Criteria Decision-making Technique Research Articles

Selection of optimal strategy for managing decentralized solar PV systems considering uncertain weather conditions

Solar power is a promising source of energy that is environmentally friendly, sustainable, and renewable. Solar photovoltaic (PV) panels are the most common and mature technology used to harness solar energy. Unfortunately, these panels are prone to dust accumulation, which can have a significant impact on their efficiency. To maintain their effectiveness, solar photovoltaics s must be cleaned regularly. Eight main techniques are used to clean solar panels: natural, manual, mechanical, robotic, drone, coating, electrical, and acoustic. This study aims to identify the best cleaning method using multiple criteria decision-making (MCDM) techniques. Using the Analytical Hierarchy Process (AHP), Quality Function Deployment (QFD), Fuzzy Technique for Order of Preference by Similarities to Ideal Solution (FTOPSIS), and Preference Selection Index (PSI), this research evaluates all eight cleaning methods based on several criteria that are categorized under cost, performance, resource requirement, and safety in Abu Dhabi. The data are collected from surveys completed by experts in solar and sustainable energy. The AHP, QFD, and PSI results identified natural, manual, and surface coating as the best and most effective cleaning methods. Natural cleaning involves using rainwater primarily to remove dirt and dust; manual cleaning requires cleaning agents and wiping clothes; and surface coatings involve applying a layer of hydrophobic material to the panels to repel dust. Identifying the most effective cleaning method for dust removal from solar panels can ensure optimal efficiency recovery at minimal costs and resources.

A novel approach to three-way decision model under fuzzy soft dominance degree relations and emergency situation

In this study, we attempt to present a three-way multi criteria decision-making (MCDM) technique with fuzzy soft dominance degree relation based on additive consistency to simultaneously choose the best alternative, rank alternatives, and classify alternatives, which would aid decision-makers in making better decisions. First, we provide the additive consistency based fuzzy soft dominance degree relation. Then, based on the fuzzy soft dominance degree relation, we build the idea of the θ-similarity class and define new relative loss functions with unknown risk coefficient vectors and unknown weights of the alternatives. Further, using θ-similarity classes based conditional probability, we offer a theoretical approach for finding total decision cost to attain the best cost-sensitive granularity selection. Furthermore, we also discuss in detail about the newly proposed three-way MCDM method’s decision making mechanisms. We further check the method’s viability using the emergency plan selection problem. Comparative assessments demonstrate that the proposed approach has a better decision-making function than several other methods available in the literature. Experimental evaluations demonstrate that the proposed technique consistently rank the considered object and chooses the best one. Furthermore, the suggested technique can satisfy the decision-makers’ preferences in the classification choice in addition to offering suitable ranking decision recommendation for decision-makers.

An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents

The evaluation of the severity of the factors influencing road accidents with a detailed severity distribution is critical to plan evidence-based road safety improvements and strategies. However, currently available studies use statistical and machine learning (ML) models to evaluate the severity of factors causing road accidents without a detailed severity distribution. Further, most of these available models require significant pre-data processing and have certain data-centric limitations. However, the multi criteria decision-making (MCDM) techniques have the potential to combine expert opinions for robust analysis without any pre-data processing calculations. Thus, this study uses a hybrid analytic hierarchy process (AHP) and the preference ranking organisation method for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors and characteristics that influence road accidents within the Gujarat state, using injury types as criteria and minor road accident influencing factors as alternatives. These 82 minor factors have been further characterised into 18 characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to determine criterion weights, while PROMETHEE ranked all minor variables. Then, after applying k-mean clustering, each ranked factor has been classified as very severe, moderately severe, or severe. The result clearly highlights that overspeeding, male gender, and clear weather conditions have been concluded to be the highly severe factors for Gujarat state. Thus, by providing a clear severity analysis and distribution of factors influencing road accidents, the proposed research may help government stakeholders, researchers, and politicians build severity-based road safety reforms and strategies with clarity.

Fuzzy DEMATEL-ANP-Based Approach for Determining the CRM Readiness Factors

Research in Customer Relationship Management (CRM) in the healthcare sector is in a developing stage and demands further research to get more in-depth insight. We present a comprehensive model to develop and prioritize CRM readiness factors in hospitals using fuzzy DEMATEL-ANP, which helps top managers allocate their limited resources to enhance the required infrastructure for a CRM system. After extracting proper readiness factors, multiple criteria decision-making (MCDM) techniques are applied to assess CRM readiness. First, using the fuzzy decision-making trial and evaluation laboratory approach (DEMATEL), the interdependent relations amongst criteria are designated. Second, the fuzzy analytic network process (ANP) is applied to weigh the sub-criteria. Top management support and structure are the most critical factors which play an essential role in the CRM readiness concept. The importance of top management’s factor has been investigated in many previous works. The structure has been neglected in the previous studies; however, our results demonstrate that it should be considered a crucial factor. This study’s findings can facilitate the CRM system’s adoption process to be employed by decision-makers within hospitals to mitigate the failure rate of the CRM system’s implementation, leading to providing plenty of advantages to the patient association and hospitals. The results of this paper can also have a contribution to the implementation of CRM and artificial intelligence (AI) as an innovative strategy in organizations, particularly hospitals.

Decision support algorithm under SV-neutrosophic hesitant fuzzy rough information with confidence level aggregation operators

<abstract><p>To deal with the uncertainty and ensure the sustainability of the manufacturing industry, we designed a multi criteria decision-making technique based on a list of unique operators for single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) environments with a high confidence level. We show that, in contrast to the neutrosophic rough average and geometric aggregation operators, which are unable to take into account the level of experts' familiarity with examined objects for a preliminary evaluation, the neutrosophic average and geometric aggregation operators have a higher level of confidence in the fundamental idea of a more networked composition. A few of the essential qualities of new operators have also been covered. To illustrate the practical application of these operators, we have given an algorithm and a practical example. We have also created a manufacturing business model that takes sustainability into consideration and is based on the neutrosophic rough model. A symmetric comparative analysis is another tool we use to show the feasibility of our proposed enhancements.</p></abstract>

A FUZZY MULTI-CRITERIA FRAMEWORK FOR THE DEVELOPMENT OF SUSTAINABLE COMMUNITY-BASED TOURISM IN MALAYSIA

Community-based tourism (CBT) is a growing niche market that has positively impacted many developing countries including Malaysia. CBT in Malaysia is recognised as the Malaysian Homestay Experience Programme (MHEP) which is under the coordination of the Ministry of Tourism, Arts and Culture. Previous studies have looked into stakeholders’ contributions and the roles they play in the MHEP. However, the sustainability criteria from the multi-stakeholder perspective should be understood. The collaboration between tourism stakeholders is pivotal in understanding their perspective by considering their diverse viewpoints on critical issues and bridging the current knowledge gap. This research aims to investigate how stakeholders could participate more efficiently in developing and employing a sustainable MHEP. This study applied a mixed-method approach and was divided into two phases. The first phase was the multistage qualitative research and the second phase involved a questionnaire survey with structured questions distributed to the multi-stakeholders. Academicians, government agencies, non-governmental organisations, businesses, host communities, homestay coordinators and tourists were involved in the data collection process. The study applied a two-stage methodology by integrating two multi- criteria decision-making techniques, Decision-Making Trial and Evaluation Laboratory and analytic network process to extend the existing evaluation criteria and weightage. The multi-criteria framework will provide stakeholders as decision-makers the ability to devise plans that are sustainable and compatible with conservation and rural development strategies. This study is decisive in helping stakeholders develop sustainable CBT and contribute to government policies, specifically in terms of MHEP development and management aligned with the Malaysia rural development policy. Sustainable CBT can be an efficient rural economic development tool to generate broader economic distribution and create a more comprehensive spatial development. Furthermore, this study also provides valuable insights for tourism policymakers in terms of efficient policy interventions related to CBT in accelerating sustainable development involving environmental, economic and socio-cultural growth.

Performance analysis of machine learning algorithms and screening formulae for [formula omitted]–thalassemia trait screening of Indian antenatal women

BackgroundCurrently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for β-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCSBTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16]. MethodsA diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden’s Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS. ResultsIt was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden’s index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCSBTT. K-means clustering and the ranking from MCDM methods show that SCSBTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies. ConclusionComparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCSBTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCSBTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.

Hesitant fuzzy partitioned Maclaurin symmetric mean aggregation operators in multi-criteria decision-making

A hesitant fuzzy set, enabling the membership of an element to be a set of various possible values, is highly helpful in describing people’s uncertainty in everyday life. Hesitant fuzzy aggregation operators are the standard mathematical tools for combining many inputs according to predefined criteria into a single result. The classic hesitant fuzzy aggregation operator-based approaches have been criticized because of the ignorance of criteria classification. In this work, we develop the conception of the hesitant fuzzy partitioned Maclaurin symmetric mean and hesitant fuzzy weighted partitioned Maclaurin symmetric mean operators spurred by the partitioned Maclaurin symmetric mean. Afterward, we analyze several features and peculiar instances of the formulated operators. A novel multiple criteria decision-making (MCDM) technique is propounded on the documented hesitant fuzzy weighted partitioned Maclaurin symmetric mean operator; the MCDM method chooses the optimal alternative from several alternatives. A case study of the best location selection for hospital construction is addressed to showcase the practicability of the presented technique. Eventually, we illustrate the devised approach is more widespread and efficacious than prevailing approaches via comparative and sensitive analyses.

A data-driven framework to predict fused filament fabrication part properties using surrogate models and multi-objective optimisation

In additive manufacturing (AM), due to large number of process parameters and multiple responses of interest, it is hard for AM designers to attain optimal part performance without a systematic approach. In this research, a data-driven framework is proposed to achieve the desired AM part performance and quality by predicting part properties and optimising AM process parameters effectively and efficiently. The proposed framework encompasses efficient sampling of design space and establishing the initial experiment points. Based on established empirical data, surrogate models are used to characterise the influence of critical process parameters on responses on interest. Furthermore, process maps can be generated for enhancing understanding on the influence of process parameters on responses of interests and AM process characteristics. Subsequently, multi-objective optimisation coupled with a multi criteria decision-making technique is applied to determine an optimal design point, which maximises the identified responses of interest to meet the part functional requirements. A case study is used to validate the proposed framework for optimising an ULTEM™ 9085-fused filament fabrication part to meet its functional requirements of surface roughness and mechanical strength. From the case study, results indicate that the proposed approach is able to achieve good predictive results for responses of interest with a relatively small dataset. Furthermore, process maps generated from the surrogate model provide a visual representation of the influence between responses of interest and critical process parameters for FFF process, which traditionally requires multiple investigations to arrive at similar conclusions.

Evaluating the operation performance of the Serbian transport Freight system by using multiple criteria decision-making technique

Freight transport is a composite dynamic system that forms the unity of the infrastructure, vehicles, and personnel and which has its purpose or aim. Evaluating operation performance for freight transport systems is important for the government, companies, and users. This paper will use one of the most famous Multiple Criteria Decision-Making (MCDM) techniques to assess the operational performance of freight transport systems. At beginning, the authors will create the evaluation indicator system based on official data with four basic indicators and two sub-indicators for freight transport. These operational data/indicators represent the input of the chosen MCDM method. After that, the Analytic Hierarchy Process (AHP) method will use to calculate the indicator and sub-indicators weight and also will use to calculate the universal evaluate values and rankings of each transport mode performance. The Serbian freight transport system for the 2021st year will be selected as a case study for testing the AHP method within the MCDM approach; the associated suggestions to freight transport will also be given.

Parametric Optimization of Electric Discharge Machining of Metal Matrix Composites Using Analytic Hierarchy Process.

The present study reports on the method used to obtain the reliable outcomes for different responses in electric discharge machining (EDM) of metal matrix composites (MMCs). The analytic hierarchy process (AHP), a multiple criteria decision-making technique, was used to achieve the target outcomes. The process parameters were varied to evaluate their effect on the material erosion rate (MER), surface roughness (SR), and residual stresses (σ) following Taguchi’s experimental design. The process parameters, such as the electrode material (Cu, Gr, Cu-Gr), current, pulse duration, and dielectric medium, were selected for the analysis. The residual stresses induced due to the spark pulse temperature gradient between the electrode were of primary concern during machining. The optimum process parameters that affected the responses were selected using AHP to figure out the most suitable conditions for the machining of MMCs.

A Single-Valued Extended Hesitant Fuzzy Score-Based Technique for Probabilistic Hesitant Fuzzy Multiple Criteria Decision-Making

The probabilistic hesitant fuzzy set (PHFS) is a worthwhile extension of the hesitant fuzzy set (HFS) which allows people to improve their quantitative assessment with the corresponding probability. Recently, in order to address the issue of difficulty in aggregating decision makers’ opinions, a probability splitting algorithm has been developed that drives an efficient probabilistic-unification process of PHFSs. Adopting such a unification process allows decision makers to disregard the probability part in developing fruitful theories of comparison of PHFSs. By keeping this feature in mind, we try to introduce a class of score functions for the notion of the single-valued extended hesitant fuzzy set (SVEHFS) as a novel deformation of PHFS. Interestingly, a SVEHFS not only belongs to a less dimensional space compared to that of PHFSs but also the proposed SVEHFS-based score functions satisfy a number of interesting properties. Eventually, some case studies of multiple criteria decision-making (MCDM) techniques under the PHFS environment are provided to demonstrate the effectiveness of proposed SVEHFS-based score functions.

Improving the Identification and prioritization of the most important risks of safety equipment in FMEA with a hybrid multiple criteria decision-making technique

The purpose of this paper is to develop a new Failure Mode and Effect Analysis (FMEA) framework for identification, prioritization and improvement of failure modes. A hybrid multiple criteria decision-making (MCDM) method combining Stepwise Weight Assessment Ratio Analysis (SWARA) and VlseKriterijumska optimizacija I KOm-promisno Resenje (VIKOR) is used to rank the risk of failure modes identified in FMEA. For this purpose, the SWARA method is utilized to obtain the influential weights and then VIKOR technique is employed to give the prioritization levels for the failure modes in safety equipment. A case study of a gas company in Lorestan Province (Iran) is provided to illustrate the potential application and benefits of the proposed FMEA approach. The obtained results show that the new risk priority model can be effective to find high risk failure modes and create suitable maintenance strategies. The proposed FMEA also can overcome the shortcomings, improve the effectiveness of the traditional FMEA and provide useful information to help in managing risks of safety equipment.

Geometric optimization of segmented thermoelectric generators for waste heat recovery systems using genetic algorithm

Recently, kinds of thermoelectric (TE) materials have been applied in the waste heat recovery system. This paper further presents a geometric optimization for segmented thermoelectric generator (STEG) modules to improve the energy harvesting performance. Sixteen length ratios of two TE materials are optimized to achieve the maximum output power. In the optimization procedure, the genetic algorithm is employed to find the optimal solution, while thermal, flow, and electric fields are fully coupled and solved by the finite element method. The optimization results indicate that the maximum output power of optimal STEGs is 24.39% (parallel-flow) and 24.33% (counter-flow) higher than that of non-segmented TEGs. Besides, the STEG under parallel-flow has better electrical performances due to the increase of temperature difference and the variation of temperature distribution. The interface temperatures are just fluctuating around the turning point of ZT values, despite of the significant variations of end temperatures. The performances of each TE legs are also investigated, which shows that some TE materials have little contribution to the whole TE module. Subsequently, hybrid TEG modules are proposed to reduce the manufacturing difficulty, which are further ranked by a multiple criteria decision-making technique. Compared with optimal STEGs, two best compromise solutions decrease the maximum output power only about 1.5%, but reduce the proportion of segmented TE legs by 56.25% and 50%, respectively.

A Hybrid, Data-Driven Causality Exploration Method for Exploring the Key Factors Affecting Mobile Payment Usage Intention

Several methodologies for academically exploring causality have been addressed in recent years. The decision-making trial and evaluation laboratory (DEMATEL), one of the multiple criteria decision-making (MCDM) techniques, relies on expert judgements to construct an influential network relation map (INRM), revealing the mutual causes and effects of the criteria and dimensions for presentation of the results in a visual manner. The interactional impacts may be evaluated without considering the presumed hypotheses. The DEMATEL has been successfully utilized to assist in complex decision-making problems in various contexts. However, there is controversy about the reliance upon expert judgements, which could be subjective. Thus, this study seeks to overcome this dispute by developing a data-driven, concept-based novel hybrid model which the authors call SEM-DEMATEL. The model first constructs the direct effects between indicators based on structural equation modeling (SEM) and then utilizes DEMATEL to confirm the interdependence among the variables and identify their causes and effects. Finally, an empirical study exploring the key factors affecting mobile payment usage intention is further conducted to demonstrate the feasibility, validity, and reliability of the novel SEM-DEMATEL research approach. The results identify that the perceived value is the key influencing indicator of m-payment usage intention, and the objectivity and efficiency of the research results are compared.

An enviro-economic optimization of a hybrid energy system from biomass and geothermal resources for low-enthalpy areas

In this paper, a combined biomass-geothermal system, intended to supply heat in low enthalpy areas with an extremely cold climate, is optimized based on a nonlinear optimization methodology. A Multiple Criteria Decision-Making technique is coupled with a two-step optimization to achieve the most exploitable energy with the least pollution and cost possible. Three nonlinear objective functions for optimization with three criteria for decision-making were used to minimize the heat generation cost and pollution for a modeled building in Kuujjuaq, Canada. The biomass-geothermal system is split into two parts, surface, and subsurface parts. Twelve scenarios, including three wood pellet types, in four distance ranges from pellet mills, are first defined. Then, via modeling a building for heat demand analysis, the required heat is yielded. Afterward, in the first step of optimization, the cost and pollution functions for surface parts are developed and optimized using the genetic algorithm and screened by the MCDM technique, called TOPSIS, to size the biomass and geothermal subsystems. In the second step, using the sizing from the first step as a constraint, the cost of the geothermal ground heat exchanger is minimized. Twelve scenarios are optimally configured in this way with minimum cost and pollution in relation to operational parameters, such as utilization time and rated powers. The research proposes a methodology that sizes the biomass geothermal (bio-geo) system and can be extended to other technologies, such as turbines, energy storages, or fuel. Furthermore. It provides a correlation between cost and heat generation from biomass-geothermal systems for Kuujjuaq, Canada, and twelve optimal scenarios with system operating parameters. A basis for system sizing and system selection for baseload and peak demand shaving is also considered. Geothermal- and biomass-rated capacities vary with scenarios from 44% to 56% of the total rated capacity.

Evaluation of heat treatment parameters’ effect on some physical and mechanical properties of poplar wood with multi-criteria decision making techniques

Effects of the heat treatment parameters were evaluated relative to some physical and mechanical properties of poplar wood (Populus alba L.) with use of two of the prominent multi criteria decision-making (MCDM) techniques: Entropy and The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). To meet this objective, the test samples were heat-treated at 120, 150, 180, and 210 °C for 2 and 4 h in a laboratory-scale oven. With increasing temperature and duration, the shrinkage and swelling ratios of heat-treated samples were improved. However, the bending strength, modulus of elasticity, and compression strength generally decreased with increasing process temperature and duration. According to (MCDM) analyses, thermal modification definitely improved the physical properties of wood up to a point. Bending strength was found to be the most important determinant of heat treatment success. The other determinants were identified as swelling, compression strength, shrinkage, and modulus of elasticity, respectively. Also, the best results were obtained at 120 °C for 2 h. In general, heat treatment above 150 °C or 4 h is not recommended.

Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan's Healthcare Industry.

Although the electronic health (e-health) cloud computing system is a promising innovation, its adoption in the healthcare industry has been slow. This study investigated the adoption of e-health cloud computing systems in the healthcare industry and considered security functions, management, cloud service delivery, and cloud software for e-health cloud computing systems. Although numerous studies have determined factors affecting e-health cloud computing systems, few comprehensive reviews of factors and their relations have been conducted. Therefore, this study investigated the relations between the factors affecting e-health cloud computing systems by using a multiple criteria decision-making technique, in which decision-making trial and evaluation laboratory (DEMATEL), DANP (DEMATEL-based Analytic Network Process), and modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approaches were combined. The intended level of adoption of an e-health cloud computing system could be determined by using the proposed approach. The results of a case study performed on the Taiwanese healthcare industry indicated that the cloud management function must be primarily enhanced and that cost effectiveness is the most significant factor in the adoption of e-health cloud computing. This result is valuable for allocating resources to decrease performance gaps in the Taiwanese healthcare industry.

South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused an outbreak of pneumonia in December 2019 in Wuhan, China, has spread rapidly throughout the world. This ongoing pandemic has resulted over 55.6 million cases of COVID-19 leading to 1.34 million deaths in more than 188 countries. However, it has been observed that the death rate is significantly in the lower side for the SAARC countries compared to the First World Nations. In this paper, the possible factors have been represented that determine this uneven distribution of COVID-19 deaths. The significance of the factors has been presented in this paper after the data analysis of the factors from 165 different countries. Based on the correlation of the factors and their critical impact towards the concerned countries death toll, the risk index of each factor has been labeled using analytical hierarchy process (AHP)-based MCDM, i.e., multiple criteria decision-making technique. The risk index of all the factors has been used to generate the susceptibility of COVID-19 for each of the countries in study, specifically the SAARC Nations. Finally, the hierarchical clustering was applied to visualize the death toll of the countries corresponding to their susceptibility index.

The Dynamics of Sustainability Risks in the Global Coffee Supply Chain: A Case of Indonesia–UK

Indonesia is one of the leading global coffee producers, and the sustainability of its coffee supply chains is therefore of crucial importance, not only for the coffee sector, but also for the thousands of livelihoods involved. Recognising sustainability risks within supply chains is an important component of understanding logistics. This research investigated the sustainability risks in the Indonesia–UK coffee supply chain by using System Dynamics (SD), a simulation modeling paradigm commonly used to assess complex systems. The model parameters and other components of the dynamic model were extracted through interviews with key stakeholders in the coffee supply chain, supported by evidence from a literature review. The model was then verified and validated in different stages, before being used to investigate five different what-if scenarios to consider changes to parameters in the system. The results of this investigation demonstrate the importance of improving agricultural productivity to support a sustainable coffee supply chain. This research also confirms that by combining the SD model and the multiple criteria decision-making technique, it is possible to achieve a more practical and accurate solution than by the individual tool alone, thus ensuring a better understanding of the whole issues affecting the coffee supply chain.