Effects of Recession on Asset Management Performance in Small Businesses in Spain

The number of studies that assess the level of maintenance in a country is still very small, despite the contribution of this area to national competitiveness. The literature analyses asset management based on key performance indicators, but not via a multicriteria model. This chapter describes a multicriteria model, constructed by means of the fuzzy analytic hierarchy process (FAHP). The weightings are converted into utility functions, allowing the final utility of an alternative to be calculated via a multi-attribute utility function. Data on the state of asset management in Spain, in 2005 and 2010, are used to produce discrete probability distributions. Finally, a Monte Carlo simulation is applied to estimate the uncertainty of a complex function. In this way, the level of excellence of asset management in small businesses in Spain, before and after the recession, could be determined. The results show that the economic crisis experienced in Spain since 2008 has had a negative effect on the level of asset management in most sectors.

EVALUATION OF THE PLACES FOR CREATION OF APIARIES AND OPTIMAL DISTRIBUTION OF THE BEE COLONIES

An approach for evaluation of the places for creation of apiaries and optimal distribution of bee colonies formed on the basis of the feeding capacities of the areas with flowering plants, the distances between these sites and the feeding areas is proposed. A multicriteria model with two main criteria is considered. The first maximizes the sum of the products of the weights for a given place multiplied by the number of colonies that will be positioned at that place. This criterion is divided into two sub-criteria, including the ‘subjective’ and ‘objective’ assessment of place preferences, respectively. The second criterion aims to minimize malnourished bee colonies. The model, with the proposed approach for ‘objective’ assessment of potential distribution sites, can be applied both for cases without overpopulation of the area with bee colonies and for areas with overpopulation.

Socioeconomic Impact of Climate Change in Rural Areas of Greece Using a Multicriteria Decision-Making Model

Agriculture is the main and, in some cases, the only, source of income and employment in rural areas. The change in the conditions under which agriculture is practiced has various effects on the agricultural economy but also on the social structure of rural areas. Climate change has multiple effects on agricultural production, necessitating the reorganization of agricultural production in some cases. These effects of climate change will also impact the economic and social aspects of farms in rural areas. This paper attempts to identify these effects by measuring the socioeconomic impacts of climate change in the region of Central Macedonia in Greece. For this reason, a multicriteria model was developed to simulate these impacts by estimating a set of seven social and economic indicators. The model was implemented to the average farm which was estimated from the main cultivations of the region. A scenario analysis was also used in combination with the multicriteria model. The multicriteria model suggests modifications are needed in the average farm crop plan of the region as a result of the climate change impact. The scenarios results show that climate change will negatively affect all the social and economic indicators and will continue to affect them over the years. These results can be used by policymakers to understand the economic and social impacts of climate change in the region to plan their future policies.

Multicriteria Model Proposition to Support the Management of Systems of E-Waste Collection

Background: Considering the global concern in balancing economic growth with environmental sustainability, the study proposes a model to support multicriteria decision-making. From the systematic literature review and bibliometric analysis, there was an increasing trend in studies on electronic waste due to governments, stakeholders, and the population to better address the management of this waste; Methods: We propose a decision model considering some aspects and phases that help from collecting information to support decision making, based on the FITradeoff ordering method, to support policy decisions for managing Waste from Electrical and Electronic Equipment (WEEE) collection systems.; Results: After applying the proposed model, validated based on the perception of a decision-maker working in a federal public agency, we obtained the final classification with ten positions of alternatives; Conclusions: This outcome can assist in decision making and management of the collection of WEEE. In addition, we made recommendations to manufacturers have more responsibility in the design and traceability of the product to guarantee its recovery after disposal effectively.

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis, psoriasis, or skin lesions. We collected a database of 631 images with healthy skin and different skin pathologies to train and assess all stages of the methodology. The proposed framework starts with the segmentation of the epidermal layer using a DeepLab v3+ model with a pre-trained Xception backbone. We employ transfer learning to train the segmentation model for two purposes: to extract the region of interest for classification and to prepare the skin layer map for classification confidence estimation. For classification, we train five models in different input data modes and data augmentation setups. We also introduce a classification confidence level to evaluate the deep model’s reliability. The measure combines our skin layer map with the heatmap produced by the Grad-CAM technique designed to indicate image regions used by the deep model to make a classification decision. Moreover, we propose a multicriteria model evaluation measure to select the optimal model in terms of classification accuracy, confidence, and test dataset size. The experiments described in the paper show that the DenseNet-201 model fed with the extracted region of interest produces the most reliable and accurate results.

A multicriteria model for evaluation of Brazilian wineries from a tourism destination perspective

Purpose This paper aims to propose a multicriteria model for the evaluation of tourist service quality in Brazilian wineries from a tourism perspective. Design/methodology/approach The model is comprising two phases: structure of the problem and application of the method. First, the selection of wineries in a given region, the identification of decision-makers that will perform the evaluation according to a set of 19 criteria based on the Tourqual protocol and the construction of the evaluation matrix in the next phase. Then, the analytic hierarchy process (AHP) method is applied and a rank of wineries is provided. Findings The model is applied to evaluate the seven most important wineries in South Brazil and the results provided by the AHP method, considering the categories of Tourqual protocol, are consistent with the opinion of specialists in wine tourism. Research limitations/implications The model needs to be applied to other case studies to evaluate the consistency of the results and their acceptability by the tourism sector. Practical implications The model has the potential to be applied as a formal tool for evaluation of wineries, support decision-making processes in different wine tourism management structures: private wine and tourism organizations; public managers of tourism activity and managers of governance structures. Originality/value This paper presents a novel AHP-based model for evaluation of service quality in the winery’s tourism domain, an empirical application of the model for evaluation of wineries in one of the most important regions that produce grapes and wine in South America.

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.