Hybrid Multi-criteria Decision Research Articles

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Performance Evaluation of Railway Infrastructure Managers: A Novel Hybrid Fuzzy MCDM Model

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.

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.

Integrated operational model to analyse policies and socio-demography leading to selection of electric vehicle - A case study on Bihar

In the push for sustainability in the power sector, Electric Vehicles show substantial promise. However, they still face hurdles in replacing traditional combustion engine cars, which have dominated the market for decades. Consumer awareness, especially regarding new technologies like Electric Vehicles, remains a significant barrier to adoption. This study aims to bridge the gap between technical aspects and consumer knowledge and a case study is done on the Indian state of Bihar. Using a hybrid Multi-Criteria Decision-Making approach with Triangular Fuzzy Numbers, a customized model is presented to help residents choose the right Electric Vehicle based on their needs and resources. Results are analyzed from customers’ perspective using Quality Function Deployment and conduct Sensitivity Analysis to explore cost trends. This survey examines seven Electric Vehicle models available in India, with MG ZS Electric Vehicle emerging as a potential standout. It's important to note that this research work does not endorse any specific Electric Vehicle model; rather, a mathematical framework is build to guide customers in selecting an Electric Vehicle that suits their requirements.

Enabling organizations to strategically manage risks in circular supply chains

AbstractCircular supply chains (CSCs) prioritize resource efficiency by reusing, remanufacturing, and recycling materials, offering environmental benefits and competitive advantages. However, they are facing various risks and organizational challenges that hinder their efficiency. To ensure long‐term sustainability, this study identifies the key risks and introduces a prioritization model for risk management in CSC strategies proposing a framework for the risk evaluation based on the set of criteria and prioritization model using a combination of three multicriteria decision‐making (MCDM) methods. The model applies fuzzy Delphi‐based analytical hierarchy process (AHP) method to determine the criteria weights and fuzzy axial distance‐based aggregated measurement (ADAM) method for ranking the alternatives. The findings of this study allow for the identification of the most important risks associated with CSC enabling stakeholders to allocate resources strategically and focus efforts on crucial areas. Results highlight that the most important risks are supply chain complexity, resource availability, and quality and technological challenges. The most important contributions are the identification of the most relevant risks that threaten the resilience and sustainability of CSCs, the establishment of the framework for their evaluation, and the development of a novel hybrid MCDM model for their ranking. These contributions are also the main theoretical implications of the study. On a practical level, it enables organizations to strategically manage risks, standardize risk assessment, and improve their competitive advantage by enhancing resilience, reducing disruptions, and lowering operational costs.

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks

This study presents a hybrid multicriteria decision-making approach for evaluating stocks in the Saudi Stock Market. The objective is to provide investors and stakeholders with a robust evaluation methodology to inform their investment decisions. With a market value of USD 2.89 trillion dollars in September 2022, the Saudi Stock Market is of significant importance for the country’s economy. However, navigating the complexities of stock market performance poses investment challenges. This study employs the best–worst method and the technique for order preference by similarity to identify an ideal solution to address these challenges. Utilizing data from the Saudi Stock Market (Tadawul), this study evaluates stock performance based on financial criteria, including return on equity, return on assets, net profit margin, and asset turnover. The findings reveal valuable insights, particularly in the banking sector, which exhibited the highest net profit margin ratios among sectors. The hybrid multicriteria decision-making-based approach enhances investment decisions. This research provides a foundation for future investigations, facilitating a deeper exploration and analysis of additional aspects of the Saudi Stock Market’s performance. The developed methodology and findings have implications for investors and stakeholders, aiding their investment decisions and maximizing returns.

Sustainable Afforestation Strategies: Hybrid Multi-Criteria Decision-Making Model in Post-Mining Rehabilitation

This article describes an effective approach for selecting suitable plant species for afforestation in post-mining rehabilitation. The research was conducted in the Western Black Sea region of Turkey. The aim of the research is to perform accurate criteria weighting and species prioritization for afforestation in post-mining degraded areas. This helps to ensure consistent conditions for the future use of the site as a forest, sustainability of nature, and selection of appropriate species adapted to the difficult post-mining conditions. In this study, which is a multi-criteria decision-making problem (MCDM), the weights of the criteria were determined by stepwise weight assessment ratio analysis (SWARA), and the priority ranking of the species was determined by the analytic hierarchy process (AHP). Analyses were carried out with 10 afforestation criteria and five tree species. According to the analysis, the top three ranked criteria are Economic Efficiency > Carbon Stock and Credit > Reducing Afforestation Cost. The five species’ priority ranking is Robinia pseudoacacia L. (0.456) > Alnus glutinosa subsp. glutinosa (0.248) > Populus nigra subsp. nigra (0.146) > Salix alba L. (0.103) > Quercus robur subs. robur (0.048). The hybrid approach is expected to increase the effectiveness of post-mining rehabilitation works.

Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)

ABSTRACT One of the challenges that organisations face is the risk reduction through creating resilient supply chains. There are different strategies to reach the resiliency. In this paper, we analysed five important strategies: redundancy, backup supplier, reserved capacity, reserved inventory, and increasing work-shifts. These supply chain resilience (SCR) strategies are evaluated using discrete event simulation and hybrid multi-criteria decision-making methods. For this end, Bayesian best-worst method (BWM) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were used for weighting the SCR evaluation criteria and ranking the SCR strategies, respectively. Six discrete event simulation models were generated and run for evaluating each strategy in order to achieve quantitative criteria. On the other hand, the ideas of experts were used for weighting qualitative criteria and also ranking the alternatives. Results showed that the increasing work-shift strategy has the first rank and the reserved capacity strategy has the lowest rank.

A novel hybrid multi-criteria decision methodology for assessing tool materials in friction stir welding process

A novel hybrid multi-criteria decision methodology for assessing tool materials in friction stir welding process

BWM Integrated VIKOR method using Neutrosophic fuzzy sets for cybersecurity risk assessment of connected and autonomous vehicles

Connected and autonomous vehicles (CAVs) have gained significant importance in intelligent transportation systems. However, CAVs are associated with significant cyber-risks caused by different threat-agents, which require robust mitigation strategies for secure operation. Different categories of threat-agents come with varying resources, motivation, and skills that must be addressed effectively for the secure operation of CAVs. This study offers a comprehensive analysis of cybersecurity risks from different categories of threat-agents targeting CAVs. A novel hybrid multi-criteria decision making (MCDM) technique has been developed to prioritize the threat-agent categories as part of the cyber defense mechanism. In this study, the major threat-agent categories are identified from the existing literature and cybersecurity reports. Then expert opinion is collected on the likelihood and severity of cyber-attacks undertaken by different threat-agent categories. Single-valued neutrosophic fuzzy sets (SVNFS) are implemented to consolidate the subjectivity of the linguistic opinions given by the experts. Then Best-Worst Method (BWM) is used to determine the relative criteria weightage on the cyber-attack consequences. These weights are then supplied to the ViseKriterijum-ska Optimizacija I Kompromisno Resenj (VIKOR) method for ranking the categories of threat-agents based on the risk perceived by the experts. A case study is presented with five categories of threat-agents and five loss criteria targeting CAVs to validate the viability of the proposed model. The result shows that insider attackers pose maximum risk for cybersecurity of CAVs. The proposed technique will provide helpful insights to the decision-makers for formulating effective defense strategies against cyber-attackers.

Risk Analysis of the Use of Drones in City Logistics

Drone delivery in city logistics is gaining attention due to road congestion, environmental threats, etc. However, there are risks associated with using drones which can result in hazardous events, such as conflicts in the air, loss of control, and system failures. It is crucial to assess the risks involved in using different types of drones and choose the option with the lowest risk. The existence of different criteria important for this decision imposes the need to apply the multi-criteria decision-making (MCDM) method(s). This paper proposes a new hybrid model that combines the fuzzy Factor Relationship (FARE) method for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the alternatives. A single-rotor microdrone weighing up to 4.4 lb was chosen as the optimal solution, and after that, the most favorable are also the drones of this size (multi-rotor and fixed-wing microdrones). The establishment of a novel hybrid MCDM model, the identified risks, the set of criteria for evaluating the least risky drones, and the framework for prioritizing the drones are the main novelties and contributions of the paper.

Application of Multi-Criteria Decision-Making (MCDM) to Select the Most Sustainable Power-Generating Technology

In response to the growing importance of sustainability and regulatory pressures, companies are increasingly engaging in sustainable projects to mitigate environmental and social harm. Therefore, it is crucial to incorporate sustainability considerations during selecting construction projects in the feasibility phase. This study aims to identify a comprehensive set of sustainability criteria and sub-criteria to help the owners of power-generating plants to select the most sustainable technology for their new projects. Sixteen criteria are identified and categorized under the pillars of sustainability: economic, social, and environmental, plus the technical category. To illustrate practical application, a case study demonstrates the use of these essential sustainability criteria through a hybrid multi-criteria decision-making (MCDM) model for power-generating technology ranking. The results suggest that when stakeholders’ perspectives are weighted approximately equally, considering all sustainability pillars, natural gas with carbon capture is favored for sustainability. A three-scenario sensitivity analysis was performed involving expert opinions from one of the largest power-generating companies in Canada. This integrated generic model can be utilized by industry experts to apply multi-dimensional rational decision-making techniques to solve the complex problem of selecting the most sustainable alternative in construction projects.

Performance measurement of factors affecting consumer experience in healthcare services using fuzzy and grey techniques

PurposeThe importance of consumer experience in service industries, particularly healthcare, is widely acknowledged as it captures the intricacies of quality management. In tandem with the emerging research trends that evaluate service excellence through user experience, this study renders a performance analysis of the dimensions of consumer experience that individually or collectively shape healthcare consumers’ perceptions of service quality.Design/methodology/approachA cross-sectional study was conducted across 13 mid-tier corporate hospitals to collect data from 438 patients. The data was processed through factor analysis in SPSS to confirm sample adequacy and factor extractability. Further, two independent multi-criteria decision-making (MCDM) tools, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) and Grey Relational Analysis (GRA), were executed to render performance analysis of identified factors.FindingsUsing F-TOPSIS, factors such as “information” and “hospital environment” received higher performance ratings, while items related to “communication with doctors” and “humanistic care” received lower rankings. Minor yet anticipated deviations were observed while verifying performance scores using GRA. Nonetheless, both outcomes exhibited a strong correlation coefficient of 97.14%, confirming analytical consistency.Originality/valueHitherto, such usages of hybrid MCDM techniques have rarely been executed to convey a clear understanding of consumers’ experiences in healthcare services. Moreover, the findings provide a clear insight into consumers’ key response areas, which can further be translated to maximize consumer gratification, thus assisting healthcare managers in improving service performance and clinical decision-making.

A hybrid multi-criteria group decision methodology based on fairly operators and EDAS method under interval-valued Fermatean fuzzy environment

A hybrid multi-criteria group decision methodology based on fairly operators and EDAS method under interval-valued Fermatean fuzzy environment

A Taguchi-based hybrid multi-criteria decision-making approach for optimization of performance characteristics of diesel engine fuelled with blends of biodiesel-diesel and cerium oxide nano-additive

Given the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi’s L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands.

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.

Hybrid multi-criteria decision making approach proposal for evaluating workloads of aircraft maintenance technicians.

Aircraft maintenance technicians (AMTs) have the most difficulty in terms of total workload criteria working in line maintenance. This is a very important problem for the Aircraft Maintenance Organization. A systematic and scientific approach is required for its solution. This study proposes a new Multi-Criteria Decision Making (MCDM) based approach to evaluating the total workloads of AMTs to identify the most challenging AMT tasks in the aircraft maintenance organization. A new hybrid MCDM approach is proposed by integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity (TOPSIS) methods to compare AMTs on the basis of workloads according to license categories. The hybrid method proposed in this study evaluates the total workload under three main titles: mental, physical, and environmental workload. Focusing on AMTs working in line maintenance of an aircraft maintenance organization, this study revealed that the most important workload criteria determined by the DEMATEL method are lower back strain, upper back strain, time pressure, and air temperature criteria. The results of the TOPSIS method showed that the license categories of AMTs are sorted according to the workloads as follows: A, B2, B1, and B1 + B2. The AMTs holding a "Category A" license have fewer workloads than the other categories. The findings of the study reveal some measures that might allow authorities to minimize the workload of AMTs. In addition, the study contributes to the literature because there are few studies that systematically analyze total workloads by using MCDM methods.

An OPA-F-Based Single-Valued Neutrosophic Fuzzy MARCOS Approach with Dombi Aggregation Operators for Evaluating Indoor Sex Work Risk in the Economy

The primary risk factor affecting the economy of indoor sex work is examined in this study. Policymakers must thoroughly examine this factor's complex effects since they have a direct bearing on the financial security of employees and the industry's economic dynamics. Interventions can be designed to improve overall economic well-being in the setting of indoor sex work by addressing and mitigating this significant risk. A hybrid multi-criteria decision-making (MCDM) called Fuzzy Ordinal Priority Approach-Single-Valued Neutrosophic Fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution (OPA-F-SVNF-MARCOS) is created to determine the most important parameter in the study. A thorough multi-criteria decision-making (MCDM) system, such as the MARCOS approach, allows for the consideration of a wide range of elements that impact the danger of indoor sex work, such as social dynamics, legal frameworks, economic indicators, and public health concerns. In the suggested method, OPA-F establishes the weights for the criteria, and SVNF-MARCOS assigns a ranking to each option. Furthermore, Dombi aggregation operators capture the interaction between risk variables and the degrees of uncertainty they are linked with. This study helps those involved in this delicate field—policymakers, law enforcement, and social care providers—make decisions that are better informed and grounded in evidence.