Compromise Solution Research Articles

The effect of main dimensions on the preliminary design of motor yachts

The design process of motor yachts mainly relies on the experience of designers, who have confidence in the knowledge acquired from designing units with similar hull-form characteristics. However, once a new concept needs to be developed, the acquired experience on a standard platform is no longer sufficient to achieve in a short time a successful design. The design of a motor yacht implies considering multiple aspects of ship hydrodynamics: resistance, propulsion, seakeeping, and manoeuvring. Such factors have been widely discussed individually on different kinds of ships, but an appropriate joint investigation of hulls like motor yachts is missing in the open literature. Therefore, the present paper intends to cover this gap, providing guidelines for the design of motor yachts in a length range between 20 and 40 meters. As a preliminary study, a series of 15 yacht hulls has been developed, starting from a reference hull form. Seakeeping, manoeuvring, and propulsive performances have been evaluated at a reference environmental condition and speeds according to the ISO 22834:2022 guidelines. Such calculations allow for developing response surfaces of the hydrodynamic properties for the series of yachts as a function of the hull's main dimensions. As a final result, the obtained responses allow for identifying the best compromise solutions for the main dimension selection of a new motor yacht in the length range of 20-40 meters.

Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an increasing degree of distribution. The designs leverage the execution of the GA in the infrastructure devices themselves by dealing with the specific features of this domain: constrained resources and wide geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the application placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared against a control case, representing a traditional centralized version of this GA algorithm, evaluating solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. The second design demonstrates the highest overall efficiency in optimization performance and network cost. Finally, the proposal with a distributed population that only interchanges solutions between the workers’ neighbors achieves the lowest network load but with compromised solution quality.

Evaluation of Industry 4.0 strategies for digital transformation in the automotive manufacturing industry using an integrated fuzzy decision-making model

The current paper proposes a methodological frame to identify the best Industry 4.0 (I4) strategy for the automotive manufacturing industry as a roadmap for companies to make a more straightforward digital transformation. We noticed a critical research gap when we performed an extensive literature review. Although there are many studies available that assess the readiness of companies for I4, only a few deal with decision-making in terms of sustainable development, and none have applied that to the automotive industry. Practitioners can apply the proposed model to determine the strategy based on technological developments in I4. The current paper proposes an extended version of the combined compromise solution approach with the help of the picture fuzzy sets. According to the paper's results, by using the proposed model, developing a new business model based on technological improvements is the best strategy for the digital transformation of the automotive industry. Also, the lack of research and development implementations on I4 implementation is the most influential factor. Hence, practitioners in the automotive industry know the significance of research and development in reaching successful results for digital transformation. The acquired outcomes of the paper show that developing technology-based new business models is the best strategy for the automotive manufacturing industry concerning digital transformation instead of accommodating traditional business models and high technology utilization.

Multi‐objective optimization of a Fibonacci phyllotaxis micro pin‐fin heat sink

AbstractThere are still significant technical challenges associated with thermal management of electronic devices such as microprocessors. To improve heat dissipation performance of integrated circuits, a new Fibonacci phyllotaxis design of circular micropin fin heat sinks has been developed. To minimize both chip temperature and pumping power, a multi‐objective optimization technique was employed. The effect of design parameters such as phyllotaxis coefficient, pin fin diameter, and pin fin height on response parameters was numerically investigated using the full factorial design of the experiment. Artificial neural network was coupled with MO‐Jaya, to arrive at a Pareto frontier of optimal compromise solutions. The optimal set of design variables were found to be a height of 300 μm, a diameter of 122.6 μm, and a phyllotaxis coefficient of 130 μm with an inlet velocity of coolant 2.263 m/s. The selected optimum design was then investigated numerically, and the outcomes were compared to those predicted by the MO‐Jaya algorithm. The final confirmed response variables were a maximum temperature of 51.6°C and a pumping power of 0.191 W. The results show that the Fibonacci phyllotaxis structure of the micro pin fin heat sink has better heat‐dissipating performance.

Optimizing Microwave-Assisted Extraction from Levisticum officinale WDJ Koch Roots Using Pareto Optimal Solutions

The current research trend is not only focused on advanced techniques to intensify the extraction of bioactive compounds from plants, but also on the optimization process. The objective of this work was the implementation of the multiple criteria analysis using navigation on Pareto sets to determine the optimal parameters for the microwave-assisted extraction of Levisticum officinale WDJ Koch roots in order to obtain the maximum efficiency of the antioxidant potential of the extracts. The optimized parameters were extraction time, microwave power, and plant biomass/solvent ratio, while the evaluation criteria were based on the total phenols, flavonoids, reducing sugars, and antioxidant capacity. It was shown that the process parameters analyzed, i.e., biomass/solvent ratio, process time, and microwave power, determined the extraction efficiency of total phenolic content (TPC). A different observation was made for the analysis of total flavonoid content (TFC) and total antioxidant potential (TAA). Compared to the assessment of TFC and TAA, a completely different trend was observed for the analysis of total reducing sugars (RSC). Sets of Pareto optimal, compromise, and preferred solutions were identified that will maximize the efficiency of the extraction of bioactive compounds from biomass. Due to the determined number of Pareto optimal solutions, an approach related to the introduction of preferences in the optimization procedure was applied. It was shown that for a satisfactory level of bioactive compounds, extraction should be carried out at a maximum microwave power of 750 W. Preferred solutions were obtained for root biomass to water ratios ranging from 0.0536 g/mL to 0.0679 g/mL. The preferred optimal time for microwave-assisted water extraction ranged from 64.2857 to 85.7143 s.

Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0

Abstract. The optimization of aircraft trajectories involves balancing operating costs and climate impact, which are often conflicting objectives. To achieve compromised optimal solutions, higher-level information such as preferences of decision-makers must be taken into account. This paper introduces the SolFinder 1.0 module, a decision-making tool designed to identify eco-efficient aircraft trajectories, which allow for the reduction of the flight's climate impact with limited cost penalties compared to cost-optimal solutions. SolFinder 1.0 offers flexible decision-making options that allow users to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under atmospheric conditions simulated by the ECHAM/MESSy Atmospheric Chemistry model. This paper focuses on the ability of the module to identify eco-efficient trajectories while solving a bi-objective optimization problem that minimizes climate impact and operating costs. SolFinder 1.0 enables users to explore trajectory properties at varying locations of the Pareto fronts without prior knowledge of the problem results and to identify solutions that limit the cost of reducing the climate impact of a single flight.

The use of AI to uncover the supply chain dynamics of the primary sector: Building resilience in the food supply chain

Despite the fact that artificial intelligence (AI) techniques are increasingly influencing economic and societal dynamics, certain aspects of AI's role in structural change, such as enhancing the resilience of supply chains, have not been thoroughly explored in academic research. Thus, we first identify thirteen variables that are influential in shaping the structural transformation of food supply chains. These variables are chosen based on the financial, technological, social, and organizational challenges to building resilience within the food supply chain. Then, we generate an interval-valued T-spherical fuzzy Combined Compromise Solution (CoCoSo)'B model to uncover the role of AI in the supply chain dynamics and structural change of the food sector. After that, we explore the role of AI in alleviating the factors in building resilience within the food supply chain through the generated decision model and expert interview. The result also reveals that the AI advantageAd2“Enhancing information sharing among nodes” (1.983) has the most potential to influence organizational adoption behavior. The outcomes of the present study can provide a new decision-support technique for uncovering the effects of AI on supply chain dynamics and structural change in the food sector.

Evaluation of cutting parameters during dry turning of AISI 304 stainless steel and optimization through a modified WPCA approach

The main objective of this study is to analyze how input parameters such as cutting speed ( Vc), depth of cut ( ap), feed per revolution ( f), and tool nose radius ( r) influence the performance of dry turning AISI 304 stainless steel. Performance evaluation includes cutting force components ( Fx, Fy, and Fz), surface roughness (SR), material removal rate (MRR), cutting power ( Cp), and cutting temperature ( T). Experiments are planned using the Taguchi L18 experimental design, and a coated carbide tool (GC 2025) is employed. ANOVA revealed that the most influential factors on SR are f and r, contributing 52.40% and 26.54%, respectively. Axial and tangential cutting force components are influenced by ap and f, with contributions of 72.99% and 5.93% for Fx and 81.57% and 13.38% for Fz, respectively. The radial component is influenced by ap (85.74%). Vc, f, and ap all contribute to cutting power, with contributions of 55.13%, 24.35%, and 10.74%, respectively. Finally, ap and the interaction f*ap contribute 45.18% and 8.81%, respectively, to the temperature in the cutting zone. This article introduces a novel multi-objective optimization method based on weighted principal component analysis. This method distinguishes itself by its ability to address challenges related to data matrix normalization, the ambiguity of eigenvector signs, and the issue of negative signs in Taguchi’s philosophy. The results of the new method, the desirability function, and the classical WPCA method are compared. The results show that the new method suggests a compromise solution that is similar to the desirability function and better than the classical WPCA’s suggestion.

CONSERVATIVES OF THE KINGDOM OF POLAND IN 1914-1916

Since the birth of independent Poland in 1918, historians and publicists have been arguing about who deserves the main credit for this most signifi cant event in the modern history of the Polish nation. In the period between the world wars, the dispute for supremacy was fought between J. Piłsudski and R. Dmowski, in socialist Poland they were supplanted by the Russian October Revolution, but already in the 1980s the debate resumed with renewed vigor. Th e compromise solution proposed by Prof. S. Kieniewicz was not widely s upported. Since the 1990s, there has been a steady return to pre-war approaches to the problem. Th us, political forces other than the pilsudniks and national democrats, led by R. Dmowski, once again remain outside the fi eld of vision of the authors writing on this topic. Among them are conservatives of the Kingdom of Poland, through whose eff orts, from September 1917 to 14 November 1918, the construction of the state institutions of the Kingdom of Poland was underway with the consent of the Central Powers. Th e Kingdom was formed by the act of Emperors Wilhelm II Hohenzollern and Franz Joseph Habsburg of 5 November 1916. Th e contribution of the conservatives is clearly underestimated; moreover, they alone are accused of pursuing a conciliatory (Ugodian) policy towards the Russian, fi rst, and then the German and Austro-Hungarian authorities, although all other political parties in the Polish lands, with the exception of the social democrats of the Kingdom of Poland and Lithuania, also took conciliatory positions at the beginning of the 20th century. Moreover, it was these parties, which were united under the general name of “activists”, who responded to the manifesto of 5 November and sent their representatives to the Provisional State Council of the Polish Kingdom. Th e conservatives, on the other hand, took a wait-and-see attitude in November 1916 and refused political cooperation with the German and Austro-Hungarian occupation authorities until September 1917.

An Online Hotel Selection Method With Three-Dimensional Analysis of Reviews' Helpfulness

The multi-attribute decision-making method based on online reviews has been widely used in addressing the hotel selection problem. However, due to information overload and the presence of fake reviews, traditional hotel selection methods that rely solely on unverified review analysis can affect the outcome of hotel selections. In this study, a novel three-dimensional helpful review analysis model based multi-attribute decision-making approach for hotel selection is established. Firstly, a new three-dimensional helpful review analysis model that effectively filters out sentiment inconsistency reviews, topic inconsistency reviews, and reviews from invalid sources is proposed. Secondly, this study employs TF-IDF and LDA to extract attributes for hotel selection. We further utilize BERT to extract sentiment level for each attribute. Then, a ranking result for alternative hotels is obtained using a combination compromise solution method (CoCoSo). Finally, we demonstrate its effectiveness and feasibility through a case study of selecting the optimal hotel from TripAdvisor.com.

Challenges in adopting blockchain technology in supply chain management: a too far fetched idea?

PurposeRecent technological advancements, often linked to Industry 4.0, require organizations to be more agile and innovative. Blockchain technology (BT) holds immense potential in driving organizations to achieve efficiency and transparency in supply chains. However, there exist some insurmountable challenges associated with the adoption of BT in organizational supply chains (SC). This paper attempts to categorically identify and systematize the most influential challenges in the implementation of BT in SC.Design/methodology/approachThis study resorts to an extensive literature review and consultations with experts in the field of supply chain management (SCM), information technology and academia to identify, categorize and prioritize the major challenges using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Combined Compromise Solution method (CoCoSo).FindingsThe top three classes of challenges revealed in this study are privacy challenges (PC), infrastructure challenges (IC) and transparency challenges (TC). Maintaining a balance between data openness and secrecy and rectification of incorrect/erroneous input are the top two challenges in the PC category, integration of BT with sustainable practices and ensuring legitimacy are the top two challenges in the IC category, and proper and correct information sharing in organizations was the top most challenge in the TC category.Originality/valueFuture scholars and industry professionals will be guided by the importance of the challenges identified in this study to develop an economical and logical approach for integrating BT to increase the efficiency and outcome of supply chains across several industrial sectors.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Optimal design of an LCC-S WPT3 Z1 SAE J2954 compliant system, using NSGA-II with nested genetic algorithms for simultaneous local optimization

Wireless Power Transfer (WPT) for electric vehicles is one of the most promising methods that, given its advantages, will drive the deployment of electric vehicles. This paper presents a mathematical optimization method applied to the complete design of an LCC-S WPT3 Z1 11 kW system that complies with the SAE J2954 standard (Wireless Power Transfer for Light-Duty Plug-in/Electric Vehicles and Alignment Methodology, 2020). A design method based on three phases is proposed, allowing the complete inductor system, including ferrites shielding and compensation circuit components, to function in any relative primary and secondary position. In Phase 1, a multi-objective NSGA-II algorithm is designed, utilizing three nested genetic algorithms. The goal is simultaneously searching for the local optimum between the primary and secondary systems in three positions. This is achieved by modeling the circuit’s electrical and electromagnetic parameters with equations, enabling an iterative process with reduced computational time. The NSGA-II algorithm yields three scenarios: primary copper volume minimization, secondary copper volume minimization, and a compromise solution that optimizes the total volume. The result is then modeled in Phase 2 using a 3D finite element program that includes ferrite and optimal shielding, obtaining the values of inductances and mutual inductance in the three positions, as well as design data for manufacturing. This result is introduced in Phase 3 to optimize compensation circuit components using a second NSGA-II algorithm with three nested genetic algorithms. Again, three scenarios are obtained based on the desired system behavior and the optimal cost of the components. The result is validated through simulation with Matlab-Simulink and experimentally using a prototype constructed for this purpose.

Multi-objective trajectory planning for industrial robots using a hybrid optimization approach

Abstract In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard multi-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables. Then, in the second phase, a global search is performed using a population-based search metaheuristic in order to build a first approximation of the Pareto front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in order to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direct search method to enhance the quality of compromise solutions and to converge toward the true optimal PF. By combining different optimization techniques, we intend not only to improve the overall search mechanism of the optimization strategy but also the resulting hybrid algorithm should keep the robustness of the population-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flexible, and it can be implemented in different ways according to the applied techniques in the different phases. In this paper, we illustrate the efficiency of the hybrid framework by considering different techniques available in various numerical optimization libraries which are combined judiciously and tested on various case studies.

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Multicriteria group decision making for prioritizing IoT risk factors with linear diophantine fuzzy sets and MARCOS method

In a dynamic world of technological advances, the Internet of Things (IoT) is a transformational and widespread force that has revolutionized the way we communicate with our surroundings and regulate our environments. It offers several advantages but also introduces inherent risks. In this study, we provide a comprehensive analysis of the risks associated with IoT and employ the effectiveness of a Linear Diophantine Fuzzy Set to rank the risk factors. Because of the significant uncertainties frequently present in IoT contexts, the use of a fuzzy framework is invaluable in discerning and addressing these risks. The primary contribution is to employ the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method and linear diophantine fuzzy sets to propose a multi-criteria group decision-making method (MCGDM) for ranking attributes to facilitate risk prioritization, enabling consumers to determine the crucial hazards in their IoT systems. Furthermore, we implement a comparative study and a sensitivity analysis to demonstrate the robustness of our proposed methodology. The insights obtained from our research not only improve the awareness of IoT hazards but also enable organizations and individuals to make informed decisions when navigating IoT fields. By proactively addressing these risks, we endorse the development and secure deployment of IoT technology.

Regional Evaluation Study of VFTO Interference to Secondary Side Cables Based on Cloud Model and MARCOS.

With the advent of the data era, most power secondary side equipment tends to be digitized. The power system needs more accurate numerical results to further improve its operating efficiency. Therefore, it is important to study the electromagnetic interferences of very fast transient overvoltage (VFTO) generated by gas-insulated switchgear (GIS). To protect the secondary side cable from interferences, the secondary side cable is wrapped with an outer shield and the shield is grounded. When the interference of VFTO comes, it will couple the interference current and interference voltage on the shield of the cable. By grounding, the interference is greatly discharged. However, due to the grounding resistance, there will be a potential difference between the grounding points at the two ends of the shield of the cable. This causes a corresponding interference current to flow through the shield, which will affect the transmission of signals inside the cable. In the actual substation, the resistivity of the soil, the ambient temperature and humidity of the area, and so on will have impacts on the grounding resistance. In addition, the irregularity of the cable arrangement and the time of the use of the cable will have impacts on the signal transmission of the cable. Based on the abovementioned issues, this article proposed a comprehensive assessment method based on the combination of the cloud model and measurement of alternatives and ranking according to compromise solution (MARCOS). The method brings the cloud model into MARCOS by the algorithm of the contribution of the cloud droplets. It overcomes the difficulty of cloud model quantification. By comparing the results of the proposed method with the actual conditions at the substation and the results of the common MARCOS assessment method, the validity of the method is verified, and a reference scheme is provided for substation optimization.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

ObjectiveThe objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. MethodsIt integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. ResultsThe findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. ConclusionsTo ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Sistem Pendukung Keputusan Rekomendasi Hotel Bintang Tiga Menggunakan Kombinasi Entropy dan Combine Compromise Solution

Three Star Hotels are lodging places that offer the perfect balance between comfort, adequate facilities, and affordable prices. With a friendly atmosphere and professional service, the hotel welcomes guests from various backgrounds warmly. One of the problems in choosing a Three Star Hotel is confusion due to variations in quality and facilities among hotels that have similar ratings. Although they share the same categories, the standards and services offered can vary greatly. This can make potential guests find it difficult to choose the right hotel that suits their preferences and needs. In addition, some hotels may not meet guest expectations due to issues such as poor cleanliness or facilities that do not function properly, which can generate dissatisfaction. The combination of Entropy weighting and the Combine Compromise Solution method can be a powerful approach in providing three-star hotel recommendations to potential guests. By combining these two methods, it can produce more informed and objective three-star hotel recommendations. Entropy weighting helps in assessing the relative importance of each criterion, while the Combine Compromise Solution allows us to reach a compromise solution that blends different preferences and criteria. The result is recommendations that are more accurate and tailored to potential guests' needs and preferences. The recommendation results showed that AN Hotel with a value of 1,782 got 1st place, AL Hotel with 1.271 got 2nd place, and YN Hotel with 1,145 got 3rd rank.

SIMULARE NUMERICĂ A INFLUENȚEI UNOR PARAMETRI GEOMETRICI ASUPRA CALITATII DECONTAMINARII UNUI RECEPTOR DE TIP NON-LAMBERTIAN, UTILIZAND O MATRICE DE LED-uri UV-C

The ultraviolet (UV) irradiation has been studied and used in the recent decades as a mean to inactivate various potentially harmful microorganisms, being considered an effective treatment that could limit or even avoid the use of chemical disinfectants. Within the wavelength spectrum of UV radiation, the UV-C radiation wavelength ranging between 200 and 280 nm is considered lethal to most types of microorganisms. In this paper it was studied the variation of the distribution of radiation's intensity generated by a matrix of 25 UV-C LEDs (5 x 5), (assimilated to the LED panel above a stationary conveyor), on a spherical surface (assimilated to a berry) positioned in reprezentative locations below the matrix. A fruit located under the irradiation matrix receives the strongest radiation from the LED located at the smallest distance from it, but is influenced, as a result of the superposition principle, to a lesser extent by the other LEDs within the network. It has been found that for a too small distance between the matrix of LEDs and the conveyor, the radiation dose is uneven on the surface of fruit, and by increasing this distance a radiation distribution much more uniform is obtained, but at the expense of a decrease in its intensity and an increase in the time required for irradiation, implicitly leading to an increase in operating costs. In conclusion, for the most efficient operation of the equipment, a compromise solution must be chosen.