Smartphone Selection with HF-MEREc and HF-MEREc

Abstract

In the rapidly changing world of e-commerce logistics, selecting the best custom-designed smartphone is crucial for operational efficiency. Motivated by this need, in this study, a hybrid multi-criteria decision-making (MCDM) approach is presented for assessing custom-designed smartphones for a crowdsourced e-commerce logistics firm in Turkey, HepsiJET. This approach addresses various possibly contradictory qualitative and quantitative criteria, requiring a hybrid MCDM method like the proposed HF-MEREC-COBRA (hesitant fuzzy - Method based on the Removal Effects of Criteria - Comprehensive Distance Based Ranking) for decision-making. The HF-MEREC-COBRA method uses the hesitant fuzzy Method based on the Removal Effects of Criteria (HF-MEREC) to compute criteria weights, followed by the use of hesitant fuzzy Comprehensive Distance Based Ranking (HF-COBRA) to evaluate and rank smartphone options. Integration of MEREC and COBRA methods with the use of Hesitant Fuzzy Linguistic Term Sets (HFLTS) and the concept of hesitant fuzzy sets (HFS), namely HF-MEREC-COBRA, has never been studied in the literature. For comparative analysis, the HF-MEREC-TOPSIS (HF-MEREC-Technique for Order Preference by Similarity to Ideal Solution) method is also applied to the same problem. A case study is conducted where five smartphone alternatives are evaluated by five expert decision-makers (DMs) using twenty-seven evaluation criteria. Both HF-MEREC-COBRA and HF-MEREC-TOPSIS methods yielded the same ranking, which resulted in Xiaomi Redmi Note 11 Pro 128 GB 8 GB Ram (A2) being the best alternative. A sensitivity analysis is performed, revealing that HF-MEREC-COBRA maintains strong robustness against moderate variations, up to ±40%, in criterion weights. © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).