Multi-criteria Decision-making Process Research Articles

Rating ESG key performance indicators in the airline industry

The environmental, social, and governance (ESG) integration finds itself in a transition with rapid developments worldwide, given that the pandemic incentivized companies and investors to focus on other social and governance measures such as ESG ratings. However, the divergence of ratings from the ESG and a lack of transparency lead the companies to report voluntary indicators without standardization. This study aimed to identify the ESG criteria and the most suitable set of key performance indicators (KPIs) in the airline industry after the impact of COVID-19. Furthermore, the second objective was to determine the appropriate weights and ranking of the identified criteria. The multi-criteria decision-making analytical hierarchical process was applied for this purpose. Additionally, the use of intuitionistic variables delivers a comprehensive model for rating the airlines according to their ESG performance. The most relevant criteria found in the study were critical risk management, greenhouse gas emissions, and systemic risk management. Regarding the KPIs, the top-3 weights were the number of flight accidents, jet fuel consumed and sustainable aviation used, and the number of digital transformation initiatives.

Parametric Integration of Multiple Criteria from a Cultural Heritage Perspective

Parametric design is a versatile decision-making methodology that allows multi-criteria optimization. However, it is not as common for addressing aspects such as the cultural heritage of a given community. In this context, qualitative research proposes linking a cultural heritage classification with parametric design algorithms that include a stage of “design thinking” methodology, which culminates in community validation. This paper aims to demonstrate the potential of parametric and low-tech design as a multi-criteria decision strategy. Algorithms were developed with the mechanical properties of a given material, with meteorological information as input data, geometry as response, and with a simultaneously integrated multi-criteria decision-making process to improve the design. Most algorithms take as input variables such as size, function, or geometry and, as output, the structural material that best fits them, but not the opposite. This methodology was tested on a case study with the Misak community in Colombia, using Guadua (Guadua angustifolia), a subfamily of the bamboo, but which is little used. These criteria provided multiple design alternatives that were constantly debated to adjust and test the parameters of the prototype. The principal outcome is that an existing cultural heritage classification allows for the parametric model’s generalization ability.

Determining the optimal partition system of a modular building from a circular economy perspective: A multicriteria decision-making process

To evaluate the impact potentials of materials, products, or systems, the existing approach has been to assess their environmental, economic, or social implications to influence decision-making. This has led the construction industry to an eco-efficient range of doing more with less. The current approach, though efficient, is not effective in causing the construction industry to achieve sustainable development. To attain sustainable development, there is the need for intentional design and detailed assessments of the impact potentials of things from the beginning-of-life/design stage of product development. This comprehensive iterative process termed systemic circularity, has been adopted in this study to determine the optimal partition systems for a modular building from a circular economy perspective. The predictive building systemic circularity indicator, complex cost method and life cycle assessment were used to assess the different partition systems' recovery, cost, and impact potentials. A cradle-to-cradle system boundary was adopted, and an end-of-life allocation approach was implemented to help share the benefits/burdens of the processes beyond the system boundary. The VIKOR method was adopted to enhance the selection of the optimal partition. The chosen optimal partition for the case building had 95.83% of mass recyclable, 26.93 kg CO2/m2 in global warming, and cost 4293 RMB/t. Although the selected partition was optimal among the alternatives, its propensity to be the prototype for systemic circularity still needs improvement. Therefore, a best practice model for achieving systemic circularity was proposed, which could lead the construction industry towards sustainable development when adopted.

GIS-based ecotourism potentiality mapping in the East Hararghe Zone, Ethiopia

The East Hararghe Zone (EHZ) is one of the eastern Ethiopian zones most endowed with diverse landscapes and abundant resources to promote and use for ecotourism development. Potential ecotourism sites have, however, hardly ever been explored so far. The objective of this study was to model and identify potential ecotourism sites by combining Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) and Analytical Hierarchy Process (AHP) methods. Six criteria, including landscape and naturalness, wildlife, topography, accessibility, geology, and climate, were established based on experts' preferences and literature, and the thematic factors for suitability modeling were derived from freely accessible satellite imagery and existing geospatial data and combined using a weighted linear combination (WLC) method. The results reveal that about 26.19% were highly suitable, 35.34% were moderately suitable, 25.28% were marginally suitable, and 13.17% were not suitable. Most areas with high to marginal suitability were found in the southeast, southwest, and uplands in the northern part, while the southernmost extent had the highest proportion of areas unsuitable for ecotourism development. The area under the curve (AUC) assessment has verified the model's performance, resulting in an overall AUC of 74.96%. This suggests that a model-driven map is a reliable spatial support tool for sustainable ecotourism development in areas with diverse landscapes and resources.

Navigating COVID-19: unraveling supply chain disruptions through best-worst method and fuzzy TOPSIS

PurposeThe COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).Design/methodology/approachTo determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.FindingsThe study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).Research limitations/implicationsThis study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.Originality/valueThis study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

Decision Support System for Prioritization of Offshore Wind Farm Site by Utilizing Picture Fuzzy Combined Compromise Solution Group Decision Method.

The selection of offshore wind farm site (OWFS) has important strategic significance for vigorously developing offshore new energy and is deemed as a complicated uncertain multicriteria decision-making (MCDM) process. To further promote offshore wind power energy planning and provide decision support, this paper proposes a hybrid picture fuzzy (PF) combined compromise solution (CoCoSo) technique for prioritization of OWFSs. To begin with, a fresh PF similarity measure is proffered to estimate the importance of experts. Next, the novel operational rules for PF numbers based upon the generalized Dombi norms are defined, and four novel generalized Dombi operators are propounded. Afterward, the PF preference selection index (PSI) method and PF stepwise weights assessment ratio analysis (SWARA) model are propounded to identify the objective and subjective weight of criteria, separately. In addition, the enhanced CoCoSo method is proffered via the similarity measure and new operators for ranking OWFSs with PF information. Lastly, the applicability and feasibility of the propounded PF-PSI-SWARA-CoCoSo method are adopted to ascertain the optimal OWFS. The comparison and sensibility investigations are also carried out to validate the robustness and superiority of our methodology. Results manifest that the developed methodology can offer powerful decision support for departments and managers to evaluate and choose the satisfying OWFSs.

The use of explanatory statistics for mapping groundwater potential zones in a semiarid area: Case of the Waddai province, eastern Chad

In Waddaï province, Eastern Chad, the dry tropical climate of the Sahel region causes a scarcity of surface water resources in the area. To respond to the growing demand on this vital element, and to preserve the social, economic and political stability, several groundwater (GW) exploration programs have been conducted in the previous decades. Of the few thousands of boreholes executed at the province of Waddaï, most fail to find GW reservoirs due to a lack of GW potential maps. In this study we try to fill this knowledge gap using Frequency Ratio (FR) and Weight of Evidence (WoE) techniques. The position of successful wells was used to train the statistical analysis methods and validate the models, while a total of nine topographic, geological and environmental factors were chosen as predictive variables. Results showed that the Southern and central parts of the province have greater potential for the presence of GW. This disposition was shown to be mainly controlled by lithology and slope, where flat areas dominated by unconsolidated alluvial deposits favour infiltration while rocky steep terrains favour surface flow. In comparison to the only previous study recently published, which used and expert opinion Multi-Criteria Decision-Making and Analytical Hierarchical Process based approach for the same purposes, some contradictions with the FR and WoE were observed in some predictive variable class weights, especially regarding lineament density and drainage density. This shows that, in areas where hydrogeological mechanisms and processes are not well understood, it is risky to rely on theoretical simplifying assumptions which are not always true.

Enhancing shear strength and handleability of dewatered clay-rich coal tailings for dry-stacking

Mineral tailings dams pose high pollution risks to the environment and catastrophic failures. Dry stacking has been identified as a promising alternative to mitigate these risks and offers various benefits to the mining industry but lacks systematic research outcomes. To facilitate dry stacking, coal tailings slurries were dewatered using either filtration or centrifugation methods, resulting in a semi-solid form (cake) that can be safely disposed of. The handleability and disposability of these cakes are greatly influenced by the selection of chemical aids (such as polymer flocculants) and the mechanical dewatering technique employed. The effects of polyacrylamide (PAM) flocculants with a range of molecular weight, charge, and charge density are presented. Coal tailings samples with differences in clay mineralogy were dewatered using press filtration, solid bowl centrifugation, and natural air drying. Handleability and disposability of the tailings were assessed by their rheological properties, including yield stress, adhesive and cohesive stresses, and stickiness. Residue moisture, type of polymer flocculants, and clay mineralogy were found to be crucial factors affecting the handleability and disposability of the dewatered cakes. The tailing yield stress (shear strength) increased as the solid concentration increased. In the semi-solid regime (above 60 wt% solids), the tailings displayed stiff exponential growth. Similar trends were observed for stickiness and adhesive/cohesive energy of the tailings with a steel (truck) surface. Adding polymer flocculants increased the shear strength of the dewatered tailings by 10–15%, thus favouring disposability. However, the polymer selection for coal tailing handling and processing is a trade-off between its disposability and handleability, which requires a multi-criteria decision-making process. The current results also suggested that cationic PAM could be most suitable for dewatering by press filtration, while anionic PAM should be selected for dewatering by solid bowl centrifugation.

Scenario-based performance assessment of green-grey-blue infrastructure for flood-resilient spatial solution: A case study of Pazhou, Guangzhou, greater Bay area

Flood resilience has aroused significant interest in coastal areas dealing with a growing frequency of severe rainstorms caused by climate change and urbanisation. At the core of flood resilience is the development of a resilient green-grey-blue infrastructure system that can resist, absorb, and recover from floods in a timely manner. Current flood resilience research, however, is limited to evaluating single infrastructure systems, failing to examine the dynamic process or find ideal spatial infrastructure designs for decision-makers. This research proposes a scenario-based assessment framework for integrated green-grey-blue infrastructure systems to improve flood resilience during urban design decision-making. Rainfall-runoff, drainage networks, and river system models are interlinked to provide quantitative simulation evaluations of water quantity and urban impact in various spatial organisations of infrastructure design. A dynamic, multi-criteria decision-making process is used to reveal the importance of five temporal indicators and rank design alternatives. In Guangzhou, China, the efficiency of this architecture is demonstrated on Pazhou Island, a typical river network area. Given the limited water and green space available, the results demonstrate that submerged areas exert a greater influence during peak rainfall, and blue infrastructure storage becomes an essential factor following rainfall. Furthermore, from a spatial perspective, the looped network of green-blue infrastructure enhances flood resilience, and downstream waterway connections and green space-aligned waterways boost the water storage capacity of green-grey-blue infrastructure. This paradigm can improve flood resilience in the Greater Bay Area in the future, especially in response to heavy rainstorms and river floods.

Selection of strategies to improve energy efficiency in industry: A hybrid approach using entropy weight method and fuzzy TOPSIS

Energy has now turned out to be one of the key factors for competitiveness and economic growth across the globe. In this line, the manufacturing industries have been acknowledged among the biggest drivers of economic development in all nations. Considering rapid industrial development and high rates of energy consumption, energy demand is multiplying faster than ever. Accordingly, it seems critical to improve energy efficiency (EF) and reduce energy consumption in various industries. Finding the common types of energy waste, the strategies to step up EF can consequently provide industrial owners, developers, and planners with solutions to deal with such issues. Selecting the implementation strategies for this purpose is thus assumed multi-criteria decision-making (MCDM) process. In the present study, the ceramic tile industry in Iran, as one of the energy-intensive industries (EIIs) due to its manufacturing process, was investigated. To this end, the strategies to improve EF and their evaluation criteria were initially established by reviewing the related literature and eliciting expert opinions. Then, the Entropy Weight Method (EWM) and the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fTOPSIS) were operated to allocate weights to the criteria, and rank the given strategies, respectively. Based on the literature review, 22 EF strategies and 4 evaluation criteria were retrieved, and finally approved by the experts. The study results revealed that “cost of implementing strategies” received the highest weight among the evaluation criteria. With regard to these criteria, “Reducing leakage in the compressed air production/distribution system”, Reducing power consumption of the presses, ball mills, crushers, and glazing during peak hours by shifting the load to non-peak hours”, as well as “Sealing/filling the pores between the rail wall and wagon (sand seal) in tunnel kilns”, were given the highest priority as the implementation strategies at factory level. Also, according to the change in the weight of the criteria, sensitivity analysis was performed for the robustness and strength of the ranking.

The Role of Thermodynamic and Informational Entropy in Improving Real Estate Valuation Methods.

Price, Cost and Income (PCI) are distinct economic indicators intrinsically linked to the values they denote. These observables take center stage in the multi-criteria decision-making process that enables economic agents to convey subjective utilities of market-exchanged commodities objectively. The valuation of these commodities heavily relies on PCI-based empirical observables and their supported methodologies. This valuation measure's accuracy is critical, as it influences subsequent decisions within the market chain. However, measurement errors often arise due to inherent uncertainties in the value state, impacting economic agents' wealth, particularly when trading significant commodities such as real estate properties. This paper addresses this issue by incorporating entropy measurements into real estate valuation. This mathematical technique adjusts and integrates triadic PCI estimates, improving the final stage of appraisal systems where definitive value decisions are crucial. Employing entropy within the appraisal system can also aid market agents in devising informed production/trading strategies for optimal returns. The results from our practical demonstration indicate promising implications. The entropy's integration with PCI estimates significantly improved the value measurement's precision and reduced economic decision-making errors.

New similarity measure of Pythagorean fuzzy sets based on the Jaccard index with its application to clustering

A similarity measure is being widely used as a tool to create the degree of similarity between two different objects or sets. Several similarity measures between two Pythagorean fuzzy sets (PFSs) are suggested in literature. As far as our knowledge is concerned no one has considered similarity measure between two Pythagorean fuzzy sets (PFSs) based on Jaccard index (JI) so far. Therefore, in this manuscript a novel similarity measure based on JI between two PFSs is suggested. The JI is statics used to compare similarity as well as variety of simple sets. The advantages of proposed similarity based on Jaccard index includes: (i) It can be utilized to compare any two sets and it can also be applied to text data, numerical data and even categorical data. (ii) It is robust to outliers, which means that it is not significantly affected by presence of rare or unusual items exist in the data. This makes it useful in variety of applications including clustering and anomaly detection (iii) It is simple and efficient in computation which make it right choice in many applications where fast processing time is important. Then we gave several numerical examples to show the reliability of suggested similarity measures. Our proposed similarity measure between PFSs based on JI give better properties with respect to numerical results. Based on the numerical analysis, our proposed method is reliable, reasonable and better than the existing ones. Furthermore, we utilized proposed similarity measure with Pythagorean Fuzzy Technique of Order Preference by Similarity to Ideal Solution (PF-TOPSIS) in handling complex daily life issues involving multicriteria decision-making (MCDM) processes. Furthermore, similarity measure between two PFSs is also very useful in clustering. Therefore, we also apply our proposed similarity measure in an application to clustering based on Pythagorean fuzzy data set. Final results revealed that our suggested methods are well suited and reasonable in multicriteria decision-making and clustering environment.

Geospatial and Analytical Hierarchical Process approach for potential sites of water harvesting in lower Kulsi basin, India

Abstract Water crisis issues are common in rural and urban areas, which can be tackled honestly by planning and managing existing water resources. The site suitability map is the principal footstep of planning practice for a region’s sustainable surface and groundwater resource development. The paper aims to delineate and suggest suitable sites for water harvesting for sustainable water resource development by taking the lower Kulsi watershed as a study area. Analytical Hierarchical Process (AHP) and Geographical Information System (GIS) techniques are used in the multi-criteria decision-making process to identify a suitability map of water harvesting structures like percolation tanks, check dams, and surface farm ponds on the basin of the specific key factor of each design. About one hundred ninety-six sites are detected from the most suitable category of suitability based on particular criteria and suggestions for sustainable water conservation in the study area.

Energy retrofitting of heritage buildings: an integrated methodology

A resilient and sustainable way of life presupposes the reduction of the energy consumption and the relevant greenhouse gas (carbon) emissions. This approach leads to the establishment and implementation of various directives and regulations regarding the refurbishment of the existing building stock. Often, these regulations exclude heritage buildings due to the lack of robust methodologies which will ensure a balance between the energy retrofitting and preservation of the building’s heritage values. Heritage buildings however, host a variety of public uses and their holistic and successful energy upgrade could serve as an excellent example of a heritage building energy refurbishment to the community. Thus, there is an emerging need for developing such methodologies. This paper aims to propose an integrated methodology for the energy retrofitting of heritage buildings, based on a multi-criteria decision-making process for the selection of the appropriate retrofitting solutions considering simultaneously the heritage significance, the heritage and the energy impact.

Assessing and Mapping Forest Functions through a GIS-Based, Multi-Criteria Approach as a Participative Planning Tool: An Application Analysis

A relatively new planning tool in Italy is the Local Forest Plan, which stands at a broader level in the land planning scale compared to the Forest Management Plan but at a finer scale considering a Regional or even National Forest Plan. This intermediate scale was considered the most appropriate for working out a planning process based on a multi-criteria assessment of forest functions. The proper functioning of forest ecosystems can provide services conceived as benefits people can obtain through ecological processes that sustain and satisfy essential needs of human life. Four particular forest functions providing services were identified: protective, productive, naturalistic and touristic, respectively. A set of functional criteria, as well as attributes within criteria, were recognized to perform an “Analytic Hierarchy Process” (AHP). A specific application of this methodological approach was selected as a case study. This multi-criteria decision-making process involved the participation of five selected experts in a preliminary phase, followed by the participation of thirty representative stakeholders who contributed to the forest planning process and the subsequent selection of actions to be taken. Data related to forest types and management, physical and morphological features of the forested terrain and infrastructure such as forest roads, touristic trails and hiking pathways, together with natural protected areas or wildlife preservation areas, were explicitly located in space by using a “geographical information service” (GIS) software. The combined application of AHP and GIS can be considered as a significant methodological innovation presented in the case study, together with the implementation of a participative process aimed at the management of forest resources and the creation of possible new professional and entrepreneurial forest activities for the benefit of the entire residential community.

A Multi-Criteria Decision-Making Process for the Selection of an Efficient and Reliable IoT Application

Saudi Arabia initiated its much-anticipated Vision 2030 campaign, a long-term economic roadmap aimed at reducing the country’s reliance on oil. The vision, which is anticipated to be accomplished in the future, underlines compliance, fiscal, and strategy adjustments that will significantly affect all the important features of Saudi economic growth. Technology will be a critical facilitator, as well as controller, of the initiative’s significant transformation. Cloud computing, with the Internet of things (IoT), could make significant contributions to Saudi Vision 2030’s efficient governance strategy. There are multiple IoT applications that cover every part of everyday life, as well as enabling users to use a variety of IoT applications. Choosing the best IoT applications for specific customers is a difficult task. This paper concentrates on the Kingdom’s advancement towards a fresh, as well as enhanced, method of advancing the development phases pertaining to digital transformation, through implementing and adopting modern communications infrastructure and ICT technology. In addition, this study proposes a recommendation system that relies on a multi-criteria decision-making investigation focusing on the fuzzy TOPSIS method for selecting highly efficient IoT applications. The prototype, as well as the hierarchy, was created to assess and correlate critical criteria based on specialist preferences and recommendations. The T5 IoT application alternative was shown to be the most highly effective and reliable choice according to the findings of both fuzzy TOPSIS and TOPSIS.

Selection of infill parameters considering their effects on operational and mechanical performance for material extrusion of CFR-PEEK

PurposeAs newer high performance polymers in mechanical properties become available for material extrusion-based additive manufacturing, determining infill parameter settings becomes more important to achieve both operational and mechanical performance of printed outputs. For the material extrusion of carbon fiber reinforced poly-ether-ether-ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance but also to derive appropriate infill settings through a multicriteria decision-making process considering the conflicting effects.Design/methodology/approachA full-factorial experimental design to investigate the effects of two major infill parameters (i.e. infill pattern and density) on each performance measure (i.e. printing time, sample mass, energy consumption and maximum tensile load) is separately performed to derive the best infill settings for each measure. Focusing on energy consumption for operational performance and maximum tensile load for mechanical performance, the technique for order preference by similarity to ideal solution is further used to identify the most appropriate infill settings given relative preferences on the conflicting performance measures.FindingsThe results show that the honeycomb pattern type with 25% density is consistently identified as the best for the operational performance measures, while the triangular pattern with 100% density is the best for the mechanical performance measure. Moreover, it is suggested that certain ranges of preference weights on operational and mechanical performance can guide the best parameter settings for the overall material extrusion performance of CFR-PEEK.Originality/valueThe findings from this study can help practitioners selectively decide on infill parameters by considering both operational and mechanical aspects and their possible trade-offs.

Multi-Criteria Decision-Making with Novel Pythagorean Fuzzy Aggregation Operators

Unpredictability and fuzziness coexist in decision-making analysis due to the complexity of the decision-making environment. “Pythagorean fuzzy numbers” (PFNs) outperform “intuitionistic fuzzy numbers” (IFNs) when dealing with unclear data. The “Pythagorean fuzzy set” (PFS) is a useful tool because it removes the restriction that the sum of membership degrees be less than or equal to one by substituting the square sum for the sum of membership degrees. This study proposes two aggregating operators (AOs). The recommended operators outperform the already specified PFN operators. The proposed operator is utilised in the multicriteria decision-making process to identify the best candidate for instruction (MCDM).

Prioritization of heat transfer fluids in parabolic trough solar systems using CFD-assisted AHP-VIKOR approach

Parabolic trough solar collectors (PTSCs) are proven technologies and are used in various fields including concentrating solar power, integrated solar combined cycle, industrial process heat, air conditioning, and desalination. The selection of heat transfer fluid (HTF) is a part of the design consideration of concentrating solar technologies (CST) since its technical specifications have a significant effect on the system configuration, operating conditions, and levelized energy cost. Several HTFs including water, thermal oils, molten salts, and gases are already in use for PTSC systems, and others including liquid metals, ionic liquids, and nanofluids are still being tested. Each HTF has pros and cons; thermal and thermodynamic evaluations are practical to compare the performance of an HTF with that of its counterparts however they are insufficient to attribute a parabolic trough CST in terms of design concerns. This study has presented an integrated AHP-VIKOR (Analytical Hierarchy Process - VIekriterijumsko KOmpromisno Rangiranje) multi-criteria decision making (MCDM) approach assisted by computational fluid dynamics (CFD) to prioritize HTFs, particularly for CST and the results are compared to AHP-TOPSIS (Analytical Hierarchy Process - Technique for Order Preference by Similarity to Ideal Solution) for validation. While AHP is chosen to decide the criteria weights in the multi-criteria process, VIKOR is used as decision making to rank and prioritize alternatives. Water, Therminol VP-1, Solar salt, Hitec, Hitec XL, Liquid sodium, Lead-bismuth eutectic, Carbon dioxide, Air, and Helium are evaluated as the HTF inventory and a comprehensive CFD study is conducted to benchmark their thermal and thermodynamic properties in a high concentration PTSC in terms of solidification temperature, upper thermal stability, collector efficiency, pumping power consumption, receiver temperature gradient, and irreversibilities. In addition, other critical design considerations of HTFs including material cost, heat storage capability, material compatibility, operational safety aspects, operational maturity, and solar field control sophistication are taken into account in the MCDM process. Results show that the current parabolic trough CST should have priority for the HTFs of water and molten salt (Hitec) but molten metals and gaseous fluids would gain more insight in near future. This study presents an original perspective for deciding suitable HTFs in PTSC applications where thermal and thermodynamic analyses are limited.

An integrated framework for classification and selection of stocks for portfolio construction: Evidence from NSE, India

Investment extortion in the stock market is a crucial aspect considered by the investors. Therefore, investors implemented different strategies. This study was intended at constructing an investment portfolio (IP) of stocks within the NSE 100 listed companies of Non-parametric nature, fulfilling the basic premise of portfolio making that is, reducing risks while yielding an attractive return higher than any other instrument for the investors. Using DP omnibus test, the desired sample of companies following the non-normal distribution was achieved. Using financial beta, we have selected the outcome based on the nature of their ‘return’ and ‘risk'. We introduce TOPSIS (Technique for order of performance by similarity to ideal solution), a multi-criteria decision-making process (MCDM) to study the profitability of stocks, rank wise for each year, and finally, the Bayes portfolio model help to select the overall profitability associate with low risk for the construction of the portfolio.