Decision-making Approach Research Articles

Sustainable management of polyethylene terephthalate waste flow using a fuzzy frank weighted assessment model

The consequences for the ecosystem of polyethylene terephthalate (PET) waste are becoming increasingly significant and widespread. Companies managing PET waste strive to enhance sustainability in all areas. The development of systematic decision-making approaches and frameworks for PET waste management is strongly needed. This research aims to present a new methodological framework for the categorization of the most efficient PET waste management solutions. The introduced fuzzy Frank weighted sum product assessment (FWESPA) model enables rational and flexible reasoning by nonlinearly processing uncertain information. A nonlinear aggregation function is proposed for the fusion of fuzzy strategic options. It is advantageous in simulating the impact of strategic options on a final decision. An integral part of the introduced fuzzy FWESPA model is a reverse sorting algorithm. This innovative algorithm can improve the performance of traditional normalization techniques. Also, an improved fuzzy Frank ordinal priority approach linear model is formulated to define the significance of evaluation criteria. The comprehensive real-life study demonstrates the proposed decision-analytics-based approach. The results showed the following rankings of considered alternatives: “recycling” (ℤA2 = 0.8565) > “energy recovery” (ℤA1 = 0.7364) > “remanufacturing” (ℤA4 = 0.690) > “incineration” (ℤA3 = 0.6592). Based on the results presented, alternatives “recycling” (A2) and “energy recovery” (A1) represent dominant alternatives with a slight advantage of recycling. Research findings can be used when deciding the appropriate way to enhance PET waste handling. The findings also describe the benefits and limitations of each treatment option for PET waste, as well as highlight the crucial challenges.

A Two-Person Zero-Sum Game Approach for a Retirement Decision with Borrowing Constraints

A Two-Person Zero-Sum Game Approach for a Retirement Decision with Borrowing Constraints

Enterprise risk management in supply chain operation: a fuzzy risk prioritization approach

PurposeTraditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.Design/methodology/approachWe propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.FindingsThe proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.Practical implicationsThe adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.Originality/valueThis study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Application of a Decision-making Approach Based on Factor Analysis and DEMATEL for Evaluating Challenges of Adopting Electric Vehicles

Application of a Decision-making Approach Based on Factor Analysis and DEMATEL for Evaluating Challenges of Adopting Electric Vehicles

An integrated multi-criteria decision-making approach for life cycle approach in road construction projects

An integrated multi-criteria decision-making approach for life cycle approach in road construction projects

Parent Narratives Provide Perspectives on the Experience of Care in Trisomy 18.

Trisomy 18 syndrome, also known as Edwards syndrome, is the second most common autosomal chromosome syndrome after Down syndrome. Trisomy 18 is a serious medical disorder due to the increased occurrence of structural defects, the high neonatal and infant mortality, and the disabilities observed in older children. Interventions, including cardiac surgery, remain controversial, and the traditional approach is to pursue pure comfort care. While the medical challenges have been well-characterized, there are scant data on the parental views and perspective of the lived experience of rearing a child with trisomy 18. Knowledge of the parental viewpoints can help clinicians guide families through decision-making. Our aim was to identify parents' perspectives by analyzing a series of narratives. In this qualitative study, we collected 46 parent narratives at the 2015 and 2016 conferences of the Support Organization for Trisomy 18 & 13 (SOFT). The participants were asked to "Tell us a story about your experience." Inductive content analysis and close reading were used to identify themes from the stories. Dedoose, a web-based application to analyze qualitative data, was used to code themes more systematically. Of the identified themes, the most common included Impact of trisomy 18 diagnosis and Surpassing expectations. Other themes included Support from professionals, A child, not a diagnosis, and Trust/lack of trust. We examined the voice and the perspectives of the parents in their challenges in caring for their children with this life-limiting condition. The exploration of the themes can ideally guide clinicians in their approach to the counseling and care of the child in a shared decision-making approach.

A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers.

Endometriosis (EMs) and adenomyosis (AD) are common gynecological diseases that impact women's health, and they share symptoms such as dysmenorrhea, chronic pain, and infertility, which adversely affect women's quality of life. Current diagnostic approaches for EMs and AD involve invasive surgical procedures, and thus, methods of noninvasive differentiation between EMs and AD are needed. This retrospective cohort study introduces a novel, noninvasive classification methodology employing a stacked ensemble machine learning (ML) model that utilizes peripheral blood and coagulation markers to distinguish between EMs and AD. The study included a total of 558 patients (329 with EMs and 229 with AD), in whom key hematological and coagulation markers were analyzed to identify distinctive profiles. Feature selection was conducted through ML (logistic regression, support vector machine, and K-nearest neighbors) to determine significant hematological markers. Red cell distribution width, mean corpuscular hemoglobin concentration, activated partial thromboplastin time, international normalized ratio, and antithrombin III were proved to be the key distinguishing indexes for disease differentiation. Among all the ML classification models developed, the stacked ensemble model demonstrated superior performance (area under the curve = 0.803, 95% credibility interval = 0.701-0.904). Our findings demonstrate the effectiveness of the stacked ensemble ML model for classifying EMs and AD. Integrating biomarkers into this multi-algorithm framework offers a novel approach to noninvasive diagnosis. These results advocate for the application of stacked ensemble ML utilizing cost-effective and readily available peripheral blood and coagulation indicators for the early, rapid, and noninvasive differential diagnosis of EMs and AD, offering a potentially transformative approach for clinical decision-making and personalized treatment strategies.

Characterization of indigenous lactobacilli from dairy fermented foods of Haryana as potential probiotics utilizing multiple attribute decision-making approach

The interest in region-specific ethnic fermented foods and their functional microbiota is rising. The demands for functional foods are continuously rising, so research is going on to develop nutritious food with many beneficial attributes and low safety concerns. The present study was designed to isolate and characterize lactobacilli probiotic candidates from locally resourced fermented foods (dahi, lassi, and raabadi) to make ready-to-eat fermented functional products later. Cultures were isolated from 82 fermented food samples collected from different villages. The initial experiments of gram staining, catalase test, and carbohydrate fermentation were assessed for the morphology, purity, and primary characterization on the genus level, which was verified through molecular characterization using PCR. Seven lactobacilli strains (no. MS001-MS007) were then assessed for safety, probiotic candidacy, phytase degradation, and biofilm forming abilities. All seven bacterial cultures showed no hemolytic activity and antibiotic sensitivity against more than 14 antibiotics out of 20. All seven lactobacilli isolates were able to tolerate pH 3.0, 0.3% bile 0.5% pancreatin, lysozyme (100 mg/L to 300 mg/L) and also shown possessed phytase degradation ability. All the cultures showed antioxidative potential and biofilm formation ability. Culture MS007 showed considerably higher bile salt hydrolase activity among all the isolates, whereas MS005 possessed excellent phytate degradation ability among others. Bacterial strains were identified using 16S rRNA gene sequencing. Moreover, the order of preference of isolates was calculated using the multidimensional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on probiotic and other functional properties. The most promising attributes showing cultures were recognised as Limosilactobacillus fermentum MS005 and Lactiplantibacillus plantarum MS007, which could be further used for functional food product development.Graphical

Utility of Biomarker-Informed Drug Interaction Evaluation in Drug Development and Regulatory Decision Making.

The measurement of endogenous biomarkers in plasma and urine before and after administration of an investigational drug in a clinical study may provide an early indication of its drug-drug interaction (DDI) potential via a specific pathway. In the first international harmonized guideline on drug interaction studies, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M12, endogenous biomarkers have been recognized as an emerging approach in the transporter- and enzyme-based DDI risk assessment. Clinical Pharmacology Roundtable Conference 2024 held at Pharmaceuticals and Medical Devices Agency (PMDA) brought together experts from regulatory agencies, academia, and industries to discuss potential advantages and challenges of the biomarkers approach in drug development and regulatory decision making. This meeting report facilitates stakeholders involved in drug development in better understanding the utility of biomarker approaches and promotes early implementation of biomarker-informed DDI evaluation in regulatory use.

Selection of an Appropriate Global Partner for Companies Using the Innovative Extension of the TOPSIS Method with Intuitionistic Hesitant Fuzzy Rough Information

In this research, we introduce the intuitionistic hesitant fuzzy rough set by integrating the notions of an intuitionistic hesitant fuzzy set and rough set and present some intuitionistic hesitant fuzzy rough set theoretical operations. We compile a list of aggregation operators based on the intuitionistic hesitant fuzzy rough set, including the intuitionistic hesitant fuzzy rough Dombi weighted arithmetic averaging aggregation operator, the intuitionistic hesitant fuzzy rough Dombi ordered weighted arithmetic averaging aggregation operator, and the intuitionistic hesitant fuzzy rough Dombi hybrid weighted arithmetic averaging aggregation operator, and demonstrate several essential characteristics of the aforementioned aggregation operators. Furthermore, we provide a multi attribute decision-making approach and the technique of the suggested approach in the context of the intuitionistic hesitant fuzzy rough set. A real-world problem for selecting a suitable worldwide partner for companies is employed to demonstrate the effectiveness of the suggested approach. The sensitivity analysis of the decision-making results of the suggested aggregation operators are evaluated. The demonstrative analysis reveals that the outlined strategy has applicability and flexibility in aggregating intuitionistic hesitant fuzzy rough information and is feasible and insightful for dealing with multi attribute decision making issues based on the intuitionistic hesitant fuzzy rough set. In addition, we present a comparison study with the TOPSIS approach to illustrate the advantages and authenticity of the novel procedure. Furthermore, the characteristics and analytic comparison of the current technique to those outlined in the literature are addressed.

Modified fragility functions for offshore wind turbines considering soil-structure interaction subjected to wind, wave, and seismic loads

Investment allocation for offshore wind turbines (OWT) as an important class of structures is typically carried out through supporting decision-making approaches utilizing some fragility functions. This study attempts to deliver fragility functions for OWTs on monopile foundations accounting for soil-structure interaction (SSI) effects. Simultaneous wind, wave, and earthquake loads were considered probabilistically by adjusting their occurrence hazard levels for predefined damage states in diverse performance levels. The designated damage states in this study are defined based on collapse probability and some targeted performance levels which could be very straightforward to distinguish. The damage state detection is based on rotation in the connection section of the tower’s transition part to the foundation, which perceptibly reveals the effects of SSI on fragility functions. The expected results comprise modified fragility functions accounting for SSI effects contributing to less median spectral acceleration, more evidently rotational demands, further dispersions, and a subsequent dominant increase in the probability of exceeding performance limit states. Considering operational performance level, the most applied design performance level for turbines as an important class of structures, not considering the SSI effects could noticeably underestimate the demands and lead to high-risk decisions.

Values and preferences towards the use of prophylactic low-molecular-weight heparin during pregnancy: a convergent mixed-methods secondary analysis of data from the decision analysis in shared decision making for thromboprophylaxis during pregnancy (DASH-TOP) study

BackgroundVenous thromboembolism (VTE) in pregnancy is a major cause of maternal morbidity and mortality, and the use of preventive low-molecular-weight heparin (LMWH) can be challenging. Clinical guidelines recommend eliciting pregnant individuals’ preferences towards the use of daily injections of LMWH and discussing the best option through a shared decision-making (SDM) approach. Our aim was to identify individuals’ preferences concerning each of the main clinical outcomes, and categorize attributes influencing the use of LMWH during pregnancy.MethodsDesign: Convergent mixed-methods.Participants: Pregnant women or those planning a pregnancy with VTE recurrence risk.Intervention: A SDM intervention about thromboprophylaxis with LMWH in pregnancy.Analysis: Quantitatively, we report preference scores assigned to each of the health states. Qualitatively, we categorized preference attributes using Burke’s pentad of motives framework: act (what needs to be done), scene (patient’s context), agent (perspectives and influence of people involved in the decision), agency (aspects of the medication), and purpose (patient’s goals). We use mixed-method convergent analysis to report findings using side-by-side comparison of concordance/discordance.ResultsWe comprehensively determined preferences for using LMWH by pregnant individuals at risk of VTE: through value elicitation exercises we found that the least valued health state was to experience a pulmonary embolism (PE), followed by major obstetrical bleeding (MOB), deep vein thrombosis (DVT), and using daily injections of LMWH (valued as closest to a ‘healthy pregnancy’); through interviews we found that: previous experiences, access to care (scene) and shared decision-making (agent) affected preferences. LMWH's benefits were noted, but substantial drawbacks were described (agency). For participants, the main goal of using LMWH was avoiding any risks in pregnancy (purpose). Side-by-side comparisons revealed concordance and discordance between health states and motives.ConclusionsMixed-methods provide a nuanced understanding of LMWH preferences, by quantifying health states preferences and exploring attributes qualitatively. Incorporating both methods may improve patient-centered care around preference-sensitive decisions in thromboprophylaxis during pregnancy.

Artificial intelligence-based expert weighted quantum picture fuzzy rough sets and recommendation system for metaverse investment decision-making priorities

There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard.

Strategy to Improve the Quality of Madrasah Education Through Madrasah Diniyah (Madin) in Min 16 Bener Meriah Aceh

The purpose of this study was to determine the concept of organizing Madrasah Diniyah (Madin) Min 16 in Aceh. The concept of Madrasah Diniyah (MADIN) at MIN 16 Bener Meriah has the following characteristics: focusing on customers, both internal and external customers, having a high obsession with quality, using a scientific approach in decision making and problem solving, having a long-term commitment, requiring teamwork, continuous process improvement, providing education and training, providing controlled freedom, unity of purpose, and involving and empowering human resources. Meanwhile, the implementation of Integrated Quality Improvement Management in Madrasah includes Quality Strategy Planning (vision, mission, objectives, long-term institutional strategy, monitoring and evaluation), improving process quality (curriculum and learning process), improving the quality of Human Resources. Resources (HR), improving environmental quality, improving service quality, improving output quality

A behavior three-way decision approach under interval-valued triangular fuzzy numbers with application to the selection of additive manufacturing composites

Additive manufacturing composites, also recognized as three-dimensional (3D) printing composites, are highly anticipated for their potential to replace industrial materials due to the availability of multiple printing processes and optional materials. However, research gaps exist in cognitive deficiencies and psychological behaviors of decision-makers, as well as experimental error effects caused by material testing, resulting in material selection as a challenging issue. Therefore, this study proposes a novel behavior three-way decision model under the interval-valued triangular fuzzy number (IVTFN) to settle the selection issue of 3D printing composites. The research contributions are summarized as follows. First, the IVTFN is presented to account for the impacts of cognitive deficiency and experimental errors, based on which the concepts of information entropy and fuzzy measure are further developed to conduct the criterion weights. In addition, by integrating the prospect theory and regret theory, a framework for constructing the behavioral decision matrix is presented. Moreover, a novel behavior three-way decision model with the perspectives of objective and preference is proposed to classify the decision region. This study presents a comprehensive methodology integrating the three-way decision model and multi-criteria decision-making method to achieve both alternative ranking and alternative classifying. Finally, a research case of 3D printing composites reinforced by continuous hybrid fibers is adopted to illustrate the validity of the methodology. Comparative analysis and sensitivity analysis are also performed. This study offers valuable insights and tools for systematically tackling the 3D printing composite material selection issues.

Beyond tradition: charting a greener future for cassava starch industry using multi-criteria decision-making

Cassava, a staple food crop, is widely used for starch production, but its inconsistent supply, price volatility, and substantial waste generation pose challenges to the cassava industrial market's growth. This study aims to identify a sustainable biorefinery pathway by optimizing conventional cassava starch plants (business-as-usual, BAU) for economic and environmental benefits. Four scenarios were evaluated: animal feed from peel waste (Scenario 1), fungal protein from thippi waste (Scenario 2), fish feed from digested wastewater (Scenario 3), and conversion of all waste streams into animal feed, fish feed, and fungal protein (Scenario 4). Scenario 4 emerged as the best pathway using the multi-criteria decision-making (MCDM) approach, with a performance score of 0.282. Despite the highest energy consumption (18.91 MWh), Scenario 4 was favored for producing four value-added products alongside starch, yielding the highest profit at USD 8.85 million. In contrast, profits for BAU, Scenario 1, Scenario 2, and Scenario 3 were 1.91, 2.30, 5.01, and USD 8.79 million, respectively. Waste valorization in Scenarios 1, 2, 3, and 4 resulted in CO2 avoidance of 36.5., 42.6., 21.7, and 57.45 t CO2eq., respectively. However, producing value-added products increased energy consumption by 13, 73, 7, and 74% compared to BAU (4.58 MWh). The global warming potential analysis showed negative values for scenarios 2 and 4, at -436 and -434 kg CO2eq./t root, respectively. This study highlights the potential of a biorefinery approach for sustainable cassava starch production, providing insights for future research and policy development.

Analyzing the role of social media in addressing public health development in india through a multi-criteria decision-making approach

Nowadays, social media plays a vital role in managing public health developments. This research study has analyzed the factors of social media enhancing public health development. The analytic hierarchy process (AHP) is used to rank the factors, and the Decision-making trial and evaluation laboratory (DEMATEL) is used to find out the network relationship map of the role of social media in public health development. Results of AHP revealed that social media enhances public engagement and participation, collaboration of health professionals worldwide, and helps in remote monitoring of patients. Results of DEMATEL show that the development of health policies, repository of public health-related information, and prevention of health-related misinformation and fake news are classified as the topmost causal factors. Real-time monitoring of health conditions is the factor with the most significant impact on other factors. Policymakers and stakeholders should focus on improving public health through social media. Social media enhances collaboration among health professionals worldwide, and various stakeholders use it to address public health issues in the case of COVID-19.

Beyond stigma: Clinician bias in contraceptive counseling to sexual and gender minority youth

ObjectivesSexual and gender minority (SGM) youth experience disparities in sexual and reproductive health; however, little is known about how clinicians engage in contraceptive counseling with this patient population. This study describes pediatric clinician patterns and biases in contraceptive counseling with SGM youth. Study DesignWe conducted 16 in-depth interviews with a convenience sample of clinicians who counsel adolescents on contraception. Participants were recruited and interviewed in-person at the American Academy of Pediatrics National Conference in October 2022. We used codebook thematic analysis. ResultsWhen discussing contraceptive counseling among SGM youth, three major themes emerged: (1) participants’ acceptance of SGM youth identities varied from support to suspicion and rejection; (2) participants’ conceptualizations of their SGM youth patients’ identities circumscribed the scope of the contraceptive care they provided; and (3) participants described using a universal approach to contraceptive counseling that disregarded the relevance of SGM youth identities. For transgender patients, many clinicians focused on menstrual regulation and overlooked potential pregnancy risk. When discussing sexual minority patients, clinicians overemphasized pregnancy prevention and encouraged the use of highly effective contraceptive methods rather than taking a shared decision-making approach to contraceptive care. ConclusionMany clinicians demonstrated bias in approaches to contraceptive care provision to SGM youth patients by holding patients accountable to normative assumptions in transgender medicine and family planning. Training and support for adolescent-facing clinicians in bias recognition and comprehensive contraceptive care are necessary to provide person-centered reproductive health care to SGM youth.

Application of GIS-Based Multi-Criteria Decision Analysis of Hydro-Geomorphological Factors for Flash Flood Susceptibility Mapping in Bangladesh

Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), NDVI, distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09 to 9275.15 sq. km.), whereas 35.34% to 38.32% (7069.70 to 7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70%) randomly selected floods were used for training, and the remaining 60 (30%) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (AUROC = 92.1%) than the ANP (AUROC = 88.5%) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.

Machine learning-based decision-making approach for predicting defects detection: a case study

In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.