boran emreboran@gazi.edu.tr 1 Department of Supply and Logistics, Institute Of Science And Technology, Gazi University, 06500 Ankara, Turkey 2 Department of Energy Systems Engineering, Technology Faculty, Gazi University, 06500 Ankara, Turkey 123 Neural Computing and Applications https://doi.org/10.1007/s00521-022-07919-6