Rough sets based Ordinal Priority Approach to evaluate sustainable development goals (SDGs) for sustainable mining

The Sustainable Development Goals (SDGs) have been adopted by countries and companies, including mining companies around the world. The aim of this study is to investigate the degree of importance of the seventeen sustainable development goals (SDGs) on sustainable mining using a rough sets based decision making approach. This novel approach consists of three consecutive stages, namely a questionnaire (survey), data analyses, and SDGs classification. Firstly, a survey is conducted to receive a response from internationally experts across different countries. Each participant is asked to evaluate the importance of each SDG. Secondly, the analyses are carried out to make a distinction among groups of participants who respond similarly and discover viewpoints from the industry, academia, and non-governmental organizations. Finally, the degree of importance of each SDG for sustainable mining is found using a novel decision making approach including Ordinal Priority Approach (OPA) based on rough sets. The survey of the results indicated that for all the participants of the survey, independently of their background, the most important SDG for sustainable mining was “SDG8: Decent work and economic growth”, while the one perceived as the least important was “SDG14: Life below water”. The main objective of SDG8 is to promote economic growth through job opportunities and decent work for all. This in turn leads to a more sustainable, long-term economic growth. While all SDGs play an important role, the proposed rough sets based decision making method provided a rational and objective evaluation performance of their perceived priority in the mining sector

A fuzzy Einstein-based decision support system for public transportation management at times of pandemic

Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) methodology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision-makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy

Spacecraft tracking control and synchronization: an assessment of conventional, unconventional, and combined methods

Artificial intelligence (AI) promises breakthroughs in space operations, from mission design planning to satellite data processing and navigation systems. Advances in AI and space transportation have enabled AI technologies in spacecraft tracking control and synchronization. This study assesses and evaluates three alternative spacecraft tracking control and synchronization (TCS) approaches, including non-AI TCS methods, AI TCS methods, and combined TCS methods. The study proposes a hybrid model, including a new model for defining weight coefficients and interval type-2 fuzzy sets based combined compromised solution (IT2FSs-CoCoSo) to solve the spacecraft TCS problem. A new methodology is used to calculate the weight coefficients of criteria, while IT2FSs-CoCoSo is applied to rank the prioritization of TCS methods. A comparative analysis is conducted to demonstrate the performance of the proposed hybrid model. We present a case study to illustrate the applicability and exhibit the efficacy of the proposed method for prioritizing the alternative TCS approaches based on ten different sub-criteria, grouped under three main aspects, including complexity aspects, operational aspects, and efficiency aspects. AI and non-AI methods combined are the most advantageous alternative, whereas non-AI methods are the least advantageous, according to the findings of this study

Prioritizing transport planning strategies for freight companies towards zero carbon emission using ordinal priority approach

Freight transportation counts for remarkable negative effects like emissions, noise, and congestion. This urges for a modal shift toward structuring a more efficient systematic network, facilitating full use of potentials among the transportation modes. Decision-makers face uncertainty and restricted information processing skills when assessing the alternatives for sustainable freight transportation. In this study, a novel extension of the Ordinal Priority Approach under picture fuzzy sets (OPA-P) is proposed to rank the alternatives. In the OPA-P algorithm, experts’ preferences are used to determine the weighting coefficients of criteria and rank the alternatives. A case study is employed to demonstrate the formulation and solution of the problem. The outcome of this study suggests the top-ranked and most important solution for the sustainable transport planning. In addition, to verify the stability of the proposed model, a validation analysis is carried out

A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms

Metaverse comes from the meta-universe, and it is the integration of physical and digital space into a virtual universe. Metaverse technologies will change the transportation system as we know it. Preparations for the transition of the transportation systems into the world of metaverse are underway. This study considers four alternative metaverses: auto-driving algorithm testing for training autonomous driving artificial intelligence, public transportation operation and safety, traffic operation, and sharing economy applications to obtain sustainable transportation. These alternatives are evaluated on thirteen sub-criteria, grouped under four main aspects: efficiency, operation, social and health, and legislation and regulation. A novel Rough Aczel–Alsa (RAA) function and the Ordinal Priority Approach (OPA) method are used in the assessment model. We also present a case study to demonstrate the applicability and exhibit the efficacy of the assessment framework in prioritizing the metaverse implementation alternatives

Evaluation of Cooperative Intelligent Transportation System scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR

202310 bcvcVersion of RecordNot mentionPublishe

Development of a multi-criteria model for sustainable reorganization of a healthcare system in an emergency situation caused by the COVID-19 pandemic

© 2020 by the authors. Healthcare systems worldwide are facing problems in providing health care to patients in a pandemic caused by the SARS-CoV-2 virus (COVID-19). The pandemic causes an extreme disease to spread with fluctuating needs among patients, which significantly affect the capacity and overall performance of healthcare systems. In addition, its impact on the sustainability of the entire economic and social system is enormous and certain sustainable management strategies need to be selected. To meet the challenges of the COVID-19 pandemic and ensure sustainable performance, national healthcare systems must adapt to new circumstances. This paper proposes an original multi-criteria methodology for the sustainable selection of strategic guidelines for the reorganization of a healthcare system under the conditions of the COVID-19 pandemic. The selection of an appropriate strategic guideline is made on the basis of defined criteria and depending on infection capacity and pandemic spread risk. The criteria for the evaluation of strategic guidelines were defined on the basis of a survey in which the medical personnel engaged in the crisis response team during the COVID-19 pandemic in the Republic of Serbia participated. The Level-Based Weight Assessment (LBWA) model and Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH) method were used to determine the weight coefficient criteria, while a novel fuzzy Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval (RAFSI) model was used to evaluate the strategic guidelines. The proposed multi-criteria methodology was tested in a case study in the Republic of Serbia. The validity of the proposed methodology is shown through the simulation of changes in input parameters of Bonferroni aggregation functions and through a comparison with other multi-criteria methodologies