•Aging workforce represents one of the greatest challenges that mankind in the 21st century.•A multicriteria approach to integrate OSHP and ISP in the context of AW is proposed.•One of the most ...important results proven the importance of adjusting the workload as aging advances.•The model can be an useful tool for policy and business decision-makers.
Aging represents one of the greatest challenges that mankind will have to face in the 21st century. In this regard, there is a need to deepen the technical and organizational aspects for rebalancing the job/employee relationship. Nevertheless, there are no robust approaches assisting decision-makers in the effective management of Occupational Safety & Health Performance (OSHP) and Industry Systems Productivity (ISP) convergence with a focus on Aging Workforce (AW). There is no single and unique solution. Therefore, in a win-to-win logic that favors the characteristic of the reference scenario, the aim of this paper is to propose a multicriteria approach to integrate OSHP and ISP in the context of AW. More specifically, Intuitionistic Fuzzy Analytic Hieararchy Process (IF-AHP) was used to elicit the criteria and sub-criteria under uncertainty considering degree of belongingness and non-belongingness. Decision Making Trial and Evaluation Laboratory (DEMATEL) was applied to evaluate the presence and strength of the interrelations and feedback among OSHP and ISP criteria and sub-criteria. Finally, Combined Compromise Solution (CoCoSo) was implemented to calculate a Working Suitability Index (WSI) per aging. Based on this index, interventions on each worker are proposed to underpin redesign of the company while to provide a flexible management propelling high performance in terms of OSHP and ISP. A real case study in Colombia is presented. The results revealed that Efficiency and Quality with 0.162 were found to be the most important factors in evaluating the performance of aging electrical engineers while Psychosocial risk and Occupational Safety were concluded to be the main drivers for a joint OSHP-ISP aging workforce management. The model can be an useful tool for policy and business decision-makers from the electrical sector and can be replicated with slight changes in other industries.
An effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a ...comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework's applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R
2
), and mean squared error (MSE). According to each method's results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%.
Considering the unexpected emergence of natural and man-made disasters over the world and Turkey, the importance of preparedness of hospitals, which are the first reference points for people to get ...healthcare services, becomes clear. Determining the level of disaster preparedness of hospitals is an important and necessary issue. This is because identifying hospitals with low level of preparedness is crucial for disaster preparedness planning. In this study, a hybrid fuzzy decision making model was proposed to evaluate the disaster preparedness of hospitals. This model was developed using fuzzy analytic hierarchy process (FAHP)-fuzzy decision making trial and evaluation laboratory (FDEMATEL)-technique for order preference by similarity to ideal solutions (TOPSIS) techniques and aimed to determine a ranking for hospital disaster preparedness. FAHP is used to determine weights of six main criteria (including hospital buildings, equipment, communication, transportation, personnel, flexibility) and a total of thirty-six sub-criteria regarding disaster preparedness. At the same time, FDEMATEL is applied to uncover the interdependence between criteria and sub-criteria. Finally, TOPSIS is used to obtain ranking of hospitals. To provide inputs for TOPSIS implementation, some key performance indicators are established and related data is gathered by the aid of experts from the assessed hospitals. A case study considering 4 hospitals from the Turkish healthcare sector was used to demonstrate the proposed approach. The results evidenced that Personnel is the most important factor (global weight = 0.280) when evaluating the hospital preparedness while Flexibility has the greatest prominence (c + r = 23.09).
Gun and rifle manufacturing contain various failures in the process of CNC machining, material supply, research & development, infrastructure and, operator. Due to these failures, the enterprise is ...exposed to great economic losses and a decrease in competition in the global market. In addition, failures in production cause events that seriously threaten human health. Failure analysis can increase safety by determining the cause of potential errors and taking measures for identified errors in the life cycle of the products. Therefore, this study employs a Bayesian Network (BN)-based modeling approach for capturing dependency among the basic events and obtaining top event probability. Firstly, a fault tree analysis (FTA) diagram is constructed, since its target is to pinpoint how basic event failures result in a top event (system) failure by an AND/OR logical gate. While, AND logical gate should take place in both cases, it is sufficient to realize one of the states in the OR logical gate. Then, a BN-based on fault tree transformation is applied. A case study in a leading weapon factory that produces various types of guns and rifles in the Black Sea region of Turkey is performed. For the application viewpoint, appropriate control measures can be taken into account to decrease the number of failed products based on the performed failure analysis.
This paper presents a risk assessment approach for analyzing the causes of malfunction-related main engine slowdowns. A fuzzy Bayesian Network-based methodology is used to assess the factors ...contributing to the engine's slow-down processes. The model addresses the complexity and uncertainty inherent in maritime operations with fuzzy sets where numerous interrelated factors can affect engine performance, and the Bayesian network to capture probabilistic dependencies. It considers various potential causes of the slow-down of ship engines that the manufacturer provides. Results demonstrate the model's ability to identify the influential factors leading to engine slow-down events and quantify the overall risk. Integrating fuzzy logic and Bayesian Networks comprehensively assesses relevant risk factors. It enables maritime stakeholders to manage engine performance and improves operational safety proactively. Findings can inform decision-makers, enabling the implementation of targeted maintenance strategies, fuel quality control measures, and crew training programs in the maritime industry.
The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic ...scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was
(14.4%), while
evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.
Manufacturing firms aim to increase their profits and reduce costs in a competitive and rapidly changing market. One of the most important ways to reach these goals is to forecast sales correctly. ...Furniture manufacturing, which is considered a prosperous and growing industry in Turkey, has an increasing trend related to the growth in construction and associated industries, increase in urban migration and increase in per capita income. Accuracy of sales forecasting in furniture industry is affected by external factors, such as consumer confidence index, producer price index, month of the year and number of vacation days as well as the time factor itself. This study aims to develop an Autoregressive Integrated Moving Average with external variables (ARIMAX) to forecast the total monthly sales of furniture products of a well-known manufacturer in Turkey. As a follow up study, a performance comparison between ARIMAX, artificial neural networks (ANNs) and ARIMAX-ANN hybridization is performed. In conclusion, results of performance measures demonstrate that hybrid model developed for each amount of product sales give better accuracy values than single methods. Overall, it is proved that using the ARIMAX and hybridization of this method with ANN are applicable for forecasting monthly sales of furniture products. Keywords: ARIMAX, ANN, hybrid method, sales forecasting, furniture industry Proizvodne tvrtke nastoje povecati dobit i smanjiti troskove na konkurentnom trzistu koje se brzo mijenja. Jedan od najvaznijih nacina postizanja tih ciljeva jest sto tocnije predvidanje prodaje. Proizvodnja namjestaja, koja se smatra perspektivnom i rastucom industrijom u Turskoj, biljezi sve vecu potraznju, sto se povezuje s rastom gradevne industrije is njom povezanih djelatnosti te s povecanjem urbanih migracija i povecanjem dohotka po stanovniku. Na tocnost predvidanja prodaje u industriji namjestaja utjecu vanjski cinitelji kao sto su indeks povjerenja potrosaca, indeks proizvodackih cijena, mjeseci u godini i broj dana godisnjih odmora, kao i faktor vremena. Cilj ove studije jest razvoj modela integriranoga autoregresivnog pomicnog prosjeka (Autoregressive Integrated Moving Average--ARIMAX) s vanjskim varijablama za predvidanje ukupne mjesecne prodaje namjestaja poznatog proizvodaca u Turskoj. U nastavku istrazivanja provedena je usporedba performansi modela ARIMAX, umjetnih neuronskih mreza (ANNs) i hibridnog modela ARIMAX-ANN. Dobiveni rezultati pokazuju da hibridni model razvijen za prodaju svakog proizvoda daje bolju tocnost od pojedinacnih modela. Zakljucno, dokazano je da se za predvidanje mjesecne prodaje namjestaja moze primijeniti hibridizacija modela ARIMAX s ANN-om. Kljucne rijeci: ARIMAX, ANN, hibridna metoda, predvidanje prodaje, industrija namjestaja
The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare ...pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.
Most organizations in manufacturing environments aim to increase their profits and reduce costs against competitive and rapidly changing market conditions. Accuracy of sales forecasting is ...undoubtedly a successful way to reach the aforementioned goals. At the same time, this enables executives to improve customer satisfaction, reduce lost sales and plan production efficiently. As a growing industry in Turkey, furniture manufacturing has an increased product demand in relation to the recent growth in construction and related industries, increase in urban population and increase in person-level income. Therefore, accurate sales forecasting systems in this industry are more focused on the special and calendar factors, such as consumer confidence index, producer price index, time of the year and number of vacation days. In this paper, an artificial neural network (ANN) based forecasting model is proposed by using MATLAB for processing total monthly sales data of a corporate furniture manufacturer located in the Black Sea region of Turkey. The method is a component of ANN, namely Bayesian regularization. The proposed model is applied to monthly sales figures of a corporate furniture manufacturing company. In conclusion, the results of performance measures show that using the ANN model based on Bayesian rules training is an applicable choice for forecasting of monthly sales of the observed furniture factory. Keywords: artificial neural networks; Bayesian rules training; sales forecasting; furniture manufacturing Cilj vecine proizvodnih organizacija jest povecanje dobiti i smanjenje troskova u skladu s konkurentnim i promjenjivim trzisnim uvjetima. Tocnost predvidanja prodaje nesumnjivo je uspjesan nacin postizanja navedenih ciljeva. Istodobno, to povecava zadovoljstvo korisnika, ucinkovito smanjuje izgubljenu prodaju i omogucuje bolje planiranje proizvodnje. U proizvodnji namjestaja, industriji koja se u Turskoj sve jace razvija, biljezi se povecana potraznju proizvoda, u skladu s nedavnim rastom gradevinskih i srodnih industrija, s povecanjem broja urbanog stanovnistva i s rastom osobnih prihoda. Stoga precizni sustavi predvidanja prodaje u industriji namjestaja vise pozornosti usmjeravaju na posebne i kalendarske cimbenike poput indeksa povjerenja potrosaca, indeksa proizvodackih cijena, doba godine i broja dana odmora u godini. U ovom je radu predlozen model predvidanja na temelju umjetne neuronske mreze (ANN) uz pomoc MATLAB-a za obradu podataka ukupne mjesecne prodaje proizvodaca uredskog namjestaja koji se nalazi u Crnomorskoj regiji u Turskoj. Metoda je komponenta ANN-a, tj. Bayesova regulacija. Predlozeni se model primjenjuje na podatke o mjesecnoj prodaji tvrtke za proizvodnju uredskog namjestaja. zakljucno, rezultati mjerenja uspjesnosti pokazuju da je primjena ANN modela utemeljenoga na Bayesovim pravilima dobar izbor za prognoziranje mjesecne prodaje promatrane tvornice namjestaja. Kljucne rijeci: umjetne neuronske mreze, Bayesova pravila ucenja, predvidanje prodaje, proizvodnja namjestaja
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