Evaluation of Metaverse integration of freight fluidity measurement alternatives using fuzzy Dombi EDAS model

Developments in transportation systems, changes in consumerism trends, and conditions such as COVID-19 have increased both the demand and the load on freight transportation. Since various companies are transporting goods all over the world to evaluate the sustainability, speed, and resiliency of freight transportation systems, data and freight fluidity measurement systems are needed. In this study, an integrated decision-making model is proposed to advantage prioritize the freight fluidity measurement alternatives. The proposed model is composed of two main stages. In the first stage, the Dombi norms based Logarithmic Methodology of Additive Weights (LMAW) is used to find the weights of criteria. In the second phase, an extended Evaluation based on the Distance from Average Solution (EDAS) method with Dombi unction for aggregation is presented to determine the final ranking results of alternatives. Three freight fluidity measurement alternatives are proposed, namely doing nothing, integrating freight activities into Metaverse for measuring fluidity, and forming global governance of freight activities for measuring fluidity through available data. Thirteen criteria, which are grouped under four main aspects namely technology, governance, efficiency, and environmental sustainability, and a case study at which a ground framework is formed for the experts to evaluate the alternatives considering the criteria are used in the multi-criteria decision-making process. The results of the study indicate that integrating freight activities into Metaverse for measuring fluidity is the most advantageous alternative, whereas doing nothing is the least advantageous one

Prioritization of healthcare systems during pandemics using Cronbach's measure based fuzzy WASPAS approach

Pandemics are well-known as epidemics that spread globally and cause many illnesses and mortality. Because of globalization, the accelerated occurrence and circulation of new microbes, the infection has emerged and the incidence and movement of new microbes have sped up. Using technological devices to minimize the visit durations, specifying days for handling chronic diseases, subsidy for the staff are the alternatives that can help prevent healthcare systems from collapsing during pandemics. The study aims to define the efficient usage of optimization tools during pandemics to prevent healthcare systems from collapsing. In this study, a new integrated framework with fuzzy information is developed, which attempts to prioritize these alternatives for policymakers. First, rating data are assigned respective fuzzy values using the standard singleton grades. Later, criteria weights are determined by extending Cronbach´s measure to fuzzy context. The measure not only understands data consistency comprehensively, but also takes into consideration the attitudinal characteristics of experts. By this approach, a rational weight vector is obtained for decision-making. Further, an improved Weighted Aggregated Sum Product Assessment (WASPAS) algorithm is put forward for ranking alternatives, which is flexibly considering criteria along with personalized ordering and holistic ordering alternatives. The usefulness of the developed framework is tested with the help of a real case study. Rank values of alternatives when unbiased weights are used is given by 0.741, 0.582, 0.640 with ordering as R1≻R3≻R2. The sensitivity/comparative analysis reveals the impact of the proposed model as useful in selecting the best alternative for the healthcare systems during pandemics

A comprehensive model for socially responsible rehabilitation of mining sites using Q-rung orthopair fuzzy sets and combinative distance-based assessment

Mining companies play a critical role in developing mineral wealth across the globe. Interacting effectively with local communities is yet another potential source of long-term profitability, because of the opportunities that are not accessible if community engagement is not achieved. The financial advantages of a positive company image can be linked to attracting and retaining employees, as well as sustaining or even enhancing the capacity to do business with local suppliers. The socially responsible rehabilitation of a site after mine closure can facilitate access to new or former jobs for the mine workers. This study focuses on how to identify the best rehabilitation strategy after the closure of a mining site. In particular, a q-rung orthopair fuzzy sets (q-ROFSs) based CODAS (COmbinative Distance-based ASsessment) model is developed to support the evaluation of socially responsible rehabilitation activities in mining sites. To test and validate the model, the proposed methodology is compared to the ARAS (Additive Ratio Assessment) method. The results show that rehabilitation and social transition subsidy is the best alternative among those considered. Implementation of this alternative benefits the mining companies and also brings social benefits to the mine workers and the wider communities within the mining site

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

SWSCAV: Real-time traffic management using connected autonomous vehicles

Traffic management methods aim to increase the infrastructure’s capacity to lower congestion levels. Using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connectivity technologies, connected autonomous vehicles (CAVs) have the potential to operate as actuators for traffic control. In this study, a CAV-based alternative approach for traffic management is proposed (SWSCAV), and its performance is compared to that of lane control signals (LCS) and variable speed limits (VSL), which are also traffic management systems. When a shockwave is detected due to an incident, the CAVs on the road slow until they reach the speed of the observed shockwave (SWS), according to this proposed procedure. Thus, the incoming traffic flow towards the incident is slowed, preventing the queue behind from extending. In a simulation of the urban mobility (SUMO) environment, the suggested method is evaluated for 4800 scenarios on a three-lane highway by varying the market penetration rate of CAVs in traffic flow, the control distances, the incident lane, and the duration. The proposed method reduces the incidence of density values of over 38 veh/km/lane and 28 veh/km/lane in the vicinity of the incident region by 12.68 and 8.15 percent, respectively. Even at low CAV market penetration rates, the suggested method reduces traffic density throughout the network and in the location of the incident site by twice as much as the LCS system application

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

Evaluation of climate change-resilient transportation alternatives using fuzzy Hamacher aggregation operators based group decision-making model

Climate change has become one of the most significant threats that all countries face. The extent of the effects of climate change will increase if the pace is not altered. Transportation systems are very important considering the order and organization of cities. Therefore, transportation networks must be resilient to the effects of climate change. In this study, three different alternatives to climate change resilient transportation networks, which are climate change resistant design of transportation facilities, alternative routes and strategies for the transportation systems, and climate preparedness are defined. In this study, these alternatives are assessed and prioritized under twelve sub-criteria using the decision-making model. By combining the interval-valued Fermatean fuzzy Hamacher aggregation operators with three decision-making methods including the MEREC (Method using the removal effects of criteria), RS (Rank sum), and the MULTIMOORA (Multi-attribute multi-objective optimization based on the ratio analysis), we develop a novel hybrid model for handling decision-making problems. The practicability and effectiveness of the presented model are tested with a case study. The results of the study show that operationally preparing for the effects of climate change is the best choice out of the ones that were given

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

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

202310 bcvcVersion of RecordNot mentionPublishe

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