Weights Of Attributes Research Articles

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Enhancing shelf-life and nutritional value of broccoli florets through vacuum impregnation with calcium chloride and ascorbic acid.

The present investigation aimed to enhance the postharvest shelf-life of broccoli using vacuum impregnation (VI). Broccoli florets were impregnated with physiologically active chemicals, i.e. calcium chloride and ascorbic acid. Post-impregnation broccoli florets were packed in three different packaging materials (poly(ethylene terephthalate) punnets, low-density polyethylene pouches and shrink-wrap film) and stored at two temperatures (5 and 25 °C). The effects of impregnation solutions, packaging materials and storage temperature on physicochemical attributes (weight loss, total soluble solids, ascorbic acid, total chlorophylls and carotenoid content), antioxidant and phenolic contents, and shelf life were studied. The changes in the chemical structure post-impregnation and during storage were investigated using Fourier transform infrared (FTIR) spectroscopy. Results showed that impregnated broccoli florets exhibited significantly (P < 0.05) higher levels of biochemical attributes immediately after impregnation. During storage, the physicochemical and antioxidant properties of broccoli florets declined for all the samples. However, the reduction in these properties was significantly (P < 0.05) lower in impregnated florets as compared to untreated control samples. Principal component analysis and FTIR results also indicated a clear difference in the impregnated and control samples. The shelf-life of broccoli florets stored at 25 °C was assessed as 4 and 3 days for impregnated and control samples, respectively; whereas the samples stored at 5 °C had a shelf-life of 12 days for impregnated samples and 5 days for the control samples. The findings of the study elucidate the potential of VI in enhancing the initial quality and shelf-life of broccoli. The deterioration during storage is primarily due to physiological weight loss, a natural loss of water and volatile compounds that occur following harvest due to transpiration and respiration. Excessive transpiration can lead to dehydration, which reduces the quality and shelf-life of the product. © 2024 Society of Chemical Industry.

A Framework for Prioritising the Performance Criteria of Natural Fibre Composite Materials: Incorporation of CRITIC-TOPSIS Method

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) method to provide a solution to assist design engineers in prioritising the right criteria in the early design process is essential. Part of the aim of this study is to establish an integration CRITIC-TOPIS for selecting the most efficient framework to choose performance criteria, namely density, tensile strength, Young’s modulus, cellulose, and elongation at break for natural fibre material intended for cap toe shoes like abaca, bamboo, coir, jute, kenaf, sisal. Hence, a new framework was proposed and tested based on integrating Criteria Importance Through Inter Criteria Correlation (CRITIC)-Technique Order Preference by Similarity to Ideal Solution (TOPSIS). Therefore, this proposed framework consists of two phases: the first involves determining the weights of attributes using the CRITIC method, and the second consists of making material criteria decisions using the TOPSIS method. Meanwhile, to achieve this objective, numerical validation was obtained using data from selected past case studies, which were then replicated to validate the output of the proposed framework. According to the validation conducted using CRITIC-TOPSIS, the results show a significant level of similarity, with the rankings being consistent. Therefore, the proposed methodology may provide imprecise and ambiguous information for prioritising the performance criteria of natural fibre composite materials. Moreover, design engineers can utilise this framework in the composite industry to create the best possible evaluation model for composite material criteria selection for various applications.

Responses of oak seedlings to increased herbivory and drought: a possible trade-off?

Anthropogenic disturbances are causing a co-occurring increase in biotic (ungulate herbivory) and abiotic (drought) stressors, threatening plant reproduction in oak-dominated ecosystems. However, we wonder whether herbivory could compensate for the adverse impact of drought by reducing evapotranspiration. Thus, we investigate the isolated and joint effects of herbivory and drought on oak seedlings of two contrasting Mediterranean species that differ in leaf habit and drought resistance. California oak seedlings from the evergreen, and more drought-resistant, Quercus agrifolia and the deciduous Q. lobata (n=387) were assigned to a fully crossed factorial design with herbivory and drought as stress factors. Seedlings were assigned in a greenhouse to 3-4 clipping levels simulating herbivory and 3-4 watering levels, depending on the species. We measured survival, growth, and leaf attributes (chlorophyll, secondary metabolites, leaf area and weight) once a month (May-Sep) and harvested above- and below-ground biomass at the end of the growing season. For both oak species, simulated herbivory enhanced seedling survival during severe drought or delayed its adverse effects, probably due to reduced transpiration resulting from herbivory-induced leaf area reduction and compensatory root growth. Seedlings from the deciduous, and less drought-resistant species, benefitted from herbivory at lower levels of water stress, suggesting different response across species. We also found complex interactions between herbivory and drought on their impact on leaf attributes. In contrast to chlorophyll content which was not affected by herbivory, anthocyanins increased with herbivory - although water stress reduced differences in anthocyanins due to herbivory. Herbivory seems to facilitate Mediterranean oak seedlings to withstand summer drought, potentially alleviating a key bottleneck in the oak recruitment process. Our study highlights the need to consider ontogenetic stages and species-specific traits in understanding complex relationships between herbivory and drought stressors for the persistence and restoration of multi-species oak savannas.

Generalized zero-sample industrial fault diagnosis with domain bias

Generalized zero-sample fault diagnosis (GZSFD) is a challenging task involving the diagnosis of all samples from both previously seen and unseen faults. However, the scarcity of unseen samples for training causes that existing methods are hindered by domain bias, where unseen faults are more likely to be misclassified as seen faults. In this article, an efficacious solution is proposed by constructing an unseen fault detector for test samples in GZSFD with domain bias, which utilizes the detected unseen-sample knowledge to enhance the diagnosis performance. Specifically, a ResNet-based one-dimensional convolutional neural network is designed for high-quality feature extraction. Also, a Kullback–Leibler divergence-based distribution threshold detector is constructed for the identification of test samples. Afterwards, test samples are detected and distinguished into seen or unseen classes. In detected unseen classes, a zero-sample fault diagnosis (ZSFD) problem is undertaken, while in detected seen classes, a sub-GZSFD problem is addressed. For ZSFD tasks, to leverage the unseen samples in the test set, a clustering-based scheme without a predefined cluster number is used for the detected unseen fault. For sub-GZSFD tasks, combined with classification results in the ZSFD task, two embedding strategies are proposed to further mitigate the domain bias. They learn a shared weight and the optimal weights of semantic attributes from the feature space to the semantic embedding space, respectively. Using the shared fine-grained semantic attribute descriptions as auxiliary information, the final fault category can be determined. Experimental results showcase that the proposed strategies effectively alleviate the domain bias in GZSFD tasks.

Classification-based consensus model considering quantum interference in linguistic distribution environment

Multi-attribute group classification (MAGC) is widely used in dealing with a variety of practical problems. However, cognitive biases, resulting from factors such as irrational behaviors, epistemic uncertainty, and interference phenomena, may critically affect the consensus reaching process (CRP) of MAGC. To eliminate such influences, this study proposes an ordinal classification consensus model in a linguistic environment based on quantum probability theory (QPT), with the aim of classifying the alternatives into several predefined ordinal classifications accepted by a majority of experts. First, the linguistic distribution assessments (LDAs), which can depict the quantitative distribution and qualitative vagueness, are used to express the uncertain preference of experts. Second, the LDAs best-worst method (BWM) and LDAs maximizing deviation model (MDM) are developed to identify the subjective and objective weight of attributes, and then these two types of attributes weight are integrated by quantum-like Bayesian networks (QLBN). Third, a maximum consensus model (MCM), maximizing the consensus level of ordinal classification results of experts, is provided to determine the parameters of QLBN. When the consensus level is lower than the predetermined threshold, a minimum adjustment distance model (MADM) is designed to guide the preference-modification of experts to derive the consensus classification of alternatives. Finally, a risk assessment case study is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis and comparison analysis are performed to verify the feasibility and effectiveness of the proposed method

The use of Supervised Learning to perform pairwise classification for Record Linkage over real world data

Supervised Learning (SL) is a subset of Machine Learning that uses labeled data to establish the learning process and perform tasks such as classification and regression. Record Linkage (RL) is the process for entity resolution and aims to determine if two records correspond to the same entity in the real world. A challenging aspect of RL involves estimating probabilities and weights for attributes and choosing the algorithm to perform the comparison. Our study aims to address this challenge and contribute in two primary ways: 1) by providing a labeled dataset comprising pairs of records, and 2) by evaluating an alternative classification method using an SL classifier. We generated more than 28.000 pairs, manually reviewed, derived from a massive Record Linkage Process on the Brazilian Public Health System (SUS). From that, we selected 5000 random stratified pairs for evaluation. The data contains demographic attributes such as sex, city, date of birth, zip code, and name. We modeled the data pairwise in a vector of latent space. Our methodology involved comparing Random Forest, LightGBM, XGBoost, and Neural Networks for classification tasks on these pairs, utilizing cross-validation to optimize hyperparameters. Our findings reveal that XGBoost had the highest performance, achieving 96% accuracy. Additionally, we conducted privacy evaluations using bloom filters, yielding an accuracy of 93%. The adoption of SL presents a promising space due to its capability for weight estimation and classification. In future endeavors, integrating these algorithms into a framework for the Record Linkage process could streamline procedures for researchers without compromising performance.

Impact of goat manure application on growth of wild watermelon under shade house cultivation

The continuous use of inorganic fertilizers in agriculture poses detrimental effects on soil health and future crop yields. Therefore, alternative sources of plant nutrients that are environmentally friendly and cost-effective for use in crop production, especially with the escalating cost of inorganic fertilizers, are necessary. A shade-house study was designed to determine the effect of different quantities of goat manure applications on growth attributes and yield of wild watermelon (Citrullus lanatus subsp.). A randomized complete block design (RCBD) was used to arrange the treatments, with fourteen (14) replications (n = 84). Six (6) treatments consisting of various quantities (v/v) of river sand, Hutton soil, and goat manure (T0 -1:3:0, T1 -1:3:1, T2 -1:3:2, T3 -1:3:3, T4 -1:3:4, and T5 -1:3:3) were used, with T0 -1:3:0 constituting the control. Treatments had a highly significant (P ≤ 0.01) effect on dry shoot mass (65%), fruit weight (75%), and fruit diameter (75%) except for the dry root mass (44%), vine length (47%), chlorophyll (47%), number of fruit (27%) and stem diameter (22%) of wild watermelon plants. Dry shoot mass was the highest (11.14 g) in T4 -1:3:4, and the lowest dry shoot mass (6.57 g) was obtained in T1 -1:3:1 when compared to the control. The fruit fresh weight of the wild watermelon was reduced in all the treatments, when compared to the control. The biggest fruit diameter (66.66 mm) was observed in T4 -1:3:4, and the lowest (14.4 mm) was observed in T5 -1:3:5. In conclusion, T4 -1:3:4 enhanced greater vegetative growth (dry shoot matter) and yield attributes (fruit diameter and weight) of the wild watermelon.

Patient preferences for inflammatory bowel disease treatments: protocol development of a global preference survey using a discrete choice experiment.

As the therapeutic landscape for inflammatory bowel disease (IBD) continues to expand, a need exists to understand how patients perceive and value different attributes associated with their disease as well as with current and emerging treatments. These insights can inform the development and regulation of effective interventions for IBD, benefiting various stakeholders including healthcare professionals, drug developers, regulators, Health Technology Assessment bodies, payers, and ultimately patients suffering from IBD. In response to this, the present patient preference study was developed with the aim to (1) determine the relative preference weights for IBD treatment and disease related attributes, and (2) explain how preferences may differ across patients with different characteristics (preference heterogeneity). The patient preference study (PPS) was developed through an 8-step process, with each step being informed by an advisory board. This process included: (1) stated preference method selection, (2) attribute and level development (including a scoping literature review, focus group discussions, and advisory board meetings), (3) choice task construction, (4) sample size estimation, (5) survey implementation, (6) piloting, (7) translation, and (8) pre-testing. The resulting discrete choice experiment (DCE) survey comprises 14 attributes with between two and five varying levels. Participants will answer 15 DCE questions with a partial profile design, where each of the choice questions encompasses two hypothetical treatment profiles showing four attributes. Additionally, questions about patients' socio-demographic and clinical characteristics, as well as contextual factors are implemented. The survey is available in 15 different languages and aims to minimally recruit 700 patients globally. This protocol gives valuable insights toward preference researchers and decision-makers on how PPS design can be transparently reported, demonstrating solutions to remaining gaps in preference research. Results of the PPS will provide evidence regarding the disease and treatment related characteristics that are most important for IBD patients, and how these may differ across patients with different characteristics. These findings will yield valuable insights applicable to preference research, drug development, regulatory approval, and reimbursement processes, enabling decision making across the medicinal product life cycle that is aligned with the true needs of IBD patients.

Developing a Methodology to Assess Pedestrian Walkability in the City Markets Using Multi-Attribute Decision-Making Techniques

City markets are one of the busiest urban areas in India and experience heavy pedestrian footfall. This study uses multi-attribute decision-making (MADM) techniques to rank market roads based on the existing condition of pedestrian infrastructure. A set of seven key attributes was identified containing 28 sub-attributes influencing pedestrian walkability. Videography and photographic surveys were conducted at selected roads of Aminabad Market (AM) in Lucknow, and Silchar Market (SM) in Silchar. Attribute weights were calculated using the entropy weight method, and the roads were ranked using the analytical hierarchy process, the technique for order of preference by similarity to ideal solution, and a novel walkability metric called the Market Walkability Index. Spearman’s rank correlation coefficient was found to be more than 0.9 among all three methods, indicating conformity in ranking. Internal or minor roads of both the market areas (AM and SM) were observed to be the most used roads of the market areas for pedestrian movement. Still, they were not safe and walkable for pedestrian movement, indicating that more emphasis is required on these roads to improve the overall quality of the pedestrian walking environment. Research findings are expected to be essential inputs for rational decision-making about pedestrian infrastructure improvement in developing countries, which could contribute to a safer and more efficient environment for pedestrian movement.

A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

Emergency Material Prediction in South China Based on Case Analysis Method

After the occurrence of flood disasters, timely and rapid emergency response is one of the important tasks to improve the efficiency of emergency rescue and reduce disaster losses, and the prediction of material demand, which is the premise and basis of emergency response in disaster areas after disasters, is one of the key problems that need to be solved urgently. Considering the characteristics of poor information in the post-disaster disaster area, a post-disaster material demand forecasting technology based on fuzzy case reasoning was introduced. Firstly, on the basis of the analysis and summary of the existing case database, the key feature attributes of flood disasters that affect the demand for materials after the disaster are extracted, and the fuzzy set of flood disaster characteristics is established by introducing the concept of fuzzy sets, and then the membership degree of the specific feature attribute values of the new and old cases to the fuzzy set is calculated, in order to measure the similarity between the new and old cases, and the corrected measure closeness based on the weight of the feature attributes of the new and old cases is calculated, and the one with the largest closeness represents the best match between the new and old cases. Finally, an actual case is used to show the specific application process of the technology, and the existing reference cases that are closest to the new case are obtained, which can provide reference for post-disaster emergency rescue. The results show that the proposed method can provide a scientific prediction of the demand for emergency supplies and provide a basis for decision-making for the relevant emergency management departments in an actual flood disaster case in South China.

A PSO-algorithm-based dual consensus method for large-scale group decision making and its application in medical imaging equipment purchasing

The increasing complexity of social activities requires an expanding number of people to be involved in decision-making, so that the large-scale multi-attribute group decision-making problems have gained widespread attention. After overviewing the researches of large-scale multi-attribute group decision-making methods, we have found that: The existing methods only consider consensus based on evaluation information of alternatives, while ignoring the consensus on importance of attributes. Thus, in order to tackle the issues and describe the hesitancy of decision makers in the decision-making process, this paper proposes a novel dual consensus method in large-scale multi-attribute group decision making under hesitant fuzzy linguistic environment. In the consensus reaching process, the method considers not only the consensus on the evaluation information of alternatives, but also the consensus on the importance of attributes, where both consensus reaching processes are implemented by the particle swarm optimization algorithm. The subjective weights of attributes are derived from the consensus reaching process of preference matrices, and the objective weights of attributes are obtained from the consensus reaching process of decision matrices, so as to acquire the comprehensive weights. After that, the overall ranking of alternatives can be obtained by TODIM. Finally, the proposed method is applied to a case study of medical imaging equipment purchasing decision-making, and the comprehensive analysis is provided to clarify advantages of the proposed method.

Light but fruitful: enhanced fuzzy inference via weight-guided selection of rules with attribute weights

Inference using fuzzy rules enables decision-making that is supported with imprecise knowledge. Unlike conventional fuzzy reasoning approaches which directly perform pattern-matching in response to an input observation, recent techniques have integrated rule-firing-based and rule interpolating-based inference methods. This is in order to address challenging issues where observations are of different matching degrees to the rules within a given rule base, including unmatched ones. While applied generally, such a unified inference mechanism may become too complex to exploit the entire rule base for deriving a reasonable conclusion. In practice, only a small number of ‘appropriate’ rules are selected to accomplish the required inference. This paper presents an enhanced integrated fuzzy inference mechanism, which is fed with fewer rules returned by a weight-guided selection procedure. In particular, the weights of rule attributes are utilised in a dual manner: guiding the selection of appropriate rules for rule firing and determining the nearest neighbouring rules for rule interpolation. The resulting mechanism is applied to a real-world problem, empirically demonstrating its significant efficacy.

PDHL-MARCOS method based on cumulative prospect theory for MAGDM and its application in network security evaluation

The increasingly large and complex network brings convenience to our life and production, but it also brings potential dangers. Any minor vulnerability may lead to failure of application software configuration, software maintenance, user management and system management. Eventually, our network is interrupted, and even life and production data are leaked. Therefore, establishing a scientific and effective network security evaluation model is an effective way to supervise, manage and improve network security; It is also an effective measure to reduce the loss of life and production. Therefore, we first selected six factors closely related to network security to form a complete network security evaluation index system. Then, the utility function in the traditional measurement of alternatives and ranking according to compromise solution (MARCOS) method is improved by using the value function in cumulative prospect theory (CPT), and a PDHL-MARCOS evaluation model that can reflect the decision-makers’ (DMs’) attitudes towards gains and losses is obtained in the probabilistic double hierarchy linguistic term set (PDHLTS). In addition, the relative entropy (RE) method is also used to obtain expert weights. The combination weights of attributes are obtained using the correlation coefficient and the standard deviation (CCSD) method and the weight function in CPT.

Multi-attribute quantum group interference decision model considering minimum constraint and information entropy

About multi-attribute group decision making process, the opinions of different decisions will interfere with each other, or be affected by the decision-making environment, resulting in an unobserved interference effect, which is regarded as quantum entanglement in quantum theory. In this paper, an interference effect model based on minimum constraint and information entropy is proposed to measure the interference value in quantum decision models. Firstly, considering the interference effects of decision makers’ opinions, the evaluation opinions of decision makers are aggregated using Quantum-like Bayesian Network (QLBN), which are regarded as wave functions superimposed in the quantum model. Secondly, due to the uncertainty of decision process, information entropy and minimum constraint are combined to solve the interference effect. The minimum constraint is used to limit the range of interference values and the information entropy represents the uncertainty in the decision process. The grey relational degree represents the conditional probability of the decision maker about the alternatives. Thirdly, the weights of attributes and the probabilities of the alternatives are aggregated about decision makers by the proposed interference effect model, and then the alternatives are ranked by the aggregated probabilities. Subsequently, a numerical example is used to verify the validity and rationality of the model. Comparative analyses and sensitivity analyses are also implemented to demonstrate the stability and reliability of our method.

Effect of aloe-vera coating on the quality of mechanically damaged zucchini during long-term storage

Zucchini is susceptible to mechanical damage, which can occur during postharvest operations and leads to produce quality reduction. Bruising is the most common type of mechanical damage that could result in economic losses due to its effect on the appearance and shelf life of produce. This study aims to examine the effect of bruising and coating on the quality parameters of zucchini. Zucchini was bruised through the drop impact test which produced mechanical damage with an impact energy of 0.353 J. Other zucchini samples were not bruised and set as control. Half of the samples were coated with aloe vera gel. The samples were kept at 5, 10, and 22 °C for 24 days. The effect of impact bruising, storage conditions, and coating on the bruising size and other quality attributes (weight, color, firmness, pH, and total soluble solids) was investigated. The results showed that bruising parameters were dependent on storage duration and coating. At all tested storage conditions, coating with aloe vera helped regulate the weight and yellowness changes in zucchini samples during the storage period. Storage at 10 °C showed the least reduction in weight on all treated and control samples compared to other storage conditions. The redness and greenness (a* value) color changes in all tested zucchini samples were steadily increasing at 22 °C. Coated, non-bruised zucchinis showed a gradual increment in total soluble solids and were highly maintained at 10 °C storage condition. A reduction in the firmness of zucchini was significantly observed due to storage duration. Meanwhile, pH increased but was not significantly affected by any of the tested factors. Moreover, this study indicated the importance of both aloe vera coating and storage management in preserving zucchini quality parameters and extending its shelf life. Hence, coating zucchini should be considered when attempting to increase its shelf life. Moreover, the findings of the study can provide baseline data to understand the mechanism of mechanical damage on the quality of zucchini.

GHF-COPRAS Multiple Attribute Decision-Making Method Based on Cumulative Prospect Theory and Its Application to Enterprise Digital Asset Valuation

With the rapid development of the economy, data have become a new production factor and strategic asset, enhancing efficiency and energy for technological innovation and industrial upgrading in enterprises. The evaluation of enterprise digital asset value (EDAV) is a typical multi-attribute decision-making (MADM) problem. Generalized hesitant fuzzy numbers (GHFNs) can better express the uncertainty and fuzziness of evaluation indexes, thus finding wide applications in MADM problems. In this paper, we first propose the Kullback–Leibler (K-L) divergence distance of GHFNs and prove its mathematical properties. Second, recognizing that decision-makers often have finite rationality in practical problems, we combine the cumulative prospect theory (CPT) with the Complex Proportional Assessment (COPRAS) method to propose the GHF-CPT-COPRAS model for solving MADM problems. Simultaneously, we extend the distance correlation-based Criteria Importance Through Intercriteria Correlation (D-CRITIC) method to the GHF environment to rationally calculate the weights of attributes in the EDAV evaluation problem. Finally, we apply the proposed GHF-CPT-COPRAS model to the EDAV evaluation problem and compare it with existing GHF decision-making methods to verify its effectiveness and feasibility. This study provides an important reference for addressing the EDAV assessment problem within an uncertain fuzzy environment and extends its application methods in the decision-making field.

Evaluation of agile attributes for low-cost carriers to achieve sustainable development using an integrated MCDM approach

PurposeSince conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.Design/methodology/approachTwo well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.FindingsWhile the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.Practical implicationsThe findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.Originality/valueTo the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

Hedonic analysis of attributes and factors affecting small ruminant price in Benin, West Africa

The demand for small ruminants is increasing, but livestock production is not very market oriented. Understanding buyers' preferences allows breeders to orient their production and make more profit. This research aimed to determine the attributes of small ruminants and other specific factors that explain price variations of small ruminants in ordinary period. Twenty-one markets were selected using stratified sampling. Data were collected in these markets on September 2020 from 432 small ruminant transactions between sellers and buyers and analysed with the hedonic regression model. Results showed that the price of goats purchased for slaughter and breeding were respectively 9.4 % and 7.2 % higher than those bought for trade. At the same time, when sheep were purchased for breeding, consumption and catering, their prices were respectively 14.5 %, 10.3 % and 9.4 % higher than those purchased for trade. The attributes weight, age (2–4 years old), sex (female), coat colour (white), body condition (good), breed (sahelian breeds for sheep and djallonke breed for goats) and absence of horn determined significantly and positively the small ruminant's price. In addition to these attributes, the small ruminant prices varied according to type of market and seller, but the buyer had less control over these factors. Thus, it is important to improve attributes preferred by buyers through training for breeders on the control of small ruminant reproduction. These attributes should also be considered when developing strategies to improve the small ruminant sector to transform traditional livestock farming into market-oriented livestock farming. Facilitating access to information through the establishment of functional market information systems will enable breeders to move towards market-oriented livestock systems.