Order Preference Research Articles

Evacuation safety assessment in corridor-type high-rise building under fires

This study aims to elucidate the characteristics of personnel evacuation in corridor-type high-rise building under fires and to assess their evacuation performance. Utilizing Building EXODUS software, the study simulated the evacuation behavior of occupants, particularly focusing on the impact of corridor structures on the building's fire evacuation capabilities. A performance evaluation model was developed, which incorporates factors such as building conditions and natural environments. This model employs an enhanced Analytic Hierarchy Process (AHP) combined with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) analysis for more accurate assessment. The findings reveal that traditional evacuation performance assessment methods, which often overlook the impacts of corridor location, time of day, and illumination conditions, are not adequately suited for the evaluation of evacuation performance in corridor-type high-rise building under fires. Under similar external conditions, the evacuation time for buildings with connecting corridors is significantly shorter than that for conventional high-rise buildings, showing reductions of 25.0 %–34.0 % at the same time of day and 26.0 %–37.8 % under the same illumination conditions. Additionally, nighttime evacuations are 29.4 % longer than those conducted during daytime. The implementation of a layered evacuation strategy reduces evacuation time by 9.7 % compared with free evacuation methods. The enhanced model proposed in this study provides a more effective framework for assessing the evacuation performance of corridor-type high-rise buildings.

COMPARATIVE ANALYSIS OF MULTI-CRITERIA DECISION MAKING METHODS IN DETERMINING REDD+ PROJECT LOCATION

The Reducing Emissions from Deforestation and Forest Degradation (REDD+) mechanism is aimed at reducing global greenhouse gas (GHG) emissions. This study aims to develop a multi-criteria decision-making (MCDM) model specifically designed to prioritize locations for REDD + projects. The proposed research design focuses on developing a MCDM framework for determining the priority locations for projects using criteria such as climate impact reduction, contributions to local communities, and biodiversity conservation. The study utilized the Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to determine the prioritization of alternatives based on compromise solutions. The success of this research demonstrates that a systematic approach to determining priority locations can be effectively carried out using MCIM. This research is expected to aid policymakers and stakeholders in making more informed and effective decisions for environmental conservation and climate change mitigation.

Re-design methodology for an under-risk multi-business supply network

ABSTRACT Currently, several supply chains of the same business may be managed independently. This is due to different groups of clients or different marketing channels. Parallely, changes in the environment, due to unexpected situations, lead the decision-makers to modify the configuration of supply chains. Therefore, we propose a redesign methodology based on a progression of steps to study holistic synergies. This design analysis is carried out at a static and dynamic level for strategic, tactical and operational decisions. The use of qualitative and quantitative tools allows a set of solutions to be obtained, which take into account inventory levels, processing times, costs and service levels. To compare the preliminary solutions, AHP (Analytic Hierarchical Process) is combined with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). It is applied to an example of pharmaceutical distribution in South America. The results indicate an improvement of service levels and a reduction of costs from risks.

Multi-objective optimization of L-shaped fins in rectangular phase change energy storage units based on genetic algorithm

Phase change energy storage technology holds immense potential in the field of energy storage, and enhancing the efficiency of energy storage systems has long been a focal point of industry attention. This study aims to optimize the design of an L-shaped fin structure to improve the melting rate of the phase change material (PCM) within a rectangular phase change energy storage unit and enhance the overall system performance. Through the utilization of numerical simulations and the response surface method (RSM), the influence of fin design parameters - specifically, the length and thickness of the main segment and the length and thickness of the branch segment - on the energy storage per unit mass (Em) and energy storage efficiency (Pt) of the energy storage unit is evaluated. The results reveal that the length of both the main and branch segments of the L-shaped fin significantly impacts the melting rate of the PCM, whereas the thickness of these segments has a comparatively lesser effect. Additionally, when the length of the main segment reaches a sufficient value, increasing the length of the branch segment effectively addresses the challenge of PCM melting difficulties at the bottom of the rectangular phase change cavity caused by natural convection. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is employed for the multi-objective optimization of the L-shaped fin shape, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with entropy weighting is applied for decision-making to determine the optimal solution. Notably, when the weights of Em and Pt are set at 44.9 % and 55.1 % respectively, the overall performance of the energy storage unit is optimized. The Pareto-optimal decision solution exhibits a significant improvement of nearly threefold in Pt compared to the case without fins, while the reduction in Em is only 5.24 %. This design approach presents a novel perspective for determining fin parameters in phase change energy storage devices and is expected to drive advancements in phase change energy storage technology.

Targeted mechanical and energy absorption properties of 3D printed aluminium metamaterials

The potential of 3D-printed AlSi10Mg auxetic structures for diverse mechanical and energy-absorbing needs remains untapped. This article reveals a multi-criteria framework for the laser powder bed fused (L-PBF) −υ architecture considering elastic modulus (E), yield strength (σy), specific energy absorption (SEA), peak crush force (PCF) and crush force efficiency (CFE). The framework seamlessly combines trial data, multi-criteria decision-making, and performance indicators. Five auxetic structures were 3D-printed, characterised for mechanical and energy absorption traits within a 0.17–0.26 relative density range. The outcomes revealed a range of values for various parameters, including the Poisson’s ratio (−0.03 to −0.22), porosity (80.87–87.60 %), CFE (33–83 %), elastic modulus (100–632 MPa), yield strength (1.8–10 MPa), and SEA (0.5–6.8 kJ/kg). The reliability of these structures was ensured through a meticulous selection process based on an extensive literature review and empirical validation. To address the limitations of theoretical models, our work goes beyond theoretical predictions by experimentally validating these properties and integrating advanced methodologies such as the ‘analytic hierarchy process’ (AHP) and the ‘technique for order of preference by similarity to ideal solution’ (TOPSIS). This allows us to determine the best-performing auxetic architecture. The decision-making process was informed by five user-defined parameters prioritised in the order of CFE>−υ> E>σy> SEA based on their relative closeness identifying AUX5 as the best performing auxetic architecture. This study introduces an innovative method for crafting scenario-based auxetic architectures with varying performance levels based on their relative importance.

Parameter optimisation of marine four-stroke engine EGR systems using RSM and NSGA-III

ABSTRACT To reduce NOx emission of marine engines, an engine parameter optimisation method combining response surface method (RSM), Bayesian neural network and non-dominated sequence genetic algorithm (NSGA-III) is proposed. First, a simulation model of a marine four-stroke engine with an external exhaust gas recirculation (EGR) system was established and validated in AVL-BOOST software. Then, the five parameters of EGR valve opening, ignition advance angle, compression ratio (CR), intake valve opening (IVO) and exhaust valve closing (EVC) are selected as the decision parameters, and the three parameters of engine power, brake specific fuel consumption (BSFC) and NOx emission are taken as the target parameters to be optimised. In Design-Expert software, the response surface model is established by using the experimental design approach of central composite design (CCD), and the impact of decision parameters on target parameters are discussed by using response surface analysis. NSGA-III was used for the multi-objective optimisation of parameters, and the accuracy of NSGA-III was improved by the Bayesian neural network. The optimal solution is selected and verified by the technique for order of preference by similarity to the ideal solution (TOPSIS) approach. The results show that when the EGR valve opening is 47.5%, ignition advance angle is −18.99°CA, CR is 14.995, IVO is 15.27°CA and EVC is 7.68°CA, compared with the standard setting, the optimised power is increased by 4.17%, BSFC is reduced by 4.27%, and NOx emissions are reduced by 12.37%. Therefore, the combination of the Bayesian neural network and NSGA-III multi-objective optimisation can improve engine performance while reducing fuel consumption and NOx emissions.

A novel mass integration cogeneration system: Working fluid selection model and multi-criteria decision analysis

As the globe faces major difficulties such as climate change, environmental pollution, and limited energy resources, improving energy efficiency and decreasing the environmental effects of energy systems have become research priorities. A novel mass integration cogeneration (MICHP) system is proposed in this paper. In addition, a mathematical model relating the basic physical properties of the working fluid to the system's performance has been developed. R1233zd(E), R1224yd(Z), R601a, R245fa, and acetone have been chosen to be applied to the MICHP to confirm the model's accuracy. By utilizing the technique for order preference by similarity to an ideal solution, the entropy weight method, and energy, exergy, economic, and environmental analyses of the MICHP, the general efficiency of the working fluid is further explored. The results show that the intermediate heat exchanger is the largest exergy destruction equipment in the system. R601a has the highest return on investment of 0.53, and the annual operating cost is found to be proportional to the latent heat of the working fluid. R1233zd(E) has the lowest environmental impact, with a total equivalent warming impact of −4.89 × 106 kgCO2eq. The multi-criteria decision analysis of the working fluid agrees with the order of the proposed mathematical model's results, proving the model's accuracy. Consequently, the mathematical model can provide a helpful technique guide for working fluid selection in cogeneration systems.

Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution: A Hybrid Method for Assessing Vegetation Management Strategies under Electricity Distribution Lines to Prevent Deforestation Based on Ecosystem Service Criteria

This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended.

An integrated multi-criteria decision making approach for selecting the starting location of railroad projects

PurposeThe successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.Design/methodology/approachBased on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.FindingsThe study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.Originality/valueThis study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

Multi-objective optimization for economic load distribution and emission reduction with wind energy integration

In today’s power systems operation, the dual challenge of optimizing economic load distribution while minimizing power plant emissions is pivotal. This challenge is accentuated by the pressing environmental concerns and the finite nature of fossil fuel reserves. In this context, renewable energy sources, notably wind power, have emerged as indispensable alternatives due to their cost-effectiveness and environmental compatibility. However, the inherent variability of wind velocity introduces uncertainty into power output, necessitating innovative approaches to address this complexity. To tackle this issue, we propose a scenario-based probabilistic approach that dynamically considers the slope rate of power output. By leveraging the Blue Whale multi-objective algorithm and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) criterion, we identify significant solutions from the Pareto set across a spectrum of scenarios. Our method is rigorously evaluated across various systems and operational contexts, revealing its superiority over alternative algorithms. Specifically, our approach achieves lower objective function values, reduced standard deviation, and superior overall performance. These findings underscore the critical importance of efficient power system management in balancing environmental sustainability and economic viability. By embracing innovative methodologies, we can navigate the evolving energy landscape and contribute towards a more sustainable future.

Experimental investigation of machine vibration rate and machine sound level in MQL turning of AISI 1525 steel employing mango oil as lubricant

AbstractThe detrimental effects of mineral oil on the environment and machine shops have led to a surge in the usage of vegetable oil as cutting fluid. The most popular vegetable oil lubricants are edible, and they have a lot of potential to rival human consumption eventually. The study examined using mango oil, an inedible vegetable oil, as a lubricant during AISI 1525 steel turning with tungsten inserts. The best machining parameters were ultimately found using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) after the experimental studies were analyzed using the Taguchi process. Surface and contour plots were employed to investigate how different cutting settings affected the rate of vibration and sound level of the machine. Mango kernel seed oil outperformed its mineral oil counterparts by 2.3% and 57.7%, respectively, in terms of machine vibration rate and machine sound level. Moreover, feed rate (0.10 mm/rev), depth of cut (0.75 mm), and spindle speed (350 rev/min) are the ideal cutting settings to reduce machine vibrations and sound intensity. Mango oil holds significant potential as a substitute for cutting fluid derived from petroleum. The significance of this research is to formulate lubricants for industrial use that are more ecologically friendly and sustainable.

Dynamic Evaluation of Road Network Resilience to Traffic Accidents: An Emergency Management Perspective for Sustainable Cities in China

When assessing road network resilience, emergency management behavior should be considered, as this represents the road network’s capacity to adapt to and recover from traffic accidents. Given the timeliness and variability of emergency management behavior, deterministic approaches seem inadequate to represent real road network performance. Thus, this paper innovatively designs an emergency management perspective-based dynamic evaluation method of road network resilience to traffic accidents. Firstly, based on four stages of emergency management, a road network resilience evaluation index system encompassing resilience capabilities, resilience attributes and traffic accident emergency management ability indicators is constructed. Afterwards, the gray relational technique for order preference by similarity to the ideal solution (GRA-TOPSIS) evaluation method based on combination weighting, which integrates factor analysis with hesitant intuitionistic fuzzy expert scoring, is designed to quantify resilience. Finally, the obstacle degree model is utilized for identifying resilience constraints as the input of a long short-term memory (LSTM) model to predict the resilience variation trend. The fast road network of Shanghai in China is adopted as a case study, and the results indicate that road network resilience embodies significant spatial distribution characteristics. Road length, number of tractors, perception and response and disposal time of traffic accidents cast notable effects on resilience. Additionally, some roads are forecast to show descending resilience. The proposed method is valuable for helping policymakers identify current and potential vulnerable roads and to formulate proposals to effectively improve the resilience of urban agglomerations and promote sustainable cities.

Assessment of crop suitability analysis using AHP-TOPSIS and geospatial techniques: A case study of Krishnagiri District, India

The cropped area of Krishnagiri district is about 37 % of the total geographical area, and due to the utilization of non-agricultural purposes (8.2% of agricultural land) it is mandatory to increase the agricultural production to feed the people. So, this study has been planned to find the agricultural suitability and crop suitability of the Krishnagiri district using AHP-TOPSIS and GIS techniques. In this study, three main limiting factors, such as climate, soil, and topographical parameters (Annual average rainfall, Annual average temperature, slope, pH, Electrical conductivity (EC), Soil organic carbon (SOC), available NPK, and soil texture), were used as criteria for the suitability of crops. All criteria maps were generated using Arc info 10.4 software. The AHP used to estimate the weight and TOPSIS (Technique for Order of Preference by Similarity to an Ideal Solution) approach was assign the rank of each criterion for the suitability analysis. Based on the relative closeness to the ideal solution(pi), the generated crop suitability maps were categorized into five classes, namely “highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (N)”. The results revealed that, crop suitability maps finds that that maize, field bean, cluster bean, groundnut, pomegranate, lemon, and lemongrass were highly suitable for the Krishnagiri district. Rice, cotton, sugarcane, sunflower, and cashewnut were moderately suitable, and vanilla was unsuitable. This study will guide farm managers and land policymakers in making decisions and assist farmers in selecting crops that are highly suitable for achieving potential yield and profit.

Evaluating music education interventions for mental health in Chinese university student: a dual fuzzy analytic method

This study addresses the growing anxiety and depression among Chinese university students by evaluating and ranking music education strategies to alleviate these issues. We integrates Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FAHP was utilized to determine the weight of factors such as academic pressures, social relationships, and cultural norms, while fuzzy TOPSIS ranked the effectiveness of music education interventions based on these weights. The results revealed that ‘Mental health stigma’ and ‘Academic Pressures and Rigidity’ are among the highest weighted factors, significantly impacting student anxiety. ‘Music Appreciation and Music-Based Self-Care’ emerged as the most effective strategy. These results highlight the importance of direct involvement in music-related activities for improving student mental health.

CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models

Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory (CNN-LSTM)—based on multi-satellite and reanalysis data to monitor CO2 emissions resulting from groundwater storage depletion in South Korea. The data-driven models developed in this study provided reasonably accurate predictions compared with in situ groundwater storage anomaly (GWSA) observations, identifying relatively high groundwater storage depletion levels in several regions over the past decade. For each administrative region exhibiting a decreasing groundwater storage trend, the corresponding CO2 emissions were quantified based on the predicted GWSA and respective bicarbonate concentrations. For 2008–2019, XGBoost and CNN-LSTM estimated CO2 emissions to be 0.216 and 0.202 MMTCO2/year, respectively. Furthermore, groundwater storage depletion vulnerability was assessed using the entropy weight method and technique for order of preference by similarity to ideal solution (TOPSIS) to identify hotspots with a heightened potential risk of CO2 emissions. Western South Korean regions were particularly classified as high or very high regions and susceptible to groundwater storage depletion-associated CO2 emissions. This study provides a foundation for developing countermeasures to mitigate accelerating groundwater storage depletion and the consequent rise in CO2 emissions.

Wear behaviour analysis of thermo-mechanically processed AA7075 and AA7075/SiC/Graphite composite

The current work focuses on investigating the tribological performance of AA7075 alloy and AA7075 hybrid composite with SiC and graphite reinforcements of 2 and 0.5 wt percentages, respectively. The alloy and hybrid composite are fabricated through stir casting and thermo-mechanically processed through hot rolling routes. Dry sliding wear characterization has been carried out using a pin-on-disc wear testing setup for alloy and composite under various processing conditions using experiments designed according to Taguchi's L9 orthogonal array. The technique for order preference by similarity to ideal solution (TOPSIS) method has been adopted to achieve a single multi-response index (MRI) considering the minimization of multiple objective functions for specific wear rate (SWR) and coefficient of friction (COF). The analysis of variance (ANOVA) results of the MRI indicated that sample processing condition and load were the most significant parameters. The wear mechanism at different experimental conditions was studied using Scanning electron microscope (SEM) micrographs, energy dispersive spectroscopy analysis and 3D surface profile of wear track. The AA7075 composites are found to be comparatively good in all experimental conditions compared to the base alloy due to load-bearing SiC particles and solid lubricant graphite as reinforcing elements. The mechanically mixed layer (MML) formation in hybrid composites helped achieve good tribological properties. The AA7075 alloy and hybrid composites cross rolled at 350 °C yielded better results than other samples. The wear mechanism was abrasive and adhesive at low temperature and low load conditions in both alloy and hybrid composites. Whereas, at higher temperatures and high loads oxidative wear mechanism with delamination was prevalent. The transition of SWR from mild to severe value occurs at 250 °C in all experimental cases. The specific wear rate was improved by 85.64 % between worst and best conditions, i.e., annealed alloy and cross rolled composite.

Priority-based two-phase method for hierarchical service composition allocation in cloud manufacturing

Manufacturing service composition (MSC) is an essential issue in cloud manufacturing, which streamlines complex manufacturing tasks into manageable subtasks and integrates distributed services to enhance task completion. Existing studies allocate services for subtasks with maximizing quality of service (QoS) simultaneously, assuming that all subtasks are of equal importance. However, different subtasks hold varied significance and priorities. One rational method is to prioritize the allocation of premium or scarce services to important subtasks. Therefore, this study proposes a two-phase, subtask priority-based approach for the hierarchical allocation of the MSC. The initial phase applies a multi-attribute decision-making method based on complex networks, the Enhanced Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS-EK), to assess subtask importance. The TOPSIS-EK method ascertains subtask importance, delineating the subtasks into Key Manufacturing Subtasks (KMTs) and Ordinary Manufacturing Subtasks (OMTs). The second phase uses bilevel optimization for the hierarchical allocation of the MSC to KMTs and OMTs, respectively. A hybrid Particle Swarm Optimization and Genetic Algorithm with Chaos-sequence and Inheritance (PSOGA-CI) is developed to solve the model. The proposed approach is validated with a case on the production of an airplane engine turbine rotor blade.

Evaluation of Spare Parts Support Capacity of Civil Aircrafts Based on Type-2 Hesitant Pythagorean Fuzzy Sets and Improved Technique for Order Preference by Similarity to Ideal Solution

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies.

Sustainable Plastic Waste Management in Thanjavur District: An Evaluation Using Fuzzy AHP and Neutrosophic TOPSIS

Introduction: The amount of plastic garbage has increased in tandem with the steady growth in the use of plastic products worldwide. Since plastics are lightweight, inexpensive, and durable, they are highly appreciated in a variety of industries, including electronics, building, packaging, healthcare, and agriculture). Plastic's cost, adaptability, resistance to corrosion, durability, and ease of production all contribute to its rising demand.With changes to the Plastic Waste Management Rules, India has made managing single-use plastics a top priority. Objectives: The research provides an extensive examination of the methods used to manage plastic garbage in Thanjavur's rural villages. Examining household behaviors and waste production trends, it reveals a wide range of plastic kinds, including polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polyethylene terephthalate (PET). Methods: The research uses a Multi-Criteria Decision Making (MCDM) approach that combines the Neutrosophic Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for alternative ranking with the Fuzzy Analytic Hierarchy Process (FAHP) for criteria weighting. Although it uses fuzzy logic to address ambiguity and uncertainty in decision-making processes, the Fuzzy AHP approach is applied in a manner similar to that of AHP. Results: Egestas diam in arcu cursus euismod quis viverra nibh. Convallis aenean et tortor at risus viverra. Sit amet justo donec enim diam. Sem et tortor consequat id. Purus gravida quis blandit turpis. Consectetur adipiscing elit duis tristique sollicitudin nibh sit amet commodo. Eget duis at tellus at urna condimentum mattis pellentesque. Auctor elit sed vulputate mi sit amet. Conclusions: This study determines that, among the available options, Polyethylene Terephthalate (PET) is the best option by applying the fuzzy analytic hierarchy process (Fuzzy AHP) to balance these criteria. PET exhibits potential because of its economic and environmental advantages, but its effective application necessitates customized strategies that take into account the infrastructure constraints and socioeconomic characteristics of the local area. Programs for raising community knowledge and participation are crucial in promoting sustainable waste management techniques, as the study emphasizes.

Emotional design and evaluation of children’s furniture based on AHP-TOPSIS

Children’s physical and mental health development is highly related to furniture and integrating the emotional design concept into children’s furniture design helps cultivate children’s personalities and promote healthy growth. The model framework of children’s furniture emotional design was constructed from the instinctive level, behavioral level, and reflective level. The comprehensive weight of the design factors was calculated using the Analytic Hierarchy Process (AHP), and three schemes were designed according to the priority of the design factors. The evaluation matrix was constructed by combining the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the distances of each scheme from the positive and negative ideal solutions were calculated, and the optimal scheme was selected according to the relative proximity ranking of the three schemes. The experimental results verified the importance of the reflective level in the three levels of the emotional design theory and emphasized the key role of emotional design in children’s furniture. Therefore, the evaluation system based on AHP and TOPSIS is feasible, which can effectively reduce the influence of subjective factors, make the design decision more scientific, and provide a new reference approach for furniture design.