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In the recent decade, engineering and technology have been developed rapidly, and new requirements are rising for engineering education. So, new and more focused departments are rising in the engineering faculty. The Turkish economy has been developed, and it is necessary to develop new technologies in industry based on the new investments. The scientific models are required to decide which engineering departments are necessary based on the socio-economic development of the economy. For this aim, we present a strategic analysis of which department can be established to meet the requirements in Turkey in light of the latest developments in the world. In the analysis stage, we listed engineering departments in the world universities and compared them with Turkish universities. We determined the selection criteria for the alternative departments, following these analyses and their related data collected from the Turkish Statistical Institute’s website. We used the new impulse and momentum principle-based weight assignment procedure integrated Technique for Order Preferences by Similarity to the Ideal Solution (IMP-TOPSIS) method to rank alternative departments using different scenarios. We concluded that Artificial Intelligence Engineering is the most suitable alternative. In addition, Aerospace Engineering has the second importance, and Materials Science and Nanotechnology Engineering have the third importance, according to the obtained results.

The Indian banking industry is going through turbulent times. With the lowering of entry barriers and blurring product lines of banks and non-banks since the financial sector reforms, banks are functioning increasingly under competitive pressures. Hence, it is imperative that banks maintain a loyal customer base. In order to achieve this and improve their market positions, many retail banks are directing their strategies towards increasing customer satisfaction and loyalty through improved service quality. Moreover, with the advent of international banking and innovations in the marketplace, customers are having greater and greater difficulty in selecting one institution from another.

Hence, to gain and sustain competitive advantages in the fast changing retail banking industry in India, it is crucial for banks to understand in-depth what customers perceive to be the key dimensions of service quality and to evaluate banks on these dimensions. This is because if service quality dimensions can be identified, service managers should be able to improve the delivery of customer perceived quality during the service process and have greater control over the overall outcome.

The study suggests that customers distinguish four dimensions of service quality in the case of the retail banking industry in India, namely, customer-orientedness, competence, tangibles and convenience. A methodological innovation in this study has been in the use of TOPSIS in the field of customer-perceived service quality. TOPSIS has been used to evaluate and ranking the relative performance of the banks across the service quality dimensions. Identifying the underlying dimensions of the service quality construct and evaluating the performance of the banks across these factors is the first step in the definition and hence provision of quality service in the Indian retail banking industry.

Hesitant fuzzy linguistic term sets (HFLTSs) have attracted lots of attention recently due to their distinguished power and efficiency for dealing with multi-criteria decision making. To extend the applicability of HFLTSs, this paper first presents a more concise comparison formula of hesitant fuzzy linguistic term sets based on probability criterion of uniform distribution and develops novel distance measures considering the hesitance degree. Next, an aggregation scheme is designed to integrate OWD measure into the TOPSIS analysis procedure, in which we calculate different criterion weights according to the distance between criterion values and positive and negative ideal solutions. The weighted distances between the alternatives and positive and negative ideal solutions are calculated. Then the relative closeness degree to the positive ideal solution is calculated to rank all alternatives. Finally, an example is given to illustrate the capabilities and validation of the proposed algorithm.

Public involvement in transportation planning and decision-making process is a key component for ensuring that decisions are made with consideration of public needs and preferences. In this paper, a weighted Euclidean distance based TOPSIS method (WEDTOPSIS) is developed for modeling such a public decision-making process. The Weber–Fechner psycho-physical law is adopted for behavioral modeling of human judgments. Distances to the positive-ideal and negative-ideal solutions of TOPSIS are converted to value measurement models using the Weber–Fechner law. The proposed method is applied on a case where public approval of two different types of public bus operation systems considering six criteria is sought. A numerical illustration is also provided to demonstrate the applicability of the approach. The method provides plausible results in terms of preferences, and shows a high agreement with the ordinary TOPSIS in terms of rankings. Another example showing disagreement on ranking is further analyzed to outline the discrepancies between the TOPSIS and WEDTOPSIS and to indicate the proposed model’s consistency with the behavioral theory. The results are also compared with the results of the additive multi-attribute value (MAVT) method for assessing the performance of the model. Based on the findings, using the proposed method as a decision support tool can be useful, particularly where public input is needed.

Decision-maker (DM) can assign the weights of the distance measures in the relative closeness of technique for order preference by similarity to ideal solution (TOPSIS) for achieving his/her desirable ranking of alternatives. This phenomenon is called strategically setting distance measure weights in this study, which may affect the fairness of the results. In order to prevent this phenomenon, the idea of data envelopment analysis (DEA) is introduced to determine the objective weights of distance measures, and an improved TOPSIS is proposed in this study. The proposed method not only determines weights of the distance measures from an objective perspective and achieves the fully ranking of alternatives, but also provides the directions for improvement. Moreover, in comparison with the other methods, the advantages of the proposed method are analyzed, and the meanings and properties of the new relative closeness are discussed and proved. Finally, an example of evaluating the innovation development of high-tech industries in central and western regions of China is investigated to illustrate the effectiveness and the usefulness of the proposed method.

The refugee problem is one of the most important issues facing the international community today. It not only troubles the countries where refugees are generated but also has a great impact on the countries where refugees are influx. With the continuous development of globalization, the refugee problem is no longer a problem of a country or a region, but a global problem faced by the international community. To cope with the global refugee problem, this paper analyzes the number of refugees in 156 countries from 1990 to 2020 and transforms the refugee population data of these countries into a complex network through a time series visibility graph (VG) method. First, we categorize the income level of 156 countries and analyze the impact of income level on the increase of refugee numbers. Then, the evaluation index of the number of refugees is obtained through the VG method. Finally, a TOPSIS comprehensive evaluation method based on the entropy weight approach is employed to analyze the data. This paper includes two main contributions. First, the application of the VG method provides a new perspective for enriching the modeling of the global refugee population growth trend. Second, this paper shows that the TOPSIS evaluation method based on the entropy weight method is effective, which provides a new method for further research on the global refugee population growth trend.

As a basic industry in the country’s development, the transportation industry has a significant relationship to its normal operation for developing and constructing the national economy. The increase in carbon emissions from transport is an increasingly growing problem, and countries worldwide are also taking measures to reduce emissions. Using time series data over the period from 1990 to 2016, this paper applies the visibility graph approach to transform it into a complex network and excavate some information about the data, then evaluates all countries based on the TOPSIS method. We find that the development of transportation is an important symbol to measure the degree of modernization of a country’s transportation, and low-income countries have lower carbon emissions due to slower transportation development. The results of transportation carbon emissions are especially encouraging for the Chinese government given its long-term and sustained efforts to expand railway and waterway infrastructure, and provide a new perspective for further research on the development trend of global transportation carbon emissions. Meanwhile, it is urgent to speed up the development and use of clean energy for economically developed countries.

In this study, a new version of TOPSIS method is reconstructed to deal with the problem of multi-criteria decision making. Here, the data representation of all alternatives is varied according to different criteria, such as real number, interval-valued number, set-valued number and intuitionistic fuzzy-valued number, etc. Because the distinguishing ability of each criterion can be reflected by its knowledge granularity, naturally, a knowledge granularity method is constructed to measure the criteria weights. Besides, the approach of how to select the ideal solution is redefined, especially for the case that the content of criterion according to all alternatives is not a totally ordered set anymore. What is more, the decision maker’s personal preference is considered, and the concrete indicator value can be calculated by the convex combination of the distance from possible alternatives to ideal solutions. Finally, the validity of the proposed decision-making algorithm is illustrated by a synthetic example.

This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.

This paper realizes the implementation of Improved Multi-objective Mayfly Algorithm (IMOMA) for getting optimal solutions related to optimal power flow problem with smooth and nonsmooth fuel cost coefficients. It is performed by considering Simulated Binary Crossover, polynomial mutation and dynamic crowding distance in the existing Multi-objective Mayfly Algorithm. The optimal power flow problem is formulated as a Multi-objective Optimization Problem that consists of different objective functions, viz. fuel cost with/ without valve point loading effect, active power losses, voltage deviation and voltage stability. The performance of Improved Multi-objective Mayfly Algorithm is interpreted in terms of the present Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II. The algorithms are applied under different operating scenarios of the IEEE 30-bus test system, 62-bus Indian utility system and IEEE 118-bus test system with different combinations of objective functions. The obtained Pareto fronts achieved through the implementation of Improved Multi-objective Mayfly Algorithm, Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II are compared with the reference Pareto front attained by using weighted sum method based on the Covariance Matrix-adapted Evolution Strategy method. The performances of these algorithms are individually analyzed and validated by considering the performance metrics such as convergence, divergence, generational distance, inverted generational distance, minimum spacing, spread and spacing. The best compromising solution is achieved by implementing the Technique for Order of Preference by Similarity to Ideal Solution method. The overall result has shown the effectiveness of Improved Multi-objective Mayfly Algorithm for solving multi-objective optimal power flow problem.

There exists little investigation on multiattribute decision making under intuitionistic fuzzy environments although both crisp and fuzzy multiattribute decision making have achieved a great progress. In this paper, multiattribute decision making problems using intuitionistic fuzzy sets are investigated and the TOPSIS is further extended to develop one new methodology for solving such problems. In this methodology, an interval fractional programming model is constructed on the basis of the relative closeness coefficient using the TOPSIS. Comprehensive evaluation of each alternative, which may be described as an intuitionistic fuzzy set or interval number, is calculated using two auxiliary mathematical programming problems derived from the interval fractional programming model proposed in this paper. Optimal degrees of membership for alternatives are calculated to determine their ranking order using the concept of likelihood based on the ranking method of interval numbers. Implementation process of the method proposed in this paper is illustrated with a numerical example.

The paper investigates the dynamic hybrid multi-attribute group decision making problems, in which the decision information, provided by multiple decision makers at different periods, is expressed in real numbers, interval numbers or linguistic labels (linguistic labels can be described by triangular fuzzy numbers), respectively. We define the concepts of argument variable and dynamic weighted geometric aggregation (DWGA) operator, etc., and give an approach to determining the weights of periods based on the basic unit-interval monotonic (BUM) function, and then propose a dynamic hybrid multi-attribute group decision making method based on the hybrid geometric aggregation (HGA) operator and the DWGA operator. The method first utilizes three different TOPSISs (real-valued TOPSIS, interval-valued TOPSIS and fuzzy-valued TOPSIS) to calculate the individual closeness coefficient of each alternative to the positive and negative ideal alternatives based on the decision information expressed in real numbers, interval numbers and linguistic labels, respectively, provided by each decision maker at each period, and then employs the HGA operator to aggregate all individual closeness coefficients into the collective closeness coefficient corresponding to each alternative at each period. After doing so, the method uses the DWGA operator to fuse the collective closeness coefficients at different periods into the overall closeness coefficient corresponding to each alternative. These overall closeness coefficients are then used to rank and select the given alternatives. We can also reduce the above method to solve the dynamic multi-attribute group decision making problems, in which the decision information, provided by multiple decision makers at different periods, is expressed by means of values from the same type, either real numbers, or interval numbers or linguistic labels. Finally, the developed method is applied to multi-period investment decision making.

Hesitant fuzzy linguistic term set (HFLTS) is a set with ordered consecutive linguistic terms, and is very useful in addressing the situations where people are hesitant in providing their linguistic assessments. Wang [H. Wang, Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making, International Journal of Computational Intelligence Systems8(1) (2015) 14–33.] removed the consecutive condition to introduce the notion of extended HFLTS (EHFLTS). The generalized form has wider applications in linguistic group decision-making. By introducing distance measures for EHFLTSs, in this paper we develop a novel multi-criteria group decision making model to deal with hesitant fuzzy linguistic information. The model collects group linguistic information by using EHFLTSs and avoids the possible loss of information. Moreover, it can assess the importance weights of criteria according to their subjective and objective information and rank alternatives based on the rationale of TOPSIS. In order to illustrate the applicability of the proposed algorithm, two examples are given and comparisons are made with the other existing methods.

This paper, proposes the Multi-Criteria Decision Making (MCDM) methodology for selection of a maintenance strategy to assure the consistency and effectiveness of maintenance decisions. The methodology is based on an AHP-enhanced TOPSIS, VIKOR and benefit-cost ratio, in which the importance of the effectiveness appraisal criteria of a maintenance strategy is determined by the use of AHP. Furthermore, in the proposed methodology the different maintenance policies are ranked using the benefit-cost ratio, TOPSIS and VIKOR. The method provides a basis for consideration of different priority factors governing decisions, which may include the rate of return, total profit, or lowest investment. When the preference is the rate of return, the benefit-cost ratio is used, and for the total profit TOPSIS is applied. In cases where the decision maker has specific preferences, such as the lowest investment, VIKOR is adopted. The proposed method has been tested through a case study within the aviation context for an aircraft system. It has been found that using the methodology presented in the paper, the relative advantage and disadvantage of each maintenance strategy can be identified in consideration of different aspects, which contributes to the consistent and rationalized justification of the maintenance task selection. The study shows that application of the combined AHP, TOPSIS, and VIKOR methodologies is an applicable and effective way to implement a rigorous approach for identifying the most effective maintenance alternative.

Software Quality has many parameters that govern its value. Of them, usually, Reliability has gained much attention of researchers and practitioners. However, today’s ever-demanding environment poses severe challenges in front of software creators as to continue treating Reliability as one of the most important attributes for governing software quality when other important parameters like re-usability, security and resilience to name a few are also available. Evaluating, ranking and selecting the most approximate attribute to govern the software quality is a complex concern, which technically requires a multi-criteria decision-making environment. Through this paper, we have proposed an Intuitionistic Fuzzy Set-based TOPSIS approach to showcase why reliability is one of the most preferable parameters for governing software quality. In order to collate individual opinions of decision makers; software developers of various firms were administered for rating the importance of various criteria and alternatives.

Performance of a software is an important feature to determine the quality of the software developed. Performance testing of modular software is a time consuming and costly task. Several performance testing tools (PTTs) are available in the market which help software developers to test their software performance. In this paper, we propose an integrated multiobjective optimization model for evaluation and selection of best-fit PTT for modular software system. The total performance tool cost is minimized and the fitness evaluation score of the PTTs is maximized. The fitness evaluation of PTT is done based on various attributes by making use of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model allows the software developers to select the number of PTTs as per their requirement. The individual performance of the modules is considered based on some performance properties. The reusability constraints are considered, as a PTT can be used in the same module to test different properties and/or it can be used in different modules to test same or different performance properties. A real-world case study from the domain of enterprise resource planning (ERP) is used to show the working of the suggested optimization model.

Using Taguchi design of experiments (DoE), experiments were conducted with 3 factors and 3 levels. The factors were the depth of cut, spindle speed, and feed. The responses were surface roughness, flank wear, material removal rate, and spindle vibration along $x$ ($V_x$), $y$ ($V_y$), and $z$ ($V_z$) axis. To convert the multi-response optimization problem into a single response optimization problem, the technique for order of preference by similarity to ideal solution (TOPSIS) was applied. The S/N of the closeness coefficients from TOPSIS was calculated and optimum machining conditions were obtained. Further, analysis of variance (ANOVA) was performed to verify which input parameter significantly affects the output responses. From TOPSIS optimization, the responses like surface roughness and flank wear were decreased by 0.99% and 2.55%. The vibration in $x$, $y$, and $z$-axis decreased by 3.84%, 16.87% and 12.48% respectively. This optimization significantly enhances the machining characteristics.

This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi’s L₉ orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi’s $S$/$N$ and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5mm, and traverse speed (TS) of 25mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers.

In this work, initially, the raw AISI 52100 bearing steel was heat-treated to obtain 40 HRC and 45 HRC workpiece hardness. Further, dry hard turning tests were carried out to study the impact of workpiece hardness ($H$), cutting speed ($v$), feed ($f$), and depth of cut ($a$) on cutting force (Fy), surface roughness (Ra), and sound intensity (SI). An economically viable PVD-coated carbide turning tool was implemented for the experiments. The Taguchi L${}_{18}$ (2–3 mixed level) design of experiments was employed to establish the experimental plan in order to save the experimental time, energy, and cost of manufacturing. The results disclosed that the feed has the prevailing consequence on surface roughness with a 96.3% contribution, while it also significantly affects the cutting force with a contribution of 13.8%. The contribution of cutting speed and workpiece hardness on the cutting force was reported as 48.3% and 35.1%, respectively. Higher workpiece hardness required more energy for plastic deformation as a result the cutting force increases with leading hardness. The sound intensity was dominantly influenced by depth of cut (53.3%) and cutting speed (40%). Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was performed to determine the optimum machining parameters. According to the TOPSIS, the optimum level of cutting parameters was predicted as 40 HRC hardness ($H$), 150m/min cutting speed ($V$), 0.15mm/rev feed ($f$), and 0.1mm depth of cut ($a$) while the optimal result of Fy, SI, and Ra were noted as 27.66N, 70.7dB, and 0.86$\mu$m individually.

Laser cutting is a one of the efficient manufacturing processes in industry to cut the hard materials by vaporizing. Stainless steel (SS347) is the most popular material for many applications due its unique characteristics such as efficiency to retain good strength with no inter-granular corrosion even at elevated temperatures. However, the cutting or machining of this material is very difficult. On the other side, the machining cost of laser process is high when compared with other processes. In this work, GRA and TOPSIS techniques are used to study the laser cutting process parameters of SS347. The obtained results were compared with the data mining approach. The input parameters are power, speed, pressure and stand-off distance (SOD) and the output responses of surface roughness, machining time and HAZ are considered. The set of experiments were constructed by using the Taguchi’s L9 method. The predicted closeness value of TOPSIS is greater than the GRA technique and the predominant factor observed is SOD followed by pressure, speed and power. In this work, C4.5-decision tree algorithm is applied to find the most influential parameter. It also represents the low-level knowledge of data set into high level knowledge (If-Then rules form). This investigation reveals that both TOPSIS and data mining suggested the SOD as predominant factor. This result of the optimized process parameters supports the laser assisted manufacturing industries by providing optimized output. Better results were obtained using the optimized set of parameters with the machining time, HAZ and surface roughness being 7.83 s, 0.09 mm and 0.86 $\mu$m, respectively. The results of this work would be very useful for automobiles and aircrafts industries where SS347 is highly employed.