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- MakaleAçık Erişim2025Scopus SJRQ1Evaluation of Green Deal compliance performance with a hybrid comparative multi-attribute decision model
Karadag, Mehmet; Gul, Muhammet; Yucesan, Melih; Ortiz-Barrios, Miguel; Ishizaka, Alessio; Khan, Jehangir
The environmental and economic transformation initiated by the European Green Deal is influencing not only EU member states, but also other countries engaged in trade with the EU. This transformation mandates a shift towards sustainable practices in sectors such as agriculture, industry, transportation, energy, and finance. Compliance with the Green Deal is increasingly crucial for countries, particularly those with significant commercial ties to the EU. However, differing levels of development lead to varying degrees of implementation. While studies on the Green Deal compliance of EU member states exist, an evaluation of Türkiye—a candidate EU member with substantial trade relations—would address a significant gap in the literature. This study introduces a novel hybrid comparative multi-attribute decision model that combines subjective (Best Worst Method and its derivatives) and objective (Entropy) weighting to determine the relative importance of 14 sub-criteria under three main criteria: “Reducing” the impacts of climate change, “Protecting” the planet and health, and “Enabling” a green and just transition. Using the Ratio Product Model (RPM), Türkiye's Green Deal compliance from 2017 to 2021 was evaluated. Sensitivity analysis was conducted to validate the robustness of the findings, and comparisons were made using various multi-attribute decision models, including SAW, TOPSIS, and VIKOR, as well as benchmarking against EU countries. The criteria weights, derived from a combination of subjective and objective methods, highlighted the dominant importance of the “Reducing” criterion across all methods. The “Enabling” and “Protecting” criteria showed slight variations in importance depending on the method. The RPM results indicated that 2021 was Türkiye's best-performing year in terms of Green Deal compliance, while 2017 showed the weakest performance. Despite some fluctuations between 2018 and 2019, Türkiye demonstrated significant progress overall. The study not only advances the methodology for evaluating Green Deal compliance but also provides actionable insights for policymakers and industry stakeholders, enabling more informed decision-making. By highlighting Türkiye's trajectory, the findings offer strategic guidance for non-EU countries aiming to align with Green Deal objectives, fostering sustainable development and strengthening economic ties with the EU.
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İncele - MakaleAçık Erişim2024Scopus SJRQ1Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation
Ortiz-Barrios, Miguel; Cleland, Ian; Donnelly, Mark; Gul, Muhammet; Yucesan, Melih; Jiménez-Delgado, Genett Isabel; Nugent, Chris; Madrid-Sierra, Stephany
Background: Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user’s likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. Objective: This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. Methods: First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts’ knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. Results: We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree (M3=2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods; (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). Conclusions: The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.
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İncele - MakaleAçık Erişim2024Scopus SJRQ1Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons
Ortiz-Barrios, Miguel; Ishizaka, Alessio; Barbati, Maria; Arias-Fonseca, Sebastián; Khan, Jehangir; Gul, Muhammet; Yücesan, Melih; Alfaro-Saíz, Juan-Jose; Pérez-Aguilar, Armando
Seasonal Respiratory Diseases (SRDs) usually produce a heightened number of Emergency Department (ED) attendances due to their rapid dissemination within the community and the ineffective prevention measures. Such a context requires effective management of the emergency care processes to provide in-time diagnosis and treatment to infected patients. Nonetheless, EDs have evidenced severe operational deficiencies during these periods, thereby provoking extended bed waiting times in Hospitalization Departments (HDs). Therefore, this paper presents a hybrid approach merging Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to shorten the bed waiting times in HDs considering patient records collated in the first emergency care stages. First, we implemented Random Forest (RF) to estimate the probability of respiratory worsening based on sociodemographic and clinical patient data. Second, we inserted these probabilities into a DES model mimicking the emergency care from the admission to the HD. We then pretested different HD configurations and strategies seeking to reduce the HD bed waiting time. A case study of a European hospital group was used to validate the suggested framework. The AI-DES model enabled decision-makers to identify an improvement proposal with hospitalization bed waiting time lessening, oscillating between 7.93 and 7.98 h.
AtıfScopus16OpenAlex17İncele - MakaleAçık Erişim2022Scopus SJRQ1A multicriteria approach to integrating occupational safety & health performance and industry systems productivity in the context of aging workforce: A case study
Ortiz-Barrios, Miguel; Silvera-Natera, Esnela; Petrillo, Antonella; Gul, Muhammet; Yucesan, Melih
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.
AtıfScopus17WoS11OpenAlex23İncele - MakaleAçık Erişim2022Scopus SJRQ1A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case
Ortiz-Barrios, Miguel; Gul, Muhammet; Yucesan, Melih; Alfaro-Sarmiento, Isaac; Navarro-Jiménez, Eduardo; Jiménez-Delgado, Genett
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.
AtıfScopus27WoS24OpenAlex34İncele - MakaleAçık Erişim2021Scopus SJRQ1A multiple criteria decision-making approach for increasing the preparedness level of sales departments against COVID-19 and future pandemics: A real-world case
Ortiz-Barrios, Miguel; Borrego-Areyanes, Arlen Alaine; Gómez-Villar, Iván Darío; De Felice, Fabio; Petrillo, Antonella; Gul, Muhammet; Yucesan, Melih
The impact of the pandemic and the lockdown has been more devastating than expected on the world economy. It is essential to formulate strategies in real-time. In this research, a multicriteria decision-making model for increasing the preparedness level of sales departments when facing COVID-19 waves and future pandemics is proposed. The model is comprised of 8 criteria, 29 sub-criteria, and 7 alternatives. The study is based on the integration of the AHP and TOPSIS techniques. AHP is used for calculating the criteria and sub-criteria weights. While, TOPSIS is used for calculating the preparedness level, ranking the companies, and identifying the weaknesses that should be addressed for increasing their effectiveness in the current market scenario. The model is developed with the aid of an experts’ group from the electrical appliance sector and studies from the reported literature. This application is completely novel in the literature and has been applied in the wild with remarkable companies in Colombia. A case study in the electrical appliance sector is presented as a pilot study but it should be noted that the methodology is flexible and scalable in any scenario.
AtıfScopus18OpenAlex21İncele - MakaleAçık Erişim2020Scopus SJRQ1Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals
Ortiz-Barrios, Miguel; Gul, Muhammet; López-Meza, Pedro; Yucesan, Melih; Navarro-Jiménez, Eduardo
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).
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