Weighted Aggregated Sum Product Assessment Research Articles

Evaluation of sustainable hydrogen production technologies on an industrial scale using comparative analysis of decision-making methods.

Due to its various advantages in different industrial fields, hydrogen can provide energy based on sustainability goals and recreates a critical function in the economy of countries. In this regard, evaluating hydrogen production technologies on an industrial scale is necessary for industrial development and economic growth. Therefore, this study proposes a comprehensive, integrated framework of hybrid fuzzy decision-making for assessing hydrogen production technologies in Iran. In addition to considering sustainability factors, political, technical, and reliability indicators are also assessed in this research to make a comprehensive assessment. The Fuzzy Step-wise Weight Assessment Ratio Analysis (F-SWARA) technique determines the importance of indicators, and the Fuzzy Weighted Aggregates Sum-Product Assessment (F-WASPAS) approach ranks technologies. The weighing findings indicated that the sub-indices of investment cost, technical infrastructure development, and implementation costs were introduced as the most significant sub-indices with weights of 0.226, 0.151, and 0.126, respectively. The evaluation findings with the F-WASPAS method and comparative analysis with various decision-making methods revealed that electrolysis based on solar energy and electrolysis based on wind energy technologies had the highest preference. In this regard, the infrastructure and costs of hydrogen production can be improved by presenting various incentives, such as improving financial conditions while attracting investment and increasing cooperation with top companies. So, sustainable development, economic growth, and industrial development are provided.

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Assessment of Third-Party Reverse Logistics Provider Through the SWARA-WASPAS Integrated Framework

The selection of the most suitable third-party provider for reverse logistics (RL) activities is a key factor in initiating effective RL processes. However, the process of identifying the optimal third-party provider involves navigating through various conflicting objectives and criteria. This task is intricate and time-consuming, necessitating the application of multi-criteria decision-making (MCDM) approaches. This study addresses the evaluation and selection of the best 3PRLP by introducing a comprehensive MCDM framework. Therefore, this study aims to assist a manufacturer specializing in milk and dairy products who has opted to transfer its reverse logistics operations to a logistics service provider. The objective is to aid them in selecting the most suitable 3PRLP. The proposed framework incorporates the SWARA (Step-wise Weight Assessment Ratio Analysis) technique for determining weights and the WASPAS (Weighted Aggregated Sum Product Assessment) method for thoroughly evaluating alternatives. In this study, a panel of four experts made a joint decision after identifying six criteria and five alternatives. The research concluded that the reverse logistics cost (C1) emerges as the most pivotal factor, with the service quality of reverse logistics (C3) being recognized as the least significant criterion. As a result, A1 stands out as the top choice among the 3PRLP.

Implementation of the WASPAS Method in Selection Librarian FST UIN Sumatera Utara

The selection of prospective librarians is necessary to make the right decisions and determine the most suitable individual to meet the library's needs. However, in facing the complexity of changes in the information environment and technological developments, the FST library often needs help selecting prospective librarians using manual calculations, which can affect the effectiveness of the library head and staff in selecting. This research was conducted at the FST Library of the North Sumatra State Islamic University. This time, the researchers used the Weighted Aggregated Sum Product Assessment (WASPAS) method to select prospective librarians. The criteria used are communication skills (C1), mastery of technology (C2), customer service (C3), limited skills (C4), and lack of adaptability (C5). Then, the alternatives used were five samples of prospective librarians who registered at the FST library. The calculations using the WASPAS method showed that the alternative Nazla Maulida (A04) was a suitable candidate to become a librarian at the FST library with a total score of 0.854.

Adaptation challenges of edge computing model in educational institute

Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result.

Analyzing Primary Sector Selection for Economic Activity in Romania: An Interval-Valued Fuzzy Multi-Criteria Approach

This study presents an in-depth analysis of the selection process for primary sectors impacting the economic activity in Romania, employing an interval-valued fuzzy (IVF) approach combined with multi-criteria decision-making (MCDM) methodologies. This research aims to identify eight key criteria influencing the selection of Romanian primary sectors, including technology adaptation, infrastructure development and investment, gross domestic product (GDP), sustainability, employment generation, market demand, risk management and government policies. The current analysis evaluates eight primary sector performances against these eight criteria through the application of three MCDM methods, namely, Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS). Ten economic experts comprising a committee have been invited to provide their views on the criteria’s importance and the alternatives’ performance. Based on the decision-maker’s qualitative judgement, GDP acquires the highest weightage, followed by environmental impact and sustainability, thus indicating the most critical factors among the group. The IVF-MCDM hybrid model indicates the energy sector as Romanian primary sector with the most potential, followed by the agriculture and forestry sector among the list of eight alternatives. It also explores the robustness of results by considering sensitivity analysis and the potential impacts of political and international factors, such as pandemics or armed conflicts, on sector selection. The findings indicate consistency in sector rankings across the different methodologies employed, underscoring the importance of methodological choice and criteria weighting. Additionally, this study sheds light on the potential influence of political and international dynamics on sector prioritization, emphasizing the need for comprehensive decision-making frameworks in economic planning processes.

Evaluation of renewable energy projects based on sustainability goals using a hybrid pythagorean fuzzy-based decision approach

Due to the growth in energy demand and the need to pay attention to sustainable development, many developing countries face the challenge of making policies for growing sustainable energy. With attention to industrial expansion and population growth, Iran, a developing country, is confronting an energy shortage problem. Despite the appropriate capacity of renewable resources in Iran, due to the lack of proper planning for adopting renewable resources in Iran, these resources have a small share of the energy supply. This study aims to present a decision framework for assessing renewable energies in developing countries, in the case of Iran. In addition to technical aspects, sustainability aspects such as economic, social, and environmental factors are proposed to assess renewable sources in this work. The Best-Worst Method (BWM) and the Interval-Valued Pythagorean Fuzzy Weighted Aggregated Sum Product Assessment (IVPF-WASPAS) techniques are suggested to prioritize renewable sources. The findings indicated that solar energy was Iran's most suitable renewable resource. Also, the validation and sensitivity analysis confirmed the findings. This research provides a comprehensive perspective for policy-making and planning of sustainability in the energy industry.

Picture Fuzzy Aggregation Operator-Based Integrated MEREC-WASPAS Technique for Video Conferencing Tool Selection

The COVID-19 epidemic has drastically altered the global landscape and thus organizations are finding it difficult to provide a modern and productive work environment to their stakeholders. As a result, personnel have shifted from in-person interactions to working remotely via video conferencing tools (VCTs). Owing to the onset of COVID-19 outbreak, usage of VTCs has gained great interest among people, especially student fraternity and employees as it reduces the hassle of direct interaction. But selecting the right platform for virtual interaction can be challenging, as it involves a number of factors in order to reduce expenses and maximize productivity. The present treatise has focused on devising a mathematical approach to identify the best VCT by structurizing the problem as a multi-criteria group decision making (MCGDM) model. To carry out the research, the notions of two popular techniques, namely Method based on the Removal Effects of Criteria (MEREC) and Weighted Aggregated Sum Product Assessment (WASPAS) have been extended under Picture fuzzy environment with the aid of Picture fuzzy weighted averaging operator to deal with MCGDM situation. Furthermore, to overcome limitations of existing Picture fuzzy score function (PFSF), a novel PFSF has been developed and its significant properties are analyzed through theoretical and numerical justifications, showing its superiority over the existing one. The proposed PFSF can not only overcome the limitations of the existing SF, but also can rank any type of Picture fuzzy numbers irrespective of their membership and non-membership grades. Thereafter, the extended MCGDM techniques have been integrated with the aid of the proposed novel PFSF and applied to assess the best VCT. The proposed technique has identified Google Meet as the best VCT for virtual communication. Consistency and robustness of proposed technique have been validated through comparative and sensitivity analysis.

Improved q-Rung Orthopair Fuzzy WASPAS Method Based on Softmax Function and Frank operations for Investment Decision of Community Group-Buying Platform

The traditional WASPAS (Weighted Aggregated Sum Product ASsessment) method has attracted widely attention. Unfortunately, the decision-makers’ decision attitude or risk preference is ignored in the existing WASPAS methods. To overcome this shortcoming, this paper embedded expert’s decision attitude with risk preference into WASPAS method for solving multi-attribute group decision-making problems with q-rung orthopair fuzzy (q-ROF) information. We develop the q-ROF Frank softmax weighted averaging (q-ROFFSWA) and q-ROF Frank softmax weighted geometric (q-ROFFSWG) operators based on Frank operations and softmax function. The relevant properties and particular cases are explored, and the monotonicity of these operators’ score functions is analyzed. Then, the q-ROF multi-attribute group decision-making framework based on the improved WASPAS method is constructed. The weighted sum model and weighted product model in traditional WASPAS are replaced by the two proposed aggregation operators. The q-ROF distance measure is utilized to de fuzzy the performance values of alternatives. And a compromise function between optimistic and pessimistic decision attitudes with risk preferences is proposed. Lastly, the presented method is implemented in a real case of investment decision of community group-buying (CGB) platform, and sensitivity analysis and comparative study with existing methods are conducted to verify the practicality, robustness and effectiveness.

An integrated Fine-Kinney risk assessment model utilizing Fermatean fuzzy AHP-WASPAS for occupational hazards in the aquaculture sector

The multidisciplinary field of occupational health and safety (OHS) aims to identify, assess, and mitigating hazards that arise in the workplace or due to the execution of the work. The Fine-Kinney method is a quantitative risk assessment widely applied in many industries to identify, evaluate and prevent potential hazards. Despite the widespread usage of the classic Fine-Kinney approach in several research, its effectiveness in addressing the issue of objective risk appraisal among decision-makers is limited. Therefore, this study proposes a comprehensive occupational risk assessment framework that integrates the Fine-Kinney method with the analytical hierarchy process (AHP) and weighted aggregated sum product assessment (WASPAS) under the Fermatean fuzzy environment. The Fermatean fuzzy set (FFS) offers a more flexible solution in determining the uncertainty and vagueness of information, as it covers membership and non-membership degrees in a wider area compared to intuitionistic and Pythagorean fuzzy sets. The AHP approach is used to calculate the weightage of each each Fine-Kinney risk parameter (probability (P), exposure (E), and consequence (C)), and WASPAS method is used to find the ranking of the hazards. In the proposed model, Fermatean fuzzy weighted geometric (FFWG) operator is used for aggregation of expert opinions. Finally, sensitivity and comparative analyses are also performed to further highlight the adaptability and efficiency of the proposed occupational health and safety risk assessment (OHSRA) model, and a case study analyzing occupational hazards for aquaculture operations is presented to demonstrate the model's rationality and feasibility.

Sistem Pendukung Keputusan dalam Pemilihan Jasa Kurir Terbaik Menggunakan Metode AHP-WASPAS

This research aims to develop a decision support system to assist in selecting the best courier service based on specific criteria. The applied method involves using the Analytical Hierarchy Process (AHP) combined with the Weighted Aggregated Sum Product Assessment (WASPAS) method to ensure accuracy in choosing the criteria for selecting the best courier service. The criteria used in this research for selecting a courier service include delivery cost, delivery speed, number of branches, and service quality. The AHP method is utilized to generate relative weight values for each criterion, while the WASPAS method is employed to calculate the final scores for courier services based on the predetermined weights. The research results provide recommendations for selecting the best courier service. System testing results indicate that the AHP and WASPAS methods can offer more accurate suggestions in courier service selection, leading to optimal outcomes

Hybrid Analysis of Road Service Level Determination Decision Support System

Traffic congestion on the highway is one of the problems often faced by road users, as on the highway traffic in the city of Padang often experiences congestion due to the growth of several highways that are relatively smaller than the growth of traffic volume, causing a decrease in road service levels. This study aimed to determine the level of road services in several traffic lanes in the city of Padang. Data analysis using Hybrid Decision Support System (HDSS) modeling by combining the Analytical Hierarchy Process (AHP) method with the Weighted Aggregated Sum Product Assessment (WASPAS) This method is included in the Multi-Criteria Decision Making (MCDM) group. The AHP method can consistently determine the weight value of each criterion, and the WASPAS method can analyze alternative data to obtain decision results by ranking with the Weighted Sum Model (WSM) and Weighted Product Model (WPM) processes. The ranking results show that there are three types of road services in the city of Padang, namely B, C, and D with an accuracy rate of 0.6947%, this the results of this study using HDSS modeling can provide a better analysis process.

Evaluating the efficiency of green innovation and marketing strategies for long-term sustainability in the context of Environmental labeling

In the contemporary landscape, as environmental concerns take center stage, consumers are increasingly urged to make ecologically friendly choices, facilitated by the prevalence of environmental labeling. This study undertakes a comprehensive investigation into the influence of environmental labeling on customer purchasing behavior, with a specific emphasis on evaluating the effectiveness of green marketing and innovation initiatives. Using the Fuzzy Analytical Hierarchy Process (AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS) methodologies, the research pinpoints the elements and tactics able to influence consumer behavior in connection to environmental labeling. Therefore, seven factors, twenty-one sub-factors, and six strategies are identified. The results of Fuzzy AHP show the prominent role of product attributes, marketing and promotion strategies, and sustainability practices while seeking to change consumer behavior with regard to environmental labeling. In addition, the findings from the Fuzzy WASPAS approach suggest that environmentally friendly packaging, product labeling, and partnerships and collaboration are the most successful green innovation and marketing strategies. The findings of this study can help shape the design of more effective environmental labeling programs and marketing plans for encouraging sustainable purchases.

Evaluation of disassembly line layouts using an integrated fermatean fuzzy decision-making methodology: An application for refrigerator disassembly line

The layout focusing on the physical arrangement of workstations is an important consideration when designing a disassembly line. The layout of a line has a significant impact on its efficiency. The new problem addressed within this study is evaluating the main disassembly line layout types with respect to a thorough hierarchy of evaluation criteria classifying quantitative and qualitative. The addressed problem is modeled as multi-criteria decision-making (MCDM). To this end, a novel methodology that combines Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods under the Fermatean fuzzy environment is proposed for sustainable waste management. Fermatean fuzzy SWARA (FF-SWARA) is used to find out the weight of criteria consisting of disassembly line balancing characteristics, reconfigurability, working conditions, and end-of-life product characteristics. Fermatean fuzzy WASPAS (FF-WASPAS) is used to rank the alternative disassembly line layouts consisting of straight, U-shaped, two-sided, and parallel lines considering the weight of criteria. The multi-objective mixed integer linear programming (MILP) model for the disassembly line balancing problem (DLBP) of each line layout type is integrated into the proposed methodology. This integration reflects the effect of balancing performance measurements such as the number of stations, removing hazardous parts early, removing high demand parts early, and line lead time. Reconfigurability principles, working conditions, and product characteristics that rely on expert opinions are considered under the qualitative criteria. The developed methodology was applied to a refrigerator disassembly line and verified by sensitivity analysis and comparative studies. The results show that the two-sided disassembly line layout is the best option.

Quintic fuzzy sets: A new class of fuzzy sets for solving multi-criteria decision-making problems under uncertainty

This study proposes a new class of fuzzy sets called Quintic Fuzzy Set (QuFS), where the sum of the fifth power of the membership degree and non-membership degree is less than or equal to one. We introduce the complement operator and basic operations on QuFS and further develop various theorems based on these fundamental set operations. We also present the standard modal operators on QuFS and discuss its elementary properties. We then define a novel score and accuracy function for ranking QuFS, as well as the Euclidean and Hamming distances between two QuFSs. A supplier selection example is presented in detail to illustrate the viability and utility of the proposed QuF-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. We also present a multi-criteria decision-making (MCDM) problem on COVID-19 lab selection to perform a comparative analysis of QuFS with Fermatean Fuzzy Set (FFS) and Pythagorean Fuzzy Set (PFS). Furthermore, we demonstrate an existing real case study on selection of Health care waste treatment (HCWT) technique in India and perform a comparative analysis with TOPSIS, SAW (Simple Additive Weighting) and WASPAS (Weighted Aggregated Sum Product Assessment) MCDM methods in QuF framework. With the advent of QuFS, a decision-maker can now employ the fuzzy notion to manage complicated and uncertain human judgements. The comparative results indicate that the suggested QuF measures and QuF MCDM technique outperform other fuzzy concepts regarding effectiveness and validity. Due to the restrictive nature of the current fuzzy set theories, the decision-makers in some complex, uncertain real-life problems are not free to choose the degree of membership and non-membership of their choice. Compared to the Intuitionistic Fuzzy Set (IFS), PFS, and FFS, the QuFS developed in this study has a bigger space grade space. This feature of QuFS enables decision-makers to structure complex, uncertain circumstances with greater flexibility in terms of membership and non-membership degrees.

Multiple-attribute group decision-making approach using power aggregation operators with CRITIC-WASPAS method under 2-dimensional linguistic intuitionistic fuzzy framework

The 2-dimensional linguistic intuitionistic fuzzy variables (2DLIFVs) provide an efficient tool to model the cognitive information of experts in practical circumstances while considering the reliability of the evaluation results. It is a hybrid model representing qualitative information, which derives from integrating the 2-dimensional linguistic variables (2DLVs) with linguistic intuitionistic fuzzy numbers (LIFNs). In this work, we first define a novel score function to establish a proper ranking order among the 2DLIFVs. Next, we define a new distance measure under a 2-dimensional linguistic intuitionistic fuzzy framework to determine the difference between 2DLIFVs. Some mathematical properties and special cases of the defined distance measure are also discussed. The power average (PA) operators provide aggregation tools, allowing aggregated arguments to support each other in the aggregation process. Motivated by the notion of power average (PA) and power geometric (PG), we define four new aggregation operators (AOs) to aggregate 2DLIFVs by considering information correlation in terms of support degree during the aggregation process. Several mathematical properties of the proposed AOs are also investigated. In addition, a new integrated 2DLIF-CRITIC-WASPAS methodology is developed in the 2-dimensional linguistic intuitionistic fuzzy environment to deal with complex decision issues. In this method, a “Criteria Importance Through Intercriteria Correlation (CRITIC)” approach is used to obtain the associate weights of the attributes, and the “Weighted Aggregated Sum Product Assessment (WASPAS)” method is executed to establish a valid ranking order among the alternatives.Moreover, a real problem of venture capital investment related to renewable energy projects is considered to demonstrate the applicability of the proposed methodology. The evaluation process considers four aspects: the management team, service or product, finance, and the market, which are related to 14 attributes. A sensitivity analysis is carried out to explain the reliability of the obtained results. Comparing the proposed method with some extant methods establishes the strength and robustness of the formulated method in real-world situations.

Evaluating airports' Sustainable Development Goals by using multi-criteria decision making methodologies.

The recent growth of the aviation industry, which poses significant environmental challenges, has heightened the pressure on the sustainability of airports. Airport sustainability requires a holistic approach that encompasses economic, social, environmental, and operational aspects. In this regard, the United Nations' 17 Sustainable Development Goals (SDGs) Agenda provides a roadmap for the aviation industry. However, despite recognizing the importance of SDGs, aviation authorities and airports often fail to effectively integrate them into their activities and annual reports. This study aims to evaluate the significance of SDGs for airports and select the airport that prioritizes SDGs the most using Multi-Criteria Decision Making (MCDM) methodologies. This study introduces a novel approach that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods, which are MCDM techniques, to enhance airport sustainability. The SWARA method is employed to evaluate and assign weights to the SDGs in the context of airports. SDG 8 holds the highest level of significance among the goals concerning airports, while SDG 14 falls outside the scope of airport sustainability aspects. Then, five international airports that have been designated as green airports by aviation authorities and assessment organizations are selected, and the optimal alternative is determined using the WASPAS method, considering the weights obtained through SWARA. Dallas/Fort Worth International Airport is the top choice due to its successful implementations and reports aligning with the SDGs.

Influence of defects formation on the cracking behavior of LPBFed Cu-Cr-Zr alloy: Micro-mechanism and CRITIC-WASPAS approach

Advanced strategies and techniques in the Laser Powder Bed Fusion (LPBF) process for printing Cu-Cr-Zr alloy must be created to overcome the major flaws such as cavity formation, unstable thermal capillary convection and thermodynamic instability. In this research, the significant process variables in the LPBF techniques such as Laser Power (LP) of 430, 500 and 570 W, Scanning Velocity (SV) of 550, 750 and 950 mm/s, Hatching Distance (HD) of 0.06, 0.08 and 0.10 mm were considered into account to fabricate a crack-free Cu alloy coupon with maximum Relative Density (RD), Ultimate Tensile Strength (UTS) and Reduced Elastic Modulus (REM). To optimize the process variables, a Criteria Importance Through Intercriteria Correlation (CRITIC) associated with the Weighted Aggregated Sum Product Assessment (WASPAS) approach was employed. Finally, the confirmation experiments were performed for the optimal settings (LP of 430 W, SV of 750 mm/s and HD of 0.06 mm) and the initial settings (LP of 570 W, SV of 550 mm/s and HD of 0.10 mm). The optimal settings show an 8.6 % enhancement in relative density of 99.82 %, which also reflects in 47 % improvement in UTS of 336 MPa and a 27.8 % enhancement in REM of 147.45 GPa compared with the initial settings. With the help of fracture morphology, the effect of defect development on the propagation of fractures on the printed coupons was also investigated. Moreover, the confirmation experiments aligned more with the CRITIC-integrated WASPAS approach.

Evaluation of the Contaminated Area Using an Integrated Multi-Attribute Decision-Making Method

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-Entropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman’s rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach.

Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

 For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project's development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.