Criteria Importance Through Intercriteria Correlation Research Articles

Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis.

The stability of the power grid and the operational security of the power system depend on the precise prediction of wind speed. In consideration of the nonlinear and non-stationary characteristics of wind speed in different seasons, this paper employs the weight of wind resource index calculated by triangular fuzzy analytic hierarchy process (TF-AHP), criteria importance through inter-criteria correlation (CRITIC), and entropy weight method (EWM) to improve gray correlation analysis (GRA) and obtain the gray correlation degree of each season. In addition, a wind speed prediction model is provided that includes single-layer and two-layer weighting and is based on both deep and shallow machine learning models. At first, we establish each quarter's wind resource characteristics at typical monthly intervals of 10min, 30min, 60min, and 120min. The GRA's TF-AHP-CRITIC-EWM, enhanced with subjective and objective weights, is used to assess the available wind resources in each season and to compute the forecasted combination of wind speed for each season. As the final prediction results, the prediction values of each layer model are evaluated independently. For the intervals with considerable errors, we apply wavelet denoising and replacement combination. The simulation findings show that the proposed combined model surpasses earlier benchmark models in terms of goodness of fit, prediction accuracy, and generalizability.

MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches.

Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine.

An experimental investigation of wire breakage and performance optimisation of WEDM process on machining of recycled aluminium alloy metal matrix composite

Abstract In this research, a novel aluminium metal matrix composite (AMMC) was developed using recycled aluminium alloy as a matrix with 5% alumina as reinforcement. The machining experiments were conducted by varying the input parameters such as voltage (V s ), wire feed rate (F w ), current (I p ), pulse on time (ON T ) and pulse off time (OFF T ), on wire breakage. The effect of voltage level and wire breakage frequency was analysed. The parameter combinations for machining the slot of size 5 mm width and 10 mm height with high machining rate (MR) and less surface roughness (R a ) were analysed using the CRiteria Importance Through Intercriteria Correlation (CRITIC) and simple additive weighting (SAW) methods. The wire breakage frequency is lesser at minimum peak current. The optimal parameter combination for higher MR and lower R a is found to be at 30 V, 7 mm/min, 30 A, 120 μs (ON T ) and 70 μs (OFF T ). Analysis of variance (ANOVA) is performed to understand the significant factors affecting the WEDM process. ANOVA results predict that wire feed rate and voltage contribute 47.82% and 21.23%, respectively, to MR; and pulse on time shows a 23.06% influence on surface roughness. Scanning electron microscopy (SEM) was used to ascertain the pattern of wire breakage in WEDM, and based on the results obtained from employing this technique, it is inferred that the erosion and breakage of the wire are not instantaneous and that a cone shape is formed on the either portion of the wire.

Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach

The selection of suitable nanoparticles is an important task for their applications as heat transfer promoters for the mitigation of thermal issues in energy storage systems. This paper aims to present a systematic framework for the selection of nanoparticles based on multiple attribute decision-making tools. A hybrid Grey relational analysis-based technique for order preference by similarity to ideal solution (GRA-TOPSIS) approach combining the advantages of both GRA and TOPSIS methodologies is proposed and opted for evaluation, comparison, and ranking of different alternatives under the influence of several objective and subjective criteria weights. The criteria importance through inter-criteria correlation (CRITIC) and entropy weighing methods are employed to determine the objective weights, while the subjective weights are calculated using the analytic hierarchy process (AHP). Also, an attempt is made to present a generalized weight integration formula that combines several objective and subjective weights of attributes. The results demonstrate Al2O3 nanoparticles as the most acceptable alternative. The sensitivity analysis performed proves the robustness and feasibility of the adopted decision framework. Additionally, the rank reversal issue is also addressed to present the qualitative assessment of estimated results. Thus, the evaluation methodology discussed in this paper seems to provide a theoretical reference strategy for nanoparticle selection in heat transfer enhancement applications. Highlights A novel integrated multiple criteria decision making scheme for nanoparticle selection is presented An attempt is made to present a generalized weight integration formula combining several objective and subjective weights of attributes Thermal conductivity and specific surface area are reported as the most significant attributes. Al2O3 is highlighted as the best choice. The sensitivity analysis and rank-reversal test have also been performed to check the robustness of suggested procedures and Al2O3 has outperformed all other alternatives under consideration.

Stakeholder assessment in construction projects using a CRITIC-TOPSIS approach

PurposeStakeholder assessment is based on attributes possessed by stakeholders, making the process heavily reliant on evaluating attribute weights. The present assessment methods are based on Analytical Hierarchy Process, Analytical Network Process, and pairwise comparisons that heavily rely on decision-makers’ skills and knowledge. An objective evaluation of attribute weight needs to be done to overcome this challenge.Design/methodology/approachA Multi Criteria Decision Making (MCDM) based approach, through the combined use of Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods has been incorporated to objectively evaluate the weights of the stakeholder attributes and their ranking with a specific focus on construction megaprojects. Five stakeholder attributes and twelve commonly associated stakeholders with megaprojects were considered to achieve this.FindingsThe new model presented power, durability and legitimacy as three important stakeholder attributes, further used to determine stakeholder importance. The client, contractor and project manager were identified as the top three internal stakeholders, and customers, local community and utility service providers as three crucial external stakeholders.Research limitations/implicationsThe findings from the proposed method provide the practitioners with updated information about the attributes they need to look into when planning stakeholder involvement and management. Further, it also leads them to the established framework for the provided stakeholder ranking when planning their management strategies.Originality/valueThe use of CRITIC method allowed to address the possibility of any correlation between the attributes in their weight determination. This was one of the advantages as co-relation between the attributes during their weight determination has not been addressed in previous methods.

A Comprehensive Evaluation Method for the Service Status of Groins in Waterways Based on an AHP-Improved CRITIC Combination Weighting Optimization Model

A comprehensive evaluation model that combines an analytical hierarchy process (AHP) method and the improved version of the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is proposed to solve integrated assessment problems related to the service status of groins in waterways. In view of the influencing factors and functional characteristics of damaged groins for waterway regulation, a waterway groin service status (WGSS) evaluation index system with a hierarchical structure was constructed using target-criteria elements. Additionally, based on the multi-attribute characteristics of the indicators, standards for rating and assigning values to elemental indicators were developed. Considering that traditional empowerment methods are rather one-sided and limited, an AHP-improved CRITIC method combination assigning optimization model was established based on the least squares principle, and the optimal combination weights were solved by the Lagrange multiplier method. The reliability and applicability of the model were tested by successfully applying the evaluation model to the comprehensive evaluation of the service status of spur dikes in the upper reaches of the Yangtze River. The results showed that the combined weights that were calculated by the AHP and the improved CRITIC optimization model were able to reflect both subjective and objective information, which makes up for the shortcomings of a single assignment method, and the weights assigned to the service status evaluation indexes for groins were reasonable. The comprehensive evaluation method to determine the service status of groins proposed in this paper was more accurate than the traditional evaluation method with subjective assignment and provided a scientific basis for decision making to maintain the sustainability of groin function.

Thermo-hydraulic characterization and design optimization of delta-shaped obstacles in solar water heating system using CRITIC-COPRAS approach

This study reports on the performance of delta-shaped obstacles in a solar water heating system (SWHS) by means of experimental analysis and optimization. The influence of different parameter combinations such as Reynolds number (200, 600, 1000, 1400, 1800), pitch ratio (0.5, 1, 1.5), blockage ratio (0.15, 0.20, 0.25), and angle of attack (45°) on Nusselt number, friction factor and thermo-hydraulic performance of SWHS were analyzed. For the combination of Reynolds number = 1800 and pitch ratio = 0.5, the Nusselt number remained highest for 0.25 of blockage ratio, whereas the friction factor remained lowest for a blockage ratio of 0.15. The maximum thermo-hydraulic efficiency was achieved using Reynolds number = 200, pitch ratio = 0.5, and blockage ratio = 0.20. The obtained results were intensely dependent on parameter combinations without any pronounced trend. Therefore, criteria importance through inter-criteria correlation (CRITIC) and complex proportional assessment (COPRAS) approach was implemented to find optimal design alternative. The results of the hybrid CRITIC-COPRAS approach showed that the combination of Reynolds number = 1800, pitch ratio = 0.5, blockage ratio = 0.20, and angle of attack = 45° is the best alternative for maximum thermal enhancement in SWHS. The sensitivity analysis proved the robustness of the results that the first-ranked alternative is the most dominant in all scenarios.

An Integrated Taxonomy Method Using Single-Valued Neutrosophic Number MAGDM for Evaluating the Physical Education Teaching Quality in Colleges and Universities

Before the advent of the new era, my country has completed the popularization of higher education, and the country has gradually begun to pay attention to the quality of higher education. Under the background of Healthy China, the quality of physical education teaching in colleges and universities has become a hot topic of social concern. The quality of physical education teaching in colleges and universities directly determines the quality of physical education talents. A reasonable, complete, and scientific evaluation system of physical education teaching quality in colleges and universities is an effective measure to improve the quality of physical education teaching and the training of physical education talents. The physical education teaching quality evaluation in colleges and universities is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In such paper, taxonomy method is designed for solving the MAGDM under single-valued neutrosophic sets (SVNSs). First, the score function of SVNSs and criteria importance through intercriteria correlation (CRITIC) method is used to derive the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest single-valued neutrosophic number development attribute values from the single-valued neutrosophic number positive ideal solution (SVNNPIS). Finally, a numerical example for physical education teaching quality evaluation in colleges and universities is given to illustrate the built method.

Data-Driven Analysis of Regional Water Resources Allocation Decision-Making

Regional water allocation is of great importance to regional coordinated development. Therefore, this paper constructs a data-driven model for regional water allocation analysis to address the existing problems of the imbalance between water supply and demand, irrational utilization of water resources and water scarcity. Firstly, a classification of regional water allocation case is constructed through cluster analysis to obtain similar regions with the same salient characteristics. Then, the regions in the same category are divided into regions to be predicted and other regions, and the similarity of water resources allocation between regions to be predicted and other regions is calculated. The Criteria Importance Through Intercriteria Correlation (CRITC) method is used to calculate the weighted values of each index and forecast water demands of regions to be predicted. Finally, an example analysis of water allocation of each city in Hubei province in 2020 was carried out, and the results indicated that when the water allocation of each city in Hubei province was divided into 4 categories, the water allocation characteristics of different cities are presented. The relative errors of the predicted water demand do not exceed 5%, which is highly accurate and can provide decision support for rational water allocation.

Stir Casting Process Analysis and Optimization for Better Properties in Al-MWCNT-GR-Based Hybrid Composites

Pure aluminium poses inferior properties that limit its use in load-bearing applications. Reinforcing multiwall carbon nano-tube (solid lubricant) and graphene to aluminium matrix offers better (antifriction, hardness, and wear resistance) properties in composites for such applications. A stir casting processing route is employed to prepare the hybrid composite (aluminium-multiwall carbon nanotube-graphene Al-MWCNT-GR). The Taguchi L16 experimental matrix representing four variables (percent reinforcement of graphene, die temperature, melt temperature, and stir speed) operating at four levels were studied to analyze and obtain higher hardness and low wear rate in hybrid composites. Percent reinforcement of graphene showed maximum impact, and die temperature resulted with the least contribution towards both the responses. Criteria importance through intercriteria correlation (CRITIC) method is applied to determine the weight fractions (importance) for hardness and wear rate equal to 0.4752 and 0.5482, respectively. Grey relational analysis (GRA) and multi-objective optimization by the ratio analysis (MOORA) method converts multiple objective functions into a single objective function with weight fractions assigned to each output. Taguchi-CRITIC-MOORA outperformed the Taguchi-CRITIC-GRA method, which could result in 31.77% increase in hardness and a 36.33% decrease in wear rate compared to initial conditions. The optimal conditions ensure a dense microstructure with minimal pores, result in enhanced properties compared to that obtained for initial and average stir casting conditions. The worn-out surface results in a few thin and slender grooves between tracks with less crack propagation, ensuring self-lubrication in composites fabricated with the optimized condition. The better properties resulted in the hybrid composites correspond to optimized stir casting conditions and can be implemented in industries for large-scale applications.

RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT

The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach.

A Hybrid Group Decision Approach Based on MARCOS and Regret Theory for Pharmaceutical Enterprises Assessment under a Single-Valued Neutrosophic Scenario

Evaluating pharmaceutical enterprises with sustainable and high-quality development ability (SHQDA) can not only provide strategies for the pharmaceutical management department in formulating enterprise development plans, but also provide suggestions and guidance for enterprises to enhance their core competitiveness. Nevertheless, the prior research possesses several deficiencies in coping with the assessment of enterprises with SHQDA under uncertain environments to predict the psychological behavior of the evaluator and the correlation among the evaluation criteria. To conquer the aforementioned defects, we propose an integrated framework for rating pharmaceutical enterprises that incorporates regret theory, measurement alternatives and ranking based on the compromise solution (MARCOS) and Heronian mean operating within a single-value neutrosophic set (SVNS) environment. First, the single-valued neutrosophic number (SVNN) is employed to portray the assessment information of experts. Then, a novel single-valued neutrosophic score function is presented to enhance the rationality of the SVNN comparison. Next, a combined criteria weight model is constructed by synthesizing the best and worst method (BWM) and criteria importance through intercriteria correlation (CRITIC) approach to attain more reasonable and credible weight information. Furthermore, the integrated assessment framework combining regret theory-MARCOS method and Heronian mean operator is put forward to assess and select the enterprises with SHQDA under a single-valued neutrosophic setting. Ultimately, an empirical concerning the pharmaceutical enterprises assessment is presented within SVNS to illustrate the usefulness and effectiveness of the presented SVNS regret theory-MARCOS method. Thereafter, the sensitivity analysis and comparison analysis are implemented to provide evidence for the rationality and superiority of the proposed method.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

Performance Analysis of Insurance Companies Operating in the Turkish Insurance Sector’s Life and Retirement Branches Using Multiple-Criteria Decision-Making Methods

Bu çalışmada Türk sigorta sektöründe hayat/emeklilik branşında faaliyette bulunan sigorta şirketlerinin performanslarının çok kriterli karar verme (ÇKKV) tekniklerinden CRITIC (CRiteria Importance Through Intercriteria Correlation) ve TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemleri kullanılarak ölçülmesi amaçlanmıştır. Bu kapsamda Türk sigorta sektöründe faaliyet gösteren 18 hayat sigorta şirketinin 2010 – 2020 yılları arasındaki verileri ele alınmıştır. Analiz kapsamında CRITIC – TOPSIS bütünleşik modeli için Kısa Vadeli Borçlar/ Toplam Aktifler, Dönem Net Karı/ Toplam Aktifler, Cari Varlıklar / Kısa Vadeli Borçlar, Faaliyet Giderleri / Toplam Varlıklar, Gerçekleşen Hasar / Kazanılmış Prim (Net), Toplam Özkaynaklar / Toplam Aktifler, Vergi Öncesi Kar /Alınan Primler (Brüt), Net Kar / Toplam Özsermaye, Alınan Primler (Net) / Alınan Primler (Brüt) ve Teknik Kar-Zarar / Alınan Primler kriterleri seçilmiştir. Çalışmada seçilen kriterlerin öncelik ağırlıkları CRITIC yöntemi ile tespit edildikten sonra TOPSIS yöntemi ile sigorta şirketleri performans bakımından sıralanmıştır. Çalışma sonucunda ele alınan yıllar itibarıyla hayat sigorta şirketlerinin performanslarının belirlenmesinde en önemli kriterin Kısa Vadeli Borçlar/Toplam Aktifler olduğu tespit edilmiştir. Çalışma sonucunda ele alınan yıllar itibarıyla hayat sigorta şirketlerinin performanslarının belirlenmesinde en önemli kriterin Kısa Vadeli Borçlar/Toplam Aktifler olduğu tespit edilmiştir. Ayrıca Halk Hayat ve Emeklilik A.Ş. ile Ziraat Hayat ve Emeklilik A.Ş.’nin ele alınan yıllarda performans sıralamasında ilk sıralarda yer aldığı BNP Paribas Cardif Hayat A.Ş.’nin analize dahil edilen tüm yıllarda performans sıralamasında son sıralarda olduğu tespit edilmiştir.

Extended EDAS method for multiple attribute decision making in mixture z-number environment based on CRITIC method

In multiple attribute decision making (MADM) issues, the ambiguity, imprecision, and imperfection of assessment information may lead to inadequate decision-making results. However, the Z-number suggested by Zadeh in 2011 could somehow prevent this problem. For MADM issues with unknown attributes weights, an extended Distance from the Average Solution (EDAS) method is proposed under a mixture Z-number environment. In addition, the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights of the criterion, which is easy to calculate and avoids subjective forecasts. A novel illustrative example is provided to demonstrate the feasibility, validity, and practicability of the presented method, and is compared with existing decision methods. The outcome indicates that the suggested method can solve complicated decision-making problems.

Micro-hub location selection for sustainable last-mile delivery.

Sustainable Last-Mile Delivery (LMD) is one of the key phases in city logistics. Micro-hubs in cities are new emerging solutions for an easier and viable last-mile delivery process. The important question in smart and modern cities is the determination of the best micro-hub location for the LMD. This paper solves the micro-hub location selection for sustainable LMD using the multi-criteria decision-making (MCDM) techniques. The main reason for solving the micro-hub location selection is to make the last-mile delivery process in Pardubice as easier and effortless as possible. The Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC) method, and Weighted Aggregated Sum Product Assessment (WASPAS) method are coupled to solve the micro-hub location selection for sustainable LMD. First, five criteria and alternatives are identified and discussed with the experts. Second, the hybrid criteria importance is determined by combining the BWM and CRITIC methods. Third, the obtained hybrid weights are integrated within the WASPAS method to rank the micro-hub locations. The findings of the Hybrid BWM-CRITIC-WASPAS model show the Alternative 2 („Hůrka“) as the best possible location for Pardubice in the context of the LMD. In addition, a comparative analysis with some of the existing MCDM approaches is conducted for the same problem and its results show a high level of matching with the applied hybrid BWM-CRITIC-WASPAS method, which means that Alternative 2 („Hůrka“) is strongly recommended as a micro-hub location for sustainable LMD in Pardubice.

Optimization of extraction process of Congrong Shujing Granules based on AHP-CRITIC analysis

The index weight coefficients were determined by comparing the analytic hierarchy process(AHP), the criteria importance through inter-criteria correlation(CRITIC), and the AHP-CRITIC mixed weighting method. The comprehensive scores of index components(echinacoside, salvianolic acid B, paeoniflorin, and ointment yield) of each group in the orthogonal test were compared to optimize the extraction process of Congrong Shujing Granules. The results showed that the AHP-CRITIC mixed weighting method scientifically optimized the extraction process. To be specific, the decoction pieces should be added with the 6-fold amount of water and extracted twice, 1 h each time. After three verification tests, the average mass fractions of echinacoside, salvianolic acid B, and paeoniflorin were 0.72, 9.34, and 5.92 mg·g~(-1), respectively, and the average ointment yield was 47.18%. As verified by the AHP-CRITIC mixed weighting method and the orthogonal test, the optimized extraction process of Congrong Shujing Granules was stable and feasible and could be applied to industrial production.

Composite ecotourism potential index based on an integrated stochastic CRITIC-weighted sum method

ABSTRACT This work develops a computational platform to evaluate the ecotourism potential of tourist sites (alternatives) under multiple indicators (criteria). Such a platform is a generalized multi-attribute decision-making approach based on the integration of Criteria Importance Through Intercriteria Correlation (CRITIC) and the weighted sum method (WSM) under a stochastic environment. In this proposed approach, a randomly generated decision matrix is formed, derived from the initial judgments of decision-makers. Then, the priority weights of the criteria are obtained through CRITIC, while the WSM generates the global priority vector of alternatives. A stochastic process based on Monte Carlo simulation generates a large set of global priority vectors. The aggregation of these vectors yields the final priorities of the alternatives. Contextually, it generates a single-valued composite ecotourism potential index which provides the overall performance of the potential ecotourism sites. Evaluating 47 tourist sites under eight indicators demonstrates the proposed approach. Findings and insights are discussed.

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC-MABAC-Based Decision-Making Algorithm.

Based on the criteria importance through inter-criteria correlation (CRITIC) and the multi-attributive border approximation area comparison (MABAC), a decision-making algorithm was developed to select the optimal biocomposite material according to several conflicting attributes. Poly(lactic acid) (PLA)-based binary biocomposites containing wood waste and ternary biocomposites containing wood waste/rice husk with an overall additive content of 0, 2.5, 5, 7.5 and 10 wt.% were manufactured and evaluated for physicomechanical and wear properties. For the algorithm, the following performance attributes were considered through testing: the evaluated physical (density, water absorption), mechanical (tensile, flexural, compressive and impact) and sliding wear properties. The water absorption and strength properties were found to be the highest for unfilled PLA, while modulus performance remained the highest for 10 wt.% rice husk/wood-waste-added PLA biocomposites. The density of PLA biocomposites increased as rice husk increased, while it decreased as wood waste increased. The lowest and highest density values were recorded for 10 wt.% wood waste and rice husk/wood-waste-containing PLA biocomposites, respectively. The lowest wear was exhibited by the 5 wt.% rice husk/wood-waste-loaded PLA biocomposite. The experimental results were composition dependent and devoid of any discernible trend. Consequently, prioritizing the performance of PLA biocomposites to choose the best one among a collection of alternatives became challenging. Therefore, a decision-making algorithm, called CRITIC–MABAC, was used to select the optimal composition. The importance of attributes was determined by assigning weight using the CRITIC method, while the MABAC method was employed to assess the complete ranking of the biocomposites. The results achieved from the hybrid CRITIC–MABAC approach demonstrated that the 7.5 wt.% wood-waste-added PLA biocomposite exhibited the optimal physicomechanical and wear properties.

Assessment of third-party logistics providers by introducing a new stochastic two-phase compromise solution model with last aggregation

In the conditions of increasing governments' and consumers' awareness of environmental protection and sustainability, the enterprises have been encouraged to devote more attention to reverse logistics (RL) activities. Assessment of third-party reverse logistics providers (3PRLPs) is a crucial decision strategy for companies who choose to outsource their RL activities to third-party partners. To ensure accurate information is reflected in the decision process, it is imperative to consider uncertain information. To this aim, this paper draws attention to the selection of the most preferred RL providers using a novel comprehensive stochastic compromise solution by considering the uncertainty associated with decision variables. This paper presents a novel multi-attribute group decision-making (MAGDM) framework by integrating a new stochastic criteria importance through inter-criteria correlation (CRITIC) method and an innovative last aggregation stochastic two-phase compromise solution based on the visekriterijumsko kompromisno rangiranje (in Serbian) (VIKOR) approach to provide systematic decision support for companies to select the most preferred RL providers. In the proposed framework, the CRITIC method is utilized to calculate the priority weights of the attributes. The innovative last aggregation stochastic two-phase compromise solution is employed to evaluate decision-makers (DMs) and alternatives simultaneously. Moreover, the proposed compromise method exploits the pessimistic, most likely, and optimistic solutions to deal with stochastic uncertainty and provide DMs with more flexibility in the final evaluation. The approach is also validated by applying a sample case for selecting 3PRLPs along with sensitivity analysis on the weights of the different attributes in order to investigate the robustness of the results. Consequently, to demonstrate the reliability of the suggested approach, comparative analyses of the final ranking are conducted. The outcome of the study reveals the proposed model provides a viable, high-performance, and flexible decision-making process, by which the DMs can provide more prompt and correct judgments on the selection of the best 3PRLPs.