Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

Building a sustainable transportation system without involving international oil companies (IOCs) is an unrealistic feat. To date, no study has determined the best IOC and low-performing ones with respect to sustainable oil transportation, which is considered a benchmarking challenge requiring an urgent solution. Despite this limitation, the benchmarking of IOCs falls under the complex multi-criteria decision making (MCDM) because of the use of several evaluation criteria and their varying datasets and the varying importance of these criteria. Moreover, the issues involving the use of imprecise, unclear and ambiguous information remain unresolved in the existing multi-attribute decision-making methods. The robustness of the multi-objective optimisation on the basis of ratio analysis (MULTIMOORA, i.e. an updated version of MOORA) plus full-multiplicative form (FMF) method and that of the fuzzy-weighted with zero inconsistency (FWZIC) method have been proven. Therefore, in this study, we propose a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the MCDM methods to help build a sustainable transportation industry. The proposed methodology consists of two phases. The initial phase involves assigning values to the evaluation criteria of IOCs to formulate the evaluation decision matrix. The second phase involves the development of two fuzzy MCDM methods, namely, the LDFRS with the FWZIC method (hereafter called LDFRS–FWZIC) for weighting the criterion of each IOC and the LDFRS with the MULTIMOORA method (hereafter called LDFRS–MULTIMOORA) for benchmarking the IOCs. The IOCs were evaluated based on 2 criteria, 9 sub-criteria and 47 measurement items by 483 experts from 11 IOCs. Results revealed the following: (i) LDFRS–FWZIC can effectively weigh the evaluation criteria of IOCs. The highest final weight of 0.2594 was for ‘cost leadership’ (C2-1), whereas the lowest weights of 0.1148 was for ‘priority of other external matters’ (C1-2) and ‘insufficient supply’(C1-4). (ii) LDFRS–MULTIMOORA can successfully benchmark the IOCs. IOC11 ranked first, followed by IOC10 and IOC3 in the second and third ranks, respectively. IOC4 ranked the lowest (rank = 11). A sensitivity analysis was conducted to determine the robustness of the developed fuzzy MCDM methods.