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The selection and assessment process of appropriate robots became a more complex and complicated task due to various available alternatives and conflicting attributes which must take into consideration. Also, uncertainty which exists usually in the selection process is an unavoidable component that needs to be thoughtfully measured and traditional multi-attribute decision-making approaches failed to deal precisely with it. Since almost all decisions originate from subjective ordinal preferences, handling uncertainty using linguistic variables is also not enough. Thus, the objective of the current study is to present a new extended ordinal priority approach in the neutrosophic environment for the first time to select an appropriate robot. Since neutrosophic is one of the most effective and accommodating tools for handling uncertainty, thus, this method goes to transform linguistic information into triangular neutrosophic numbers using a new presented scale. This scale was used to determine the importance degree of attributes and alternatives regarding experts' opinions. Also, the score function of the triangular neutrosophic number is used for prioritizing attributes and alternatives. The experts in our proposed method have the same degree of importance, since each expert is a person with special skills and knowledge representing mastery of a particular subject. To measure the applicability and efficiency of the proposed approach, an experimental case study has been established for the robot selection problem of a new pharmaceutical city in Egypt for the first time. The source of data in this case study is experts, interviews, and questionnaires. Also, sensitivity and comparative analysis are further made for verifying the power of the proposed approach. The outcome of this study shows that the suggested approach for robot selection is quite helpful and has a great performance under uncertainty over classical and fuzzy ordinal priority approaches. Also, the suggested approach is less consumption of time and simpler than the fuzzy ordinal priority approach. Therefore, we recommend firms and governments to apply it for increasing product quality, hence the profitability of manufacturing industries and decrease needless costs. [ABSTRACT FROM AUTHOR]

End-of-life vehicle (ELV) management is currently one of the most pressing environmental issues. Waste must be recycled to ensure that ELV management is both environmentally and economically sustainable. Cairo boasts the greatest percentage of Egyptian citizens who own a car and the country's oldest automotive fleet. There is a great desire to open a new ELV recycling factory in this city. One of the most difficult strategic issues facing decision-makers is determining where to build new facilities. ELV recycling facility selection is a multi-criteria and highly uncertain challenge, and this research presents a novel approach to help with the process. An extension of the Additive Ratio Assessment (ARAS) approach in a neutrosophic type-2 environment ((N-type-2 ARAS)) is described for the first time. The new approach (N-type-2 ARAS) is used to choose an ELV recycling facility location. The proposed N-type-2 ARAS method's potential and applicability are illustrated in the context of a real-life case study in Egypt. This study used five main criteria, twenty sub-criteria, and four alternatives (locations) to select the best location. The results show that Luxor is the best location. This method has been proven reliable and consistent compared to the current state-of-the-art interval type-2 fuzzy set-based MCDM methods. This study's sensitivity analysis was conducted to show rank stability from the (N-type-2 ARAS) methodology. [ABSTRACT FROM AUTHOR]

Globalization and the rapid growth of technologies are the main challenges facing the manufacturer and its sustainability and survival. Sustainability for any manufacturing plays an important role in competitive advantage which make the manufacturing firm a sustainable competitor. Sustainability in manufacturing is integrated with Industry 4.0 (I4.0) to achieve benefits of economic, environmental, and social. But it has many criteria and factors and contains incomplete and uncertain information. So, we used the neutrosophic sets to overcome this incomplete information and treat with uncertainty environment. The Single-Valued Neutrosophic Set (SVNS) is used to evaluate these criteria, which include three values (Truth, indeterminacy, and falsity). The SVNS is integrated with Multi-Criteria Decision Making (MCDM) methods. The MCDM concept is used in this paper to deal with many conflicting criteria. A Decision-making trial and evaluation laboratory (DEMATEL) is utilized for determining the relation between five main criteria and fourteen sub-criteria in this study. Analytic Hierarchy Process (AHP) is used to compute the weights of the main and sub-criteria. Our framework is applied to a real case study in Egypt to show the validity of our framework. [ABSTRACT FROM AUTHOR]

The demand for energy in Egypt has increased dramatically due to the steady increase in economic and societal development. To meet this need, the use of more renewable energy resources is an essential part of the solution to the ultimate shortage of energy. Because of the multitude of factors involved, the selection of the most suitable renewable energy systems (RESs) is a multi-criteria decision-making (MCDM) problem. There are a good number of works associated with the design of MCDM methods, particularly under uncertain and ambiguous situations. However, efficient incorporation of ambiguity and uncertainty in decision making is still a challenging task, and thus this work proposes a hybrid MCDM approach for selecting the components of a sustainable RES under uncertain environments, utilizing different triangular neutrosophic numbers to deal with unclear information. This proposed hybrid approach starts with defining the relative importance of the selected sustainable criteria by using an Analytic Hierarchy Process (AHP). Later, in the second phase, this hybrid approach combines VIKOR and the TOPSIS to rank the RESs for a real-life case study. The results obtained indicate that concentrated solar power is the most suitable source of renewable energy for Egypt, with photoelectric power the second most suitable. • Development of an evaluation model to select the most appropriate RES in Egypt. • A novel hybrid MCDM (AHP-VIKOR-TOPSIS) approach is proposed for RES selection. • Five criteria and their related sub-criteria are considered for such decision making. • The triangular neutrosophic numbers are introduced to dispose uncertainties. • A case study is proposed to illustrate the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Financial performance evaluation is very significant for manufacturing industries in a competitive environment to achieve investment goals, especially increasing revenue. Financial performance measures must be identified accurately, because the evaluation process reflects the effectiveness of a company. The purpose of this article is to present a plithogenic multi-criteria decision-making (MCDM) model based on neutrosophic analytic hierarchy process (AHP), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, and Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The financial performance in this study is measured by a set of financial ratios. To examine the proposed model, the top 10 steel companies in Egypt are evaluated based on specified financial ratios. According to steel manufacturing experts, the weight of the criteria is determined using AHP method. The company ranking is determined using VIKOR and TOPSIS comparatively. The results show that the obtained ranks of the companies by these methods are almost the same. [ABSTRACT FROM AUTHOR]

Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity and imprecision. It outlines staff contribution and therefore plays a significant part in human resources administration. Additionally, in the era of the Internet of Things and Big Data (IoTBD), professional selection would face several challenges not only to the safe selection and security but also to make wise and prompt decisions especially in the large-scale candidates and criteria from the Cloud. However, the process of professional selection is often led by experience, which contains vague, ambiguous and uncertain decisions. It is therefore necessary to design an efficient decision-making algorithm, which could be further escalated to IoTBD. In this paper, we propose a new hybrid neutrosophic multi criteria decision making (MCDM) framework that employs a collection of neutrosophic analytical network process (ANP), and order preference by similarity to ideal solution (TOPSIS) under bipolar neutrosophic numbers. The MCDM framework is applied for chief executive officer (CEO) selection in a case study at the Elsewedy Electric Group, Egypt. The proposed approach allows us to assemble individual evaluations of the decision makers and therefore perform accurate personnel selection. The outcomes of the proposed method are compared with those of the related works such as weight sum model (WSM), weight product model (WPM), analytical hierarchy process (AHP), multi-objective optimization based on simple ratio analysis (MOORA) and ANP methods to prove and validate the results. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are the backbone in various modern Internet of Things (IoT) smart applications ranging from automated control, surveillance, forest fire detection, etc. One of the most important applications is the smart agriculture. The deployment of WSN in agricultural processes can predict crop yield, soil temperature, air quality, water level, crop price, and the appropriate time for market delivery which will help to increase productivity. In this paper, an enhanced metaheuristic algorithm called multi-verse optimizer with overlapping detection phase (DMVO) is introduced for optimizing the area coverage percentage of WSN. The proposed algorithm is tested on many datasets with different criterions and is compared with other algorithms including the original MVO, particle swarm optimization, and flower pollination algorithm. The experimental results are analyzed with one-way ANOVA test. In addition, DMVO is applied to IoT smart agriculture in East Oweinat area in Egypt and compared with Krill Herd algorithm. In addition, the experimental results are analyzed with Wilcoxon signed-rank test. The experimental results and the statistical analysis prove the prosperity and consistency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A hybridization between Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed • The proposed MOMPA is employed for solving the robot path planning problem with obstacle avoidance constraints. • After the validation experiment, MOMPA is applied for a case study on New Galala city in Egypt. New cities exploit the smartness of the IoT-based architecture to run their vital and organizational processes. The smart response of pandemic emergency response services needs optimizing methodologies of caring and limit infection without direct connection with patients. In this paper, a hybrid Computational Intelligence (CI) algorithm called Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed for planning the COVID-19 pandemic medical robot's path without collisions. MOMPA is validated on several benchmarks and compared with many CI algorithms. The results of the Friedman Ranked Mean test indicate the proposed algorithm can find the shortest collision-free path in almost all test cases. In addition, the proposed algorithm reaches an almost %100 success ratio for solving all test cases without constraint violation of the regarded problem. After the validation experiment, the proposed algorithm is applied to smart medical emergency handling in Egypt's New Galala mountainous city. Both experimental and statistical results ensure the prosperity of the proposed algorithm. Also, it ensures that MOMPA can efficiently find the shortest path to the emergency location without any collisions. [ABSTRACT FROM AUTHOR]

As a result of the increased demand for energy combined with the limitations of traditional energy sources, it is necessary to turn to renewable energy sources such as solar energy, which is one of the most promising technologies to assist the transition towards sustainable development. With the increasing interest in renewable energies in general and solar energy in particular, attempts have been made to ensure that solar energy technologies provide an amount of energy equal to or close to the amount of energy currently consumed. Solar photovoltaic farm applications have become so widespread that they are now relied upon to produce massive amounts of energy for use in factories and homes. In this regard, identifying and prioritizing convenient areas for the construction of expensive solar photovoltaic farms is an important issue. Additionally, determining the suitable geographical locations for photovoltaic farms is not only related to the amount of solar radiation received, but many dimensions must be taken into accounts such as technical, economic, environmental, socio-political dimensions, risks, and their sub-indicators. To overcome the problem of prioritizing and selecting the best location for solar photovoltaic farms to achieve sustainability factors resulting from the presence of many different indicators, this paper applies a hybrid multi-criteria decision-making approach under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators and available alternatives. The proportional importance of the designated dimensions and their sub-indicators was evaluated by the Decision-Making Trial and Evaluation Laboratory. Then, Vlse Kriterijumska Optimizacija Kompromisno Resenje is adopted to rank seven solar photovoltaic farm locations in Egypt. The Benban solar photovoltaic farm is shown to be the most sustainable alternative among the solar photovoltaic farm locations followed by Darb Al Arbaeen solar photovoltaic farm and so has priority for increased installation investments in solar energy production. To validate the proposed approach, results comparisons were implemented to indicate the advantages of the hybrid multi-criteria decision-making methods. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order. [ABSTRACT FROM AUTHOR]

Many countries that derive their energy from fossil fuel sources are turning to renewable, and environmentally friendly energy sources to alleviate the growing concerns about global warming and environmental issues. Bioenergy technology is one of the potential alternatives for renewable energy systems. This paper evaluates sustainable bioenergy production technologies through a case study in Egypt. A comprehensive methodology has been developed in which experts and decision makers are able to use linguistic terms to express their opinions and participate in the decision making required to prioritize the dimensions that affect the sustainability of bioenergy production technologies. Identification of the optimum bioenergy production technology and setting its priorities is a difficult task; many dimensions must be taken into account in the evaluation process, such as the environmental, technical, economic, and social dimensions and their sub-indicators. This paper therefore applies a hybrid multi-criteria decision-making (MCDM) approach that takes into account many conflicting dimensions. In addition, handling of uncertainty was conducted under a neutrosophic environment using trapezoidal neutrosophic numbers (TNNs). Initially, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to identify the relative importance of the dimensions and their sub-indicators. Then, Evaluation based on Distance from Average Solution (EDAS) was employed to rank alternatives. An illustrative case considering seven bioenergy production technologies was studied to confirm the feasibility of the suggested approach, and comparisons were performed to show the advantages of the hybrid MCDM techniques. Sensitivity analysis is conducted to prove the validity and stability of the developed framework with variations in weights. The results of this work provide useful information for energy policy decision makers and the results of the case study indicate that the conversion of agricultural and municipal wastes to biogas is the most suitable sustainable bioenergy technology with a weight of 0.996 followed by oil crops to biodiesel technology with weight 0.539. Image 1 • A new MCDM is proposed for the assessment of bioenergy production technologies. • The proposed approach can address uncertainties and imprecision. • 4 main dimensions and 14 sub-indicators are used in the sustainability assessment. • DEMATEL ranked the identified dimensions and EDAS prioritized the alternatives. • A case study is proposed to illustrate the rationality of the proposed approach. [ABSTRACT FROM AUTHOR]

Developments in wind energy projects have received much interest in the last decade due to the encouragement of sustainable development policies. Consequently, the number of wind energy projects has rapidly increased so that wind energy is an important part of an integrated power system. The success of an offshore wind energy project depends on the selection of the optimal offshore wind power station (OWPS) location, which is often determined through the use of multi-criteria decision-making (MCDM). There are, however, a number of shortcomings in the use of MCDM methods for site selection: 1) the incomplete utilization of information and loss of data in the decision-making process; and 2) the interaction issue in the neutrosophic vicinity is neglected. In this study, to address those shortcomings, we propose a new hybrid methodology for the selection of offshore wind power station location combining the Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)-II methods in the neutrosophic environment. First, a comprehensive index system of evaluation criteria is constructed of OWPS site selection. Then, the neutrosophic set is utilized in the specialist committee decision to express incomplete information. Furthermore, by gathering opinions of specialists, we take into consideration the interaction problem. Through the development of the hybrid method, this research presents rigorous methodological support for site selection in order to achieve benefits in coastal management. The proposed methodology for OWPS site selection is validated through the use of a case study from Egypt. • A new hybrid MCDM (AHP-PROMETHEE II) approach is proposed for OWPS location selection. • Seven criteria and their related subcriteria are considered for such decision making. • The trapezoidal neutrosophic numbers are introduced to dispose uncertainties. • A case study is carried out based on five locations in the Red Sea region of Egypt. • Model's effectiveness is verified by rigorous experimentations and comparisons against different MCDM approaches. [ABSTRACT FROM AUTHOR]