The simulation tools geared towards promoting sustainability in Mobility as a Service (MaaS) evaluation is inherently a multi-criteria decision-making (MCDM) challenge due to three primary concerns: the criteria significance, data variability, and the expert opinions' uncertainty. Despite efforts in recent years, no current developed MaaS has fully addressed all evaluation criteria. Moreover, no research has evaluated the sustainability of MaaS in the context of determining its optimality. As such, this research's pivotal contribution is to present an evaluation of simulation tools for sustainable MaaS in autonomous vehicles operating in smart cities. This evaluation leans on the advanced extension of a newly proposed an interval-valued Fermatean fuzzy rough set (IVFFRS) incorporated within integrated MCDM methodologies. The IVFFRS is designed to capture intricate and uncertain evaluative data. The initial phase of the evaluation methodology involves formulating the evaluation criteria using an interval-valued Fermatean fuzzy rough set, fuzzily weighted for zero inconsistency. The subsequent phase adopts the interval-valued Fermatean fuzzy rough decision via the opinion score method to prioritize alternatives in light of data variations. This study evaluates ten distinct simulation tools for MaaS in autonomous vehicles based on seven criteria. The methodology's robustness is further ascertained through sensitivity and comparative analyses.

As the climate change enforces decision-makers (DMs) to change established policies and strategies, investors are motivated to finance the development of green projects. Because of their environmental effects, land-use transport projects are of special importance in this process. Different types of environment-friendly land-use transportation projects are financed by the private sector or regulatory authorities and institutions. However, the choice of the correct project is a complex issue. In the decision-making process, social concerns are crucial as well as financial, technical and environmental ones. In order to solve the multi-criteria decision-making problem, we propose a novel group decision support model using the Logarithm Methodology of Additive Weights (LMAW) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) based on the fuzzy Z extension (ZE)-numbers. The proposed group decision support model has a unique capability for decision. The opinions of two groups of the DMs and experts are used to consider the decision reliability in two different stages to reach an optimal decision. To illustrate the use of model, we create a scenario that considers four small-scale green finance planning alternatives that are evaluated using twelve criteria that reflect the choice problem's economic, environmental, technical, and political aspects. According to the findings, the optimum plan should be both inclusive and equitable, as well as economically efficient. Selection of the best green finance planning involves consideration of socioeconomic variables.

Urban transportation plays a crucial role in cities that host sports events. Mexico City, as one of the World Cup 2026 hosts, is one of the world's largest and most populous cities, with a population of over 22 million people. This city faces several challenges related to urban transportation. The purpose of this study is to develop a novel group decision-making framework for evaluating six sustainable alternatives for the management of urban transportation crises. Our proposed group decision framework develops the Base Criterion Method (BCM) and Combined Compromise Solution (CoCoSo) under the fuzzy ZE-numbers for the first time in the literature to obtain reliable decisions. Based on the opinions of decision-makers and expert votes on those opinions, the proposed approach offers a unique feature in decision sciences in that the reliability of decisions can be increased in two stages. Also, sensitive analyses were executed for each of the urban transportation alternatives based on the different states of the criteria groups. According to the findings, the optimum plan should be an investment in the Metro and electric Minibusses development to manage the urban transportation crisis in Mexico City. Also, findings show that applying the fuzzy ZE-numbers leads to more accurate and reliable results.

It is unquestionable that health, both individually and collectively, is the most important issue in life, which is why human beings pay special attention to health maintenance and recovery. The correct performance of the services attracts the patient's satisfaction, which is not only influenced by the latest technology and is influenced by many variables. Therefore, the present study provides a dynamic expert system of patient satisfaction with an integrated approach of system dynamics, Interpretive Structural Modeling (ISM), and Analytic network process (ANP). First, the general structure of the problem was determined based on ISM modeling, and the formulation was done in Vensim software. Then, the validation tests, including the behavior reproduction test and the sensitivity analysis, were carried out, and the accuracy of the system was proved. In this regard, the ISM method was used to identify the levels of causal relationships between the studied variables, and the most influential variables were identified. Then, the most important criteria were identified with the ANP method and used in identifying policies. Finally, the simulation was carried out, and different policies were reviewed. The results showed that the criteria were placed on three levels, and the type of connection and their importance were clearly defined. The ISM and ANP methods have an interesting synergy with the system dynamics approach and help identify leverage points. According to the results, managers can adopt strategies that affect patient satisfaction with the greatest impact and the least action and take corrective measures.

Nowadays, population growth and urban development lead to having an efficient waste management system (WMS) based on recent advances and trends. Alongside all functions and procedures in these systems, the waste collection plays a significant role. This study proposes a two-echelon WMS to minimize operational costs and environmental impact by utilizing the industry 4.0 concept. Both models utilize modern traceability Internet of Thing-based devices to compare real-time information of waste level in bins and separation centers with the threshold waste level (TWL) parameter. The first model optimizes the operational cost and CO 2 emission of collecting waste from bins to the separation center by considering the time windows. A capacitated vehicle routing problem is designed as a later model-based to minimize the cost of waste transferring to recycling centers. In addition, to find the optimal solution, recent meta-heuristic algorithms are employed, and several novel heuristics based on the problem's specifications are developed. Furthermore, the developed heuristics methods are utilized to generate the initial feasible solutions in meta-heuristics and compared with random ones. The performance of the proposed algorithms is probed, and Best Worst Method (BWM) is applied to rank the algorithms based on relative percentage deviation, relative deviation index and hitting time.

The detection of mental disorders through social media has received significant attention. With the growing prevalence of Autism Spectrum Disorder (ASD) and the inherent difficulties in diagnosing adults, researchers have attempted to identify undiagnosed adults. Previous studies have primarily concentrated on analyzing ASD characteristics rather than directly detecting ASD. The current study aims to propose a novel framework to assist in identifying the “lost generation” of ASD adults using their social media posts. Combining traditional and deep learning methods makes it possible to model complex aspects of ASD diagnostic characteristics, which have been relatively overlooked in previous studies. To accomplish this, specific formalizations for users’ patterns of interest as a main ASD diagnostic characteristic are proposed first. The latent linguistic and semantic features of ASD users’ postings are then modeled using deep and transformer-based language models . Finally, all these different aspects are considered together to train a detection model by employing the multi-view learning approach . The experiments show that the feature of idiosyncratic interests has more discriminative power than limited and repetitive interests. The results also indicate that the early fusion of interest-related features along with deep linguistic features outperforms the other examined feature combinations. Additionally, the proposed ‘ i f − i u f ’ fusion model demonstrates improved performance in capturing patterns of interests, compared to baselines. These findings suggest the potential application of the proposed framework towards indirectly identifying ASD users on social media, as evidenced by achieving precision and recall rates of 85% and 82% respectively on the used sampled dataset .

Abstract Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP-hardand is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings.

Traditional production management approaches have paid less attention to integrate supply chain functions. Two main functions in supply chain management (i.e., production planning and distribution) are mutually dependent and important in today's real world applications. More of the time, they should be used simultaneously in an integrated manner to minimize costs in the whole chain. In this paper, a coordinated air transportation and production scheduling problem is studied to minimize the total cost of a supply chain. Additionally, time windows for the due date are considered; however, the due date for delivery time has been considered in the literature. Since this problem is NP-hard, two well-known meta-heuristics (i.e., genetic algorithm and simulated annealing) are used. Some new procedures and operators are developed in these algorithms. To evaluate the performance of the algorithms, different problem sizes are used and the results are compared. Finally, the impacts of increasing the problem size are studied.