Multi-criteria Decision-making Methodology Research Articles

Parametric optimization of bio-inspired engineered sandwich core

The present study aims to design an efficient honeycomb cell structure for enhanced energy absorption. Elytra and bamboo bio-inspired parts were compared using a multi-criteria decision-making methodology (COPRAS) and finite element analysis (through Abaqus/CAE) to select the optimal candidate geometry for the study. A circular elytra-inspired geometry featuring four reinforcing cylinders was selected, demonstrating an increase in Specific Energy Absorption (SEA) of over 68% compared to a baseline geometry of the same mass. Structure optimization, aided by a genetic algorithm (NSGA-II), significantly improved crashworthiness parameters, presenting optimized values for design variables, This resulted in an increase in SEA by up to 94% and a 34% improvement in Crushing Force Efficiency (CFE) compared to a baseline geometry. The robust correlation between the algorithm and Finite Element Method (FEM) results highlights its usefulness for initial design, reducing computational demands. The research selects a circular elytra-inspired geometry featuring four reinforcing cylinders and showcasing the potential of multi-objective optimization algorithm in conjunction with FEM analysis in creating high-performance, lightweight structures for passive safety in aeronautics.

Selection of automotive brake friction composites reinforced by agro-waste and natural fiber: An integrated multi-criteria decision-making approach

The aim of this study is to develop a method for selecting and ranking automotive brake friction composite materials made from agro-waste and natural fibers. This is achieved by using an integrated multi-criteria decision-making methodology. Consequently, six composite samples are produced by reinforcing rice husk, rice husk ash, and Grewia optiva fiber. These samples are then assessed for their frictional properties using a laboratory-scale brake tribometer, in accordance with the requirements outlined in SAE J2522 standard. The selection criteria are based on the analyzed tribological parameters, which include friction coefficients, wear, and friction fluctuations fade-recovery performances. The CRITIC (criteria importance through intercriteria correlation) method was used to determine the importance of criteria for performance evaluation. Based on CRITIC analysis, the average friction coefficient (0.1592), recovery-3 (0.1363), friction fluctuations (0.1347), and performance friction coefficient (0.1240) were identified as the key criteria. The use of MARCOS (measurement of alternatives and ranking according to compromise solution) for ranking assessment reveals that composite materials reinforced with rice husk ash exhibit the most favorable tribological characteristics. The validation of the ranking using different decision-making tools demonstrates the reliability and effectiveness of the proposed approach.

Evaluation Challenges of Leadership Management in the Energy Sector using Multi-Criteria Decision Making Approach

Leadership management has various benefits and advantages in the energy sector, such as improving energy sector development and obtaining sustainability benefits. However, the leadership management faces multiple challenges in the energy sector that must be evaluated and ranked. So, this study used the fusion multi-criteria decision-making (MCDM) methodology to assess and rank the challenges of leadership management in the energy sector. The WASPAS method is a fusion MCDM method used to rank the alternatives. Five experts are invited to evaluate the criteria and alternatives. Fourteen criteria and 13 alternatives were collected in this study. The results show that alternative 4 is the best and alternative 8 is the worst. A sensitivity analysis was conducted to show the stability of the alternatives' ranks. The sensitivity analysis was conducted in two parts. In the first part, the lambda value of WASPAS changed between 0 and 1. In the second part, 14 cases are introduced in criteria weights. The results show that the rank of alternatives is stable in different cases.

Strategic Roadmap for Adopting Data-Driven Proactive Measures in Solar Logistics

This study presents a comprehensive overview of the solar industry’s transition towards resilient energy solutions, emphasizing the critical role of data-driven practices in driving this transition through responsible resource management. As continuous technological refinement is essential to optimize solar energy’s potential, the smart use of available data plays a significant part in enhancing the accessibility of solar panels. Building upon prior research investigating the influence of Big Data on solar logistics, this paper proposes a hybrid Multi-Criteria Decision-Making (MCDM) methodology based on expert experience, providing practical support in the implementation of data-driven proactive measures within the solar industry. Specifically, this study focuses on measures aimed at effectively implementing two main logistic strategies, which are Route Optimization (RO) and Warehouse Management (WM). A rigorous analysis of criteria and measures considered to be relevant in the literature is first conducted. Criteria will be screened and weighted to eventually act as drivers toward measure assessment and prioritization. A final sensitivity analysis culminates in the formalization of findings and in the formulation of a pragmatic roadmap tailored for solar industry practitioners, designed to increase operational efficiency while integrating key sustainability principles across supply chain endeavors.

Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

This study investigates advanced data collection methodologies and their implications for understanding employee and customer behavior within specific locations. Employing a comprehensive multi-criteria decision-making framework, we evaluate various technologies based on four distinct criteria and four technological alternatives. To identify the most effective technological solution, we employ the q-spherical fuzzy rough TOPSIS method, integrating three key parameters: lower set approximation, upper set approximation, and parameter q (where q ≥ 1). Our novel approach combines the TOPSIS method with q-spherical fuzzy rough set theory, providing deeper insights into data-driven decision-making processes in corporate environments. By comparing our proposed framework with existing multi-criteria decision-making methodologies, we demonstrate its strength and competitiveness. This research contributes to enhancing decision-making capabilities in corporate settings and beyond.

기초지방자치단체 지역문화재단의 사업 우선순위 설정에 관한 연구 - 제도주의적 관점에서 계층화 분석법의 적용을 중심으로

This study investigates the prioritization in the operation and project implementation of South Korean local cultural foundations using the Analytic Hierarchy Process (AHP), a multi-criteria decision-making methodology. The purpose of this research is to provide insights into the decision-making processes of local cultural foundation personnel, grounded in the theoretical implications of institutionalism, including historical institutionalism, sociological institutionalism, and rational choice institutionalism. The contributions of this research are twofold: first, it demonstrates the applicability of AHP in multi-layered decision-making analysis, contributing to academic discourse on institutionalism and cultural administration and policy; and second, it offers practical insights to facilitate decision-making processes suitable for the environmental, situational, and conditional contexts of South Korean local cultural foundations. This study is anticipated to be academically and practically beneficial in analyzing decision-making for the promotion of regional culture in South Korea.

Literature Review: Application of MCDM Methodology to Logistics Location Problems

This article provides a comprehensive overview of the application of Multi-Criteria Decision Making (MCDM) methods in the field of logistics location selection. It traces the historical development of MCDM methods from their inception in the 1970s to the sophisticated models and algorithms used today, highlighting the evolution from simple models to complex, intelligent algorithms that incorporate environmental and social considerations. The article emphasizes the importance of combining various MCDM methods to leverage their strengths and address their limitations. It showcases the successful application of these hybrid models in diverse scenarios, such as urban transit hub optimization, inland dry port selection, and the positioning of emergency logistics centers. The article concludes by identifying current challenges, such as handling uncertainties and the complexity of models, and suggests future directions for research, including the development of adaptive models and the integration of artificial intelligence and big data analytics to enhance decision-making in logistics location selection.

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.

Synergistic Strategy of Sustainable Hospital Site Selection in Saudi Arabia Using Spherical Fuzzy MCDM Methodology

Site selection for a hospital is demanding and challenging work. It’s become more complicated when considering various infectious diseases. Multiple criteria and sub-criteria are considered for selecting the most suitable and efficient hospital location. In this paper, we find the most suitable location for multiple disease-related hospitals using the multi-criteria decision-making (MCDM) methodologies. The proposed study is done by the MCDM techniques, namely spherical Entropy and spherical VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. The decision-making for site selection is based on multiple experts’ opinions. Also, we use real data sources in spherical fuzzy numbers (SFN) to capture all its uncertainty. We proposed a new score function and an accuracy function of the SFN to evaluate crisp value from fuzzy numbers. The most prioritized criteria and sub-criteria are determined with their weights. We optimized the proposed site based on demand and criterion weight. Finally, sensitivity analysis and comparative analysis are conducted to check the stability and robustness 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.

Selecting Optimal Charcoal Company using Multi-Criteria Decision Making Methodology

A popular technique used in many industries to assist decision-makers in evaluating and selecting the best alternatives from a variety of accessible possibilities is multi-criteria decision-making (MCDM). This study proposed an MCDM methodology for selecting optimal charcoal from various options and criteria. Various criteria influence the selection problem, such as financial, safety, labour, power supply, production, and transportation. This study used the Combinative Distance Assessment (CODAS) method as an MCDM method to rank the alternatives and use the best one. This study used nine criteria and twenty alternatives. The requirements are divided into positive and negative criteria to compete for the positive and negative ideal solution using the CODAS method. The criteria weights are computed. The rank of alternatives is checked by using the sensitivity analysis. The results show the rank of other options is stable in different cases.

Application of Interval Valued Intuitionistic Fuzzy Uncertain MCDM Methodology for Ph.D Supervisor Selection Problem

The selection of Ph.D (Doctor of Philosophy) supervisor is always a vital and interesting problem in academia and especially for students who want to carry out Ph.D. Nowadays, selecting a supervisor for Ph.D in a scientific manner becomes a challenge for any student because of the variety of options available to the scholar. In this context, the present study aims to formulate a model for Ph.D. supervisor selection from the offered alternatives in an academic institute. A hybrid multi-criteria decision making (MCDM) framework has been applied to select the suitable supervisor of the student’s preferred criteria under interval-valued intuitionistic fuzzy (IVIF) scenario. The IVIF Analytic Hierarchy Process (AHP) has been employed to prioritize the criteria, whereas IVIF Technique for order preference by similarity to ideal solution (TOPSIS) technique is engaged to rank the available supervisors based on criteria weight. A set of eight criteria and five alternatives have been considered for modeling the problem. Moreover, the potential criteria are weighted and ranked by the multiple decision makers in the present study. To examine the consistency and robustness of the proposed integrated approach, sensitivity analysis and comparative analysis have been carried out. From all the analyses, it can be conferred that the suggested approach is quite useful to apply in different decision-making scenarios.

Selection of Academic Staff Based on a Hybrid Multi-criteria Decision Method Under Neutrosophic Environment

Multi-criteria decision-making (MCDM) is a decision-making process that involves assessing and selecting the best alternative from a group of options based on various criteria or qualities. In this research work, we propose and elucidate the theory of neutrosophic logic, which is unique in its approach to evaluating candidates’ performance in a manner that takes into account significant elements and criteria that are essential for the overall process when dealing with unclear, inaccurate, or incomplete data. We propose a novel hybrid integrated MCDM methodology based upon neutrosophic Delphi (N-Delphi) and neutrosophic AHP (N-AHP) methods, which takes into consideration the importance of each decision-maker and their preferences per evaluation criterion. A new MAXMIN threshold value technique treats the criteria under consideration as the decision alternatives and their score functions as their payoff values, thus reducing unnecessary resources by eliminating unimportant criteria during the personnel selection process.

Fuzzy Divergence Measure Based on Technique for Order of Preference by Similarity to Ideal Solution Method for Staff Performance Appraisal

Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Given the inherent uncertainty and ambiguity in multi-criteria decision-making (MCDM) scenarios, the concept of the fuzzy α-cut is leveraged. This allows experts to establish a broader spectrum of rankings, accommodating fluctuations in their confidence levels. To produce consistent criteria weights with the existence of outliers, the fuzzy Method based on the Removal Effects of Criteria (MEREC) is employed. To showcase the viability and effectiveness of the proposed approach, a quantitative illustration is provided through a staff performance review. In this context, the findings are compared with other MCDM methodologies, considering correlation coefficients and CPU time. The results demonstrate that the proposed technique aligns with current distance measure approaches, with all correlation coefficient values exceeding 0.9. Notably, the proposed method also boasts the shortest CPU time when compared to alternative divergence measure methodologies. As a result, it becomes evident that the proposed technique yields more sensible and practical results compared to its counterparts in this category.

A novel q-rung orthopair fuzzy best-worst method, Shannon entropy and MARCOS method for mobile medical app service quality evaluation

In this study, we propose a new hybrid multi-criteria decision-making (MCDM) methodology based on q-rung orthopair fuzzy sets (q-ROFSs) by integrating best-worst method (BWM), Shannon entropy method (SEM) and measurement of alternatives and ranking according to compromise solution (MARCOS). The methodology consists of two main phases: weight determination and alternative ranking. Firstly, the determination of the weight is mainly from both subjective and objective aspects. To determine the subjective weights, we propose a q-rung orthopair fuzzy best-worst method (q-ROF-BWM) satisfying additive consistency, which requires fewer comparisons for more reliable results, and define a new consistency ratio (CR) to test the consistency of comparisons. Additionally, objective weights are obtained using SEM extended to q-ROFSs, which are ignored by many MCDM methods. In the second stage, a new integrated alternative ranking approach called q-ROF-MARCOS is proposed, which does not become more complex with the increase in the number of criteria or alternatives. Finally, the proposed method is applied to the evaluation problem of mobile medical app service quality, and a new mobile medical app service quality evaluation index system based on SERVQUAL model is established. Sensitivity and comparative analysis validate the method’s effectiveness, rationality, superiority, and robustness.

Cervical cancer risk assessment using machine learning integrated fuzzy MCDM methodology

Cervical cancer is entirely preventable if diagnosed at an early stage; however, the current rate of cervical cancer screening participation is not very adequate, and early detection approaches are still open and demanding. Evaluating the risk levels of potential patients in a practical and economic way is crucial to direct risky candidates to screening and establishing potential treatments to conquer the disease. In this study, a machine learning-integrated fuzzy multi-criteria decision-making (MCDM) methodology is proposed to assess the cervical cancer risk levels of patients. In this context, based on behavioral criteria obtained from the publicly accessible cervical cancer behavior risk data set from the UCI repository, the risk levels of patients are evaluated. The proposed methodology is established in three stages: In the first stage, using a machine learning technique, i.e., feature selection, the most effective criteria for predicting cervical cancer risk are selected. In the second stage, the criteria for importance through intercriteria correlation (CRITIC) method is used to assign objective importance levels to the criteria. In the third stage, the cervical cancer risk levels of candidate patients are prioritized using the technique for order preference by similarity to the ideal solution (TOPSIS) and, alternatively, the evaluation based on distance from the average solution (EDAS) techniques. The proposed methodology is developed in an interval-valued Pythagorean fuzzy atmosphere for quantifying the uncertainty in the nature of the problem. This study demonstrates that the feature selection algorithm can be efficiently utilized to determine the fundamental criteria of an MCDM problem and to aid in the early identification of cervical cancer.

Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Purpose Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.Design/methodology/approach The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.Findings The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.Research limitations/implications In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.Practical implications The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).Originality/value This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

Critical evaluation of epoxy-hemp-pineapple-palm fiber composites using hybrid AHM-TOPSIS technique for sustainable structural applications

ABSTRACT This study explores the use of hemp, pineapple, and palm in green composites that are ecological substitutes for synthetic fibers in ballistics and architecture. Multi-criteria decision-making (MCDM) methodologies are taken into consideration to select the optimal choice for the configuration or constituent under deliberation. Integrated MCDM approaches like the Attribute Hierarchy Model (AHM) and TOPSIS are used to rank developed composites. Thermogravimetric analysis (TGA) revealed the thermal stability of palm, pineapple, and hemp fibers, with steadiness temperatures of 276°C, 241°C, and 166°C, respectively. The investigation spans various fiber contents, evaluating thermal, mechanical, and water absorption properties. Hybrid epoxy composites with pineapple and palm fibers exhibit exceptional performance, showcasing heightened tensile strength, yield strength, and hardness. In contrast, the hybrid composite with hemp and pineapple had better water absorption (18%) and specific heat capacity (5.6%) compared to the pineapple-only composite. An in-depth AHM-TOPSIS unveiled that the hybrid epoxy composites (FC7; 0.8524) with palm, hemp, and pineapple exhibit the highest quality, and composite incorporating pineapple fiber (FC2; 0.1393) ranks lowest. Scanning electron microscopy (SEM) scrutinizes fiber morphology and interfacial boundaries in composite subjected to stress rupture.

Deciding Technosignature Search Strategies: Multi-Criteria Fuzzy Logic to Find Extraterrestrial Intelligence

This study presents the implementation of Multi-Criteria Decision-Making (MCDM) methodologies, particularly the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), in prioritizing technosignatures (TSs) for the search for extraterrestrial intelligence (SETI). By incorporating expert opinions and weighted criteria based on the established Axes of Merit, our analysis offers insights into the relative importance of various TSs. Notably, radio and optical communications are emphasized, in contrast to dark side illumination and starshades in transit. We introduce a new axis, Scale Sensitivity, designed to assess the variability of TS metrics. A sensitivity analysis confirms the robustness of our approach. Our findings, especially the highlighted significance of artifacts orbiting Earth, the Moon, or the Sun, indicate a need to broaden evaluative criteria within SETI research. This suggests an enhancement of the Axes of Merit, with a focus on addressing the plausibility of TSs. As the quest to resolve the profound question of our solitude in the cosmos continues, SETI efforts would benefit from exploring innovative prioritization methodologies that effectively quantify TS search strategies.

Effect of fused deposition modeling process parameter in influence of mechanical property of acrylonitrile butadiene styrene polymer

The objective of this study is to investigate how the mechanical properties of components produced using acrylonitrile butadiene styrene (ABS) on a Creality Ender-3 3D printer are affected by various fused deposition modeling (FDM) printing parameters. The impact of various factors, including infill density, printing speed, platform temperature, extruder temperature, and so on, was assessed in terms of their influence on the ultimate tensile strength, yield strength, and elastic modulus of the manufactured components. The impact of each parameter was assessed using a Multi-criteria decision-making (MCDM) methodology. Finally, the second set of parameters, including a 35% infill thickness, 0.25 mm layer level, 40 mm/s printing speed, 75 °C platform temperature, 210 °C extruder temperature, and 75 mm/s travel speed, was discovered to be the most suitable for ABS filament used to make impellers.