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This research investigates the optimal integration of Blockchain Technology (BT) in Supply Chain Management (SCM) within Chile's maritime ports. Utilizing fuzzy Logarithmic Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation Methods (DNMA), the study systematically identifies, prioritizes, and ranks key factors influencing BT adoption in SCM. The study's findings highlight crucial factors like enhanced transaction security, good supply chain practices, and risk management. Furthermore, it ranks the application of ports as prime candidates for BT integration. The research contributes theoretically by developing a hybrid model combining MCDA methods, and practically by guiding the strategic application of BT in the maritime logistics sector, aligning with the principles of Industry 5.0. This paper presents a novel approach that explores the utilization of BT in maritime supply chain management, incorporating MCDA in a vague environment. The research gap of this study lies in defining new contexts in both theoretical and practical literature reviews for extending the use of BT in SCM in the ports of Chile, according to Industry 5.0, to increase the efficiency and effectiveness of all aspects of operations in these places. The contribution of this research is applying hybrid MCDA methods in an uncertain environment to assist decision-makers (DMs) in better implementing BT in SCM in Chilean ports, according to Industry 5.0. [ABSTRACT FROM AUTHOR]

The funa is a prevalent concept in Chile that aims to expose a person's bad behavior, punish the aggressor publicly, and warn the community about it. Despite its massive use on the social networks of Chilean society, the real dissemination of funas among communities is unknown. In this paper, we extract, generate, analyze, and compare the Twitter social network's spread of three tweets related to "funas" against three other trending topics, through the analysis of global network characteristics over time (degree distribution, clustering coefficient, hop plot, and betweenness centrality). As observed, funas have a specific behavior, and they disseminate as quickly as a common tweet or more quickly; however, they spread thanks to several network users, generating a cohesive group. [ABSTRACT FROM AUTHOR]

The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup. [ABSTRACT FROM AUTHOR]

In Chile and the world, the supply of medical hours to provide care has been reduced due to the health crisis caused by COVID-19. As of December 2021, the outlook has been critical in Chile, both in medical and surgical care, where 1.7 million people wait for care, and the wait for surgery has risen from 348 to 525 days on average. This occurs mainly when the demand for care exceeds the supply available in the public system, which has caused serious problems in patients who will remain on hold and health teams have implemented management measures through prioritization measures so that patients are treated on time. In this paper, we propose a methodology to work in net for predicting the prioritization of patients on surgical waiting lists (SWL) embodied with a machine learning scheme for a high complexity hospital (HCH) in Chile. That is linked to the risk of each waiting patient. The work presents the following contributions; The first contribution is a network method that predicts the priority order of anonymous patients entering the SWL. The second contribution is a dynamic quantification of the risk of waiting patients. The third contribution is a patient selection protocol based on a dynamic update of the SWL based on the components of prioritization, risk, and clinical criteria. The optimization of the process was measured by a simulation of the total times of the system in HCH. The prioritization strategy proposed savings of medical hours allowing 20% additional surgeries to be performed, thus reducing SWL by 10%. The risk of waiting patients could drop by up to 8% annually. We hope to implement this methodology in real health care units. [ABSTRACT FROM AUTHOR]

In this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is carried out by applying maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to assess the performance of ML estimates. To illustrate the usefulness and applicability of EHPE distribution, two real applications to COVID-19 data in Chile are discussed. [ABSTRACT FROM AUTHOR]

Stock markets foster economic growth through meeting the fund requirements of the firms by individual and institutional investors. Pension funds and insurance companies with their long-term investment horizon are critical institutional investors in capital markets. Therefore, this article explores the effect of pension funds and insurance companies on stock market development in 15 emerging market economies over the 2004–2019 period through panel cointegration and causality tests. The causality analysis revealed that stock market development had a significant impact on pension funds and the insurance sector in the short term. However, the cointegration analysis revealed that pension funds had a positive effect on stock market development in Brazil, Chile, Hungary, Mexico, Peru, and South Africa and the insurance sector had a positive impact on stock market development in Chile, Indonesia, Korea Republic, Philippines, and South Africa in the long term. [ABSTRACT FROM AUTHOR]

In the present paper, we review the use of two-state, Generalized Auto Regressive Conditionally Heteroskedastic Markovian stochastic processes (MS-GARCH). These show the quantitative model of an active stock trading algorithm in the three main Latin-American stock markets (Brazil, Chile, and Mexico). By backtesting the performance of a U.S. dollar based investor, we found that the use of the Gaussian MS-GARCH leads, in the Brazilian market, to a better performance against a buy and hold strategy (BH). In addition, we found that the use of t-Student MS-ARCH models is preferable in the Chilean market. Lastly, in the Mexican case, we found that is better to use Gaussian time-fixed variance MS models. Their use leads to the best overall performance than the BH portfolio. Our results are of use for practitioners by the fact that MS-GARCH models could be part of quantitative and computer algorithms for active trading in these three stock markets. [ABSTRACT FROM AUTHOR]

This study contributes to the debate on accessibility of higher education in Chile, with a special focus on the geospatial dimension of access to university studies. This paper addresses the central question of whether geography (physical distance and neighborhood effects) plays a significant role in determining the accessibility of higher education to students in Chile. We use Heckman probit-type (Heckit) models to adjust for selection during application for higher education—that is, pre-selection among applications to study at university and, ultimately, admission (or denial) to a higher educational institution. Of all high school graduates who took the university selection test (PSU), only 37.9% were able to attend higher education. The results show that the geospatial elements—neighborhood characteristics and distance from the city of Santiago—have a significant local effect on the student's application and access to Chilean universities. Specifically, the most significant local range for each candidate is 300 neighbors. We also find that, when distance to the capital city increases, the probability of applying to university increases to a threshold of 1400 km, at which point probability begins to decrease. [ABSTRACT FROM AUTHOR]

Contagion has been one of the most widely studied and challenging problems in recent economic research. This paper aims at capturing the main impact of contagion risk of the U.S. on foreign direct investment inflows in 18 emerging countries. To quantify the degree of contagion, the time-varying tail dependence copula is employed. Then, the Granger causality test and time series regression analysis are used to investigate the temporal and contemporaneous effects of contagion risk on investment inflows, respectively. Overall, the results confirm the time-varying contagion effects of the U.S. economy on 18 emerging economies. The size of contagion effects gradually increases for all countries, except Thailand, the Philippines, Argentina, and Chile. Furthermore, the results of the Granger causality test and regression reveal that temporal and contemporaneous effects of contagion risk on investment inflows exist in 8 out of 18 countries. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on models that utilize the Kumaraswamy distribution to describe continuous variables with bounded support. In this study, we examine the trapezoidal Kumaraswamy model. Our objective is to propose a parameter estimation method for this model using the stochastic expectation maximization algorithm, which effectively tackles the challenges commonly encountered in the traditional expectation maximization algorithm. We then apply our results to the modeling of daily COVID-19 cases in Chile. [ABSTRACT FROM AUTHOR]

In recent years, a wide range of techniques has been developed to predict electoral results and to measure the influence of different factors in these results. In this paper, we analyze the influence of the political profile of candidates (characterized by personal and political features) and their campaign effort (characterized by electoral expenditure and by territorial deployment strategies retrieved from social networks activity) on the electoral results. This analysis is carried out by using three of the most frequent data analyitcs algorithms in the literature. For our analysis, we consider the 2017 Parliamentary elections in Chile, which are the first elections after a major reform of the electoral system, that encompassed a transition from a binomial to a proportional system, a modification of the districts' structure, an increase in the number of seats, and the requirement of gender parity in the lists of the different coalitions. The obtained results reveal that, regardless of the political coalition, the electoral experience of candidates, in particular in the same seat they are running for (even when the corresponding district is modified), is by large the most influential factor to explain the electoral results. However, the attained results show that the influence of other features, such as campaign expenditures, depends on the political coalition. Additionally, by means of a simulation procedure, we show how different levels of territorial deployment efforts might impact on the results of candidates. This procedure could be used by parties and coalitions when planning their campaign strategies. [ABSTRACT FROM AUTHOR]

Improving air quality is an important environmental challenge of our time. Chile currently has one of the most stable and emerging economies in Latin America, where human impact on natural resources and air quality does not go unperceived. Santiago, the capital of Chile, is one of the cities in which particulate matter (PM) levels exceed national and international limits. Its location and climate cause critical conditions for human health when interaction with anthropogenic emissions is present. In this paper, we propose a predictive model based on bivariate regression to estimate PM levels, related to PM2.5 and PM10, simultaneously. Birnbaum-Saunders distributions are used in the joint modeling of real-world PM2.5 and PM10 data by considering as covariates some relevant meteorological variables employed in similar studies. The Mahalanobis distance is utilized to assess bivariate outliers and to detect suitability of the distributional assumption. In addition, we use the local influence technique for analyzing the impact of a perturbation on the overall estimation of model parameters. In the predictions, we check the categorization for the observed and predicted cases of the model according to the primary air quality regulations for PM. [ABSTRACT FROM AUTHOR]

In this work, we propose a local search-based strategy to determine high-quality allocation of vaccines under restricted budgets and time periods. For this, disease spread is modeled as a SEAIR pandemic model. Subgroups are used to understand and evaluate movement restrictions and their effect on interactions between geographical divisions. A tabu search heuristic method is used to determine the number of vaccines and the groups to allocate them in each time period, minimizing the maximum number of infected people at the same time and the total infected population. Available data for COVID-19 daily cases was used to adjust the parameters of the SEAIR models in four study cases: Austria, Belgium, Denmark, and Chile. From these, we can analyze how different vaccination schemes are more beneficial for the population as a whole based on different reproduction numbers, interaction levels, and the availability of resources in each study case. Moreover, from these experiments, a strong relationship between the defined objectives is noticed. [ABSTRACT FROM AUTHOR]

A physical asset's health is the consequence of a series of factors, ranging from the characteristics of the location where it operates to the care it is submitted to. These characteristics can influence the durability or the horizon of the useful life of any equipment, as well as determine its operational performance and its failure rates in the future. Therefore, the assessment of the influence of asset health on Life Cycle Costs is a compelling need. This paper proposes the incorporation of a factor that reflects the projected behavior of an asset's health index into its corresponding Life Cycle Costing (LCC) model. This allows cost estimates to be made more realistic and LCC models to be operated more accurately. As a way of validating this proposal, a case study is shown. The methodology proposed in this case study was applied in a real case, considering an LNG facility located in central Chile. In addition, sensitivity studies and comparisons with the results obtained by a traditional Life Cycle Costing model are included. The results show the usefulness of incorporating asset health aspects into the Life Cycle Costing of physical assets. [ABSTRACT FROM AUTHOR]

In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables of the TD that allow estimating different potential failure mechanisms (PFM): seismic liquefaction, slope instability, static liquefaction, overtopping, and piping, which may occur in this type of tailings storage facilities in a seismically active country such as Chile. Thus, this article proposes the use of four machine learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural networks (ANN), and extreme gradient boosting (XGBoost), to estimate five possible PFM. In addition, due to the scarcity of data to train the algorithms, the use of generative adversarial networks (GAN) is proposed to create synthetic data and increase the database used. Therefore, the novelty of this article consists in estimating the PFM for TD and generating synthetic data through the GAN. The results show that, when using the GAN, the result obtained by the ML models increases the F1-score metric by 30 percentage points, obtaining results of 97.4%, 96.3%, 96.7%, and 97.3% for RF, SVM, ANN, and XGBoost, respectively. [ABSTRACT FROM AUTHOR]

Biological products utilization are increasingly encouraged in different sectors such as building construction to facilitate moving towards a circular economy. However, this task is facing several barriers in supply chain and construction sectors. This study identified common barriers in converting agricultural residues to building materials and products in the agriculture sector, transportation, and manufacturing, as well as construction and operation phases in the building sector. The feasibility level to overcome the barriers has been scored. In addition, the barriers and sectors have been prioritized through ordinal priority approach. The results ranked the priority of the barriers as technology (0.3083), policy (0.2211), knowledge (0.1972), cost (0.1500), social and cultural (0.0739), and infrastructure (0.0494). Sectors were ranked in feasibility level to overcome the barriers from lowest to highest as operating, construction, manufacturing, transport, and agriculture. It is recommended to local communities to give priority to the building sector rather than supply chain and work under an integrated framework to enhance the feasibility level, which should include localization, prevention, collaboration, and digitalization. In particular, Chile should promote converting agricultural residues to building products as the project aligns with several initiatives existing in its circular economy roadmap. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic has intensified the strain on medical care processes, especially waiting lists for patients under medical management. In Chile, the pandemic has caused an increase of 52,000 people waiting for care. For this reason, a high-complexity hospital (HCH) in Chile devised a decision support system (DSS) based on multi-criteria decision-making (MCDM), which combines management criteria, such as critical events, with clinical variables that allow prioritizing the population of chronic patients on the waiting list. The tool includes four methodological contributions: (1) pattern recognition through the analysis of anonymous patient data that allows critical patients to be characterized; (2) a score of the critical events suffered by the patients; (3) a score based on clinical criteria; and (4) a dynamic–hybrid methodology for patient selection that links critical events with clinical criteria and with the risk levels of patients on the waiting list. The methodology allowed to (1) characterize the most critical patients and triple the evaluation of medical records; (2) save medical hours during the prioritization process; (3) reduce the risk levels of patients on the waiting list; and (4) reduce the critical events in the first month of implementation, which could have been caused by the DSS and medical decision-making. This strategy was effective (even during a pandemic period). [ABSTRACT FROM AUTHOR]

Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently monitored by the health team.. In fact, and according to the Chilean Ministry of Health (MINSAL), 7 out of 10 chronic patients have suspended their medical check-ups, generating critical situations, such as a more significant number of visits to emergency units, expired prescriptions, and a higher incidence in hospitalization rates. For this problem, health services in Chile have had to reschedule their scarce medical resources to provide care in all health processes. One element that has been considered is caring through telemedicine and patient prioritization. In the latter case, the aim was to provide timely care to those critical patients with high severity and who require immediate clinical attention. For this reason, in this work, we present the following methodological contributions: first, an unsupervised algorithm that analyzes information from anonymous patients to classify them according to priority levels; and second, rules that allow health teams to understand which variable(s) determine the classification of patients. The results of the proposed methodology allow classifying new patients with 99.96% certainty using a three-level decision tree and five classification rules. [ABSTRACT FROM AUTHOR]

This study aims to help managers develop a proper strategy and policy for their company's future. After the global COVID-19 pandemic, developed countries decided to change their production and relocate and re-industrialize. The U.S.'s big electronics and automobile companies are not an exception to this rule. The lithium batteries are the main instrument of mobile phone and electric vehicles. The leading lithium battery supplier for the U.S mobile phone companies is China. Argentina, Bolivia, and Chile (in South America) have some of the largest lithium mines in the world; these countries are known as the lithium triangle. Among the 86 million tonnes of lithium resources worldwide, 49.9 million tonnes exist in this area. The researchers in this study surveyed the best country for constructing a battery for companies in the U.S. Because of the growth of electric vehicles and their use of the lithium battery, the world is facing astronomical prices for lithium. To emphasize this issue and help managers create good policy, this study combined multiple methods. The improved suitability-feasibility-acceptability (SFA) strategy is integrated with the Bayesian best-worst method (BBWM) and measurement of alternatives and rankings according to compromise solution (MARCOS) multicriteria methods to determine the best destination. For comparison, based on the SFA strategy, seven criteria are introduced: commercially viable reserves, national minimum wage, corporate income tax, accessibility to mining companies, accessibility to the waterway, population, and political stability index. The Bayesian BWM analysis reveals that the foremost factor is corporate income tax, whereas MARCOS's findings indicate that Chile is the best country to construct the lithium battery industry. To verify the proposed approach, a comparison analysis also is performed. [ABSTRACT FROM AUTHOR]

River networks are spatially complex systems difficult to describe by using simple morphological indices. To this concern, fractal theory arises as an interesting tool for quantifying such complexity. In this case of study, we have estimated for the first time the fractal dimension of Chilean networks distributed across the country, analysed at two different scales. These networks insert into variable environments, not only from a climatic and hydrological point of view, but also from a morphological point of view. We investigate to which extent the fractal dimension is able to describe the apparent disorganized character of landscape, by applying two methods. Striking patterns of organization related to Horton ratios and the fractal dimension are reported and discussed. This last parameter depends on the scale of the network, showing interesting groupings by tectonic and climatological factors. Our results suggest that under restricted conditions, the fractal dimension could help to capture the intricate morphology of Chilean networks and its links with the hydrological, climatic, and tectonic conditions present across the country. [ABSTRACT FROM AUTHOR]

Currently, in Chile, more than a quarter-million of patients are waiting for an elective surgical intervention. This is a worldwide reality, and it occurs as the demand for healthcare is vastly superior to the clinical resources in public systems. Moreover, this phenomenon has worsened due to the COVID-19 sanitary crisis. In order to reduce the impact of this situation, patients in the waiting lists are ranked according to a priority. However, the existing prioritization strategies are not necessarily systematized, and they usually respond only to clinical criteria, excluding other dimensions such as the personal and social context of patients. In this paper, we present a decision-support system designed for the prioritization of surgical waiting lists based on biopsychosocial criteria. The proposed system features three methodological contributions; first, an ad-hoc medical record form that captures the biopsychosocial condition of the patients; second, a dynamic scoring scheme that recognizes that patients' conditions evolve differently while waiting for the required elective surgery; and third, a methodology for prioritizing and selecting patients based on the corresponding dynamic scores and additional clinical criteria. The designed decision-support system was implemented in the otorhinolaryngology unit in the Hospital of Talca, Chile, in 2018. When compared to the previous prioritization methodology, the results obtained from the use of the system during 2018 and 2019 show that this new methodology outperforms the previous prioritization method quantitatively and qualitatively. As a matter of fact, the designed system allowed a decrease, from 2017 to 2019, in the average number of days in the waiting list from 462 to 282 days. [ABSTRACT FROM AUTHOR]

This research aims to determine the existence of inflection points when companies' credit risk goes from being minimal (Hedge) to being high (Ponzi). We propose an analysis methodology that determines the probability of hedge credits to migrate to speculative and then to Ponzi, through simulations with homogeneous Markov chains and the k-means clustering method to determine thresholds and migration among clusters. To prove this, we used quarterly financial data from a sample of 35 public enterprises over the period between 1 July 2006 and 28 March 2020 (companies listed on the USA, Mexico, Brazil, and Chile stock markets). For simplicity, we make the assumption of no revolving credits for the companies and that they face their next payment only with their operating cash flow. We found that Ponzi companies (1) have a 0.79 probability average of default, while speculative ones had (0) 0.28, and hedge companies (−1) 0.009, which are the inflections point we were looking for. Our work's main limitation lies in not considering the entities' behavior when granting credits in altered states (credit relaxation due to credit supply excess). [ABSTRACT FROM AUTHOR]

The main goal of this research is to determine the link between happiness and individual subjective life expectancy (SLE) among Chilean senior citizens. We use data from the 2015 edition of the Chilean Social Protection Survey. Our sample consists of 1298 seniors: 700 aged 65–74, 421 aged 75–84, and 177 aged 85 and older. We provide a novel methodological approach that allows us to measure the relative contribution of happiness to SLE, by combining the Shapley–Owen–Shorrocks decomposition with contrasts of marginal linear predictions of the equality of the means by groups. Results reveal that happiness is the most important determinant of seniors' SLE, and the effect is stronger the older the people are. Addressing varying levels of happiness is important because both happiness and unhappiness have a significant impact. In an ageing population, social agents should consider that these variables (happiness and SLE) are related to engagement in healthy lifestyles. If prevention programs integrated this interaction, welfare systems could save scarce resources. Therefore, governments should foster happiness to support active ageing. [ABSTRACT FROM AUTHOR]

In response to increased energy consumption and CO2 emissions, various energy efficiency policies, standards and housing certifications have emerged around the world. These aim to measure and quantify energy efficiency and endorse homes for meeting certain standards according to consistent categories that vary by continent or country. These energy rating systems correspond to a series of criteria and formulations that, through the calculation and combination of multiple variables, establish the classification values. In Chile, there is the Energy Rating System for Housing (CEV), which performs energy efficiency calculations using dynamic heat balance spreadsheets in Microsoft® Excel. When applied for everyday use and at a large scale, this system has the disadvantage of requiring a great deal of processing time for each simulation. This research proposes an improvement to the CEV energy demand calculation mechanism by generating a solution that takes advantage of the use of multiprocessors and implements the various algorithms in the C programming language. The results show that the CEV values obtained with the proposed calculation engine are equal to those of the current system but demonstrate a 76.5% improvement in their processing time. [ABSTRACT FROM AUTHOR]

Most humans today have mobile phones. These devices are permanently collecting and storing behavior data of human society. Nevertheless, data processing has several challenges to be solved, especially if it is obtained from obsolete technologies. Old technologies like GSM and UMTS still account for almost half of all devices globally. The main problem in the data is known as neighboring network hit (NNH). An NNH occurs when a cellular device connects to a site further away than it corresponds to by network design, introducing an error in the spatio-temporal mobility analysis. The problems presented by the data are mitigated by eliminating erroneous data or diluting them statistically based on increasing the amount of data processed and the size of the study area. None of these solutions are effective if what is sought is to study mobility in small areas (e.g., Covid-19 pandemic). Elimination of complete records or traces in the time series generates deviations in subsequent analyses; this has a special impact on reduced spatial coverage studies. The present work is an evolution of the previous approach to NNH correction (NFA) and travel inference (TCA), based on binary logic. NFA and TCA combined deliver good travel counting results compared to government surveys (2.37 vs. 2.27, respectively). However, its main contribution is given by the increase in the precision of calculating the distances traveled (37% better than previous studies). In this document, we introduce FNFA and FTCA. Both algorithms are based on fuzzy logic and deliver even better results. We observed an improvement in the trip count (2.29, which represents 2.79% better than NFA). With FNFA and FTCA combined, we observe an average distance traveled difference of 9.2 km, which is 9.8% better than the previous NFA-TCA. Compared to the naive methods (without fixing the N N H s ), the improvement rises from 28.8 to 19.6 km (46.9%). We use duly anonymized data from mobile devices from three major cities in Chile. We compare our results with previous works and Government's Origin and Destination Surveys to evaluate the performance of our solution. This new approach, while improving our previous results, provides the advantages of a model better adapted to the diffuse condition of the problem variables and shows us a way to develop new models that represent open challenges in studies of urban mobility based on cellular data (e.g., travel mode inference). [ABSTRACT FROM AUTHOR]

The generation of energy from renewable sources is a fundamental aspect for the sustainable development of society, and several energy sources such as solar, biomass, biogas, and wind must be used to the maximum to meet existing needs. In Chile, there are villages that are off-grid. A real case study is presented in this research. To meet the needs of this village we have proposed a mathematical optimization model using a CPLEX optimizer to generate the necessary energy power while minimizing the cost of energy (COE). In this study, different scenarios have been evaluated with respect to the existing energy availabilities, for example, in different periods of the year, demonstrated in terms of economic costs, the viability of resources such as biomass and biogas, and the viability of the energy production of wind power given the associated high costs. Finally, the effect of the use of renewable energy in consideration of CO2 emissions is studied in our research. [ABSTRACT FROM AUTHOR]

Pharmacy inventory management is a critical process in healthcare centers. On the one hand, effective drug procurement is fundamental for fulfilling the therapeutic requirements of patients. On the other hand, as hospital pharmacies' purchasing and storage costs comprise an important share in the hospital budgets, efficient inventory management may play a central role in operational cost containment. Therefore, healthcare centers should design and implement decision-aid strategies for planning the purchase of drugs with the aim of avoiding excessive purchasing volumes and optimizing warehouse capacity, while also meeting forecast demand and ensuring critical stock levels. In this study, we present the methodological features of a decision-aid tool for planning the purchases and inventory levels for the controlled medication pharmacy of the Regional Hospital of Talca, Chile. We report the results obtained after 1 year of operation; these results show that our strategy produced more than 7% savings compared to the regular inventory planning strategy and was more effective in preserving critical stock levels. Furthermore, from a computational point of view, our strategy outperforms a recently published approach for a similar application. [ABSTRACT FROM AUTHOR]