Objective Weights Research Articles

Factors Driving Social Capital Participation in Urban Green Development: A Case Study on Green Renovation of Old Residential Communities Under Urban Renewal in China

Urban green development is crucial for citizen well-being and serves as a key pillar of sustainable development strategies. Collaborative governance mechanisms help address management, technical, and financial challenges in urban green development. The renovation of old residential communities within the context of urban renewal is becoming a focal point in the development of Chinese cities. To promote green development in the renovation of old residential communities in China, this study introduces the collaborative governance mechanism of government–social capital cooperation and identifies the factors influencing social capital participation in green development. A hybrid approach is proposed, combining grounded theory, IGAHP subjective weighting, CRITIC objective weighting, game theory, and ISM. This approach is applied to identify, evaluate, and analyze the factors driving social capital participation in the green renovation of old residential communities. The results indicate that government incentives and constraints, policy support, the costs and benefits of implementing green renovation, the development of green renovation technologies, and construction technical abilities significantly influence the decision-making of social capital. The findings provide theoretical support for decision-making by governments and social capital in participating in the green renovation of old residential communities and offer a methodological reference for analyzing social capital participation in other urban green development projects and for further policy formulation. Future research should focus on examining social capital involvement in other types of urban green development projects.

An Integrated Two-Step Optimization Model and Aggregative Multi-Criteria Approach for Establishing Sustainable Tram Transportation Plan

The choice of the most appropriate sustainable scheme for the organization of tram transportation in cities is of great importance for tram operators, for users of transportation services, and for the protection of the environment from harmful emissions. This study aims to propose a methodology for formulating a tram transportation plan considering technological, environmental, economic, and social indicators. The variant schemes represent the routes of a tram in the tram network. The methodology includes four stages. The first stage involves the determination of variant schemes for a transportation plan of service with trams. In the second stage, a two-step optimization model is proposed to determine the number and trams by types for each tram route for each variant scheme, and also to establish the distribution of trams by depots. The third stage includes ranking the variant schemes by applying the sequential interactive model for urban systems (SIMUS) multi-criteria method. Eleven quantitative and qualitative criteria for evaluating the tram transportation plan were introduced. A verification of the results is performed in the fourth stage. For this purpose, a comparison of the preference ranking organization method for enrichment of evaluations (PROMETHEE) method and the technique for order of preference by similarity to ideal solution (TOPSIS) method is made. Both methods have different approaches for decision making and differ from the SIMUS method. Two strategies were proposed to determine the criteria weights. One is based on the Shannon entropy method and the other uses the objective weights obtained through the SIMUS method. Finally, in the fifth stage, the results obtained through the SIMUS, PROMEHEE and TOPSIS methods are combined using the expected value obtained by applying the program evaluation and review technique method (PERT). The proposed methodology was applied to study tram transportation in Sofia, Bulgaria. Five variant schemes were considered. The schemes are optimized through the criterion of minimum energy consumption. The number of trams by routes and by type was determined. An improved scheme for tram transportation in Sofia was proposed. The scheme makes it possible to increase the frequency of the trams by 13%, to reduce the zero mileage of rolling stock, and to reduce carbon dioxide pollution by 11%.

Vulnerability analysis of power grid structure based on complex network theory

IntroductionThe key nodes of an intelligent distribution network significantly impact the reliability and stability of the distribution network’s operation. The failure of these key nodes can severely affect the safe operation of the distribution network. Therefore, vulnerability analysis of key nodes is particularly important.MethodsThis article proposes a comprehensive weighting method for evaluating indicators,combining the analytic hierarchy process (AHP) and entropy weighting method, while considering the structure and operational status of the power system grid. Key node structural evaluation indicators, such as node degree, node shrinkage centrality, and electric mediator, are established considering “significance” and “destructiveness.” State indicators are established based on the degree of impact of current, voltage, and load changes on the grid, as well as the uniformity of their distribution, including the improved current distribution entropy, voltage terre entropy, and three-phase state indicators of lost load. Subsequently, based on the AHP and entropy weighting method, a comprehensive weighting method is proposed to assign subjective and objective weights to the comprehensive evaluation indicators, obtaining the comprehensive weights of the indicators. Finally, the gray correlation degree is introduced to improve the ideal solution of the multi-objective decision making method, obtaining the criticality of the grid nodes and then identifying the critical nodes.Results and DiscussionThe example analysis presented in this article shows that the identified critical nodes of the power grid have a high degree of overlap with the identification results of different methods, can better identify edge nodes, and validate the effectiveness of the proposed evaluation indicators and key node identification methods.

Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack.

Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can completely not access the structures and parameters of the target model, as well as the intermediate features and any training data associated with the model. To tackle this task, previous methods generate transferable adversarial examples from a transparent substitute model to the target model. However, we found that these works have the limitations of taking static substitute model structure for different targets, only using hard synthesized examples once, and still relying on data statistics of the target model. This may potentially harm the performance of attacking the target model. To this end, we propose a novel Dynamic Routing and Knowledge Re-Learning framework (DraKe) to effectively learn a dynamic substitute model from the target model. Specifically, given synthesized training samples, a dynamic substitute structure learning strategy is proposed to adaptively generate optimal substitute model structure via a policy network according to different target models and tasks. To facilitate the substitute training, we present a graph-based structure information learning to capture the structural knowledge learned from the target model. For the inherent limitation that online data generation can only be learned once, a dynamic knowledge re-learning strategy is proposed to adjust the weights of optimization objectives and re-learn hard samples. Extensive experiments on four public image classification datasets and one face recognition benchmark are conducted to evaluate the efficacy of our Drake. We can obtain significant improvement compared with state-of-the-art competitors. More importantly, our DraKe consistently achieves attack superiority for different target models (e.g., residual networks, and vision transformers), showing great potential for complex real-world applications.

Natural or man-made disaster? Lessons from the extreme rain and flood disaster in Zhengzhou, China on "2021.7.20"

Driven by extreme weather events, precipitation-induced flood disasters are occurring more frequently, posing substantial threats to both economic development and public safety. Increased human production activities have further intensified the impact of flood disasters. However, understanding of the factors influencing flood disasters and their associated risk assessments remains limited. To better formulate flood prevention and disaster reduction strategies suited to local conditions, this paper takes the "7.20" extreme rainfall event in the main urban area of Zhengzhou, Henan Province, China, on July 20, 2021, as a case study and uses geographic detectors to incorporate human activities into the quantitative analysis of influencing factor contributions. Based on the three aspects of disaster incubation, disaster causation, and disaster bearing, this study proposes using the influence values obtained from geographic detectors as objective weight assignments for risk assessment, thereby conducting a locally adapted risk assessment of Zhengzhou's main urban area. Research results indicate that the "7.20" heavy rainstorm in Zhengzhou's main urban area involved a superposition of natural and human factors. Impervious surfaces had the highest explanatory power, followed by population density, confirming that this flood disaster arose from the interaction of multiple factors. The flood disaster risk assessment for Zhengzhou's main urban area shows higher risk levels in the central area and relatively lower levels in peripheral areas. The central area features flat terrain, with high population density and a large proportion of impervious surfaces. Among these, 22 subdistricts are located in extremely high-risk flood zones, while 6 subdistricts, classified as low-risk, account for over 60% of the low-risk area, making them relatively lower-risk communities. Comparison with flood waterlogging point data verifies the accuracy of the risk assessment through statistical analysis, allowing for further analysis of the spatial differentiation of flood risk. The findings of this study offer valuable insights for preventing and managing urban extreme rainfall disasters.

Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management

<p>Solid waste management (SWM) protects public health, the environment, and limited resources in densely populated and urbanized countries such as Singapore. This work presents an advanced framework for optimizing SWM using advanced mathematical models and decision-making techniques, including the circular $ q $-rung orthopair fuzzy set (C$ q $-ROFS) for data, combined with the Choquet integral (CI) and logarithmic percentage change-driven objective weighting (LOPCOW) methods, enhanced by the aggregation operators (AOs) circular $ q $-rung orthopair fuzzy Einstein Choquet integral weighted averaging (C$ q $-ROFECIWA) and circular $ q $-rung orthopair fuzzy Einstein Choquet integral weighted geometric (C$ q $-ROFECIWG) aggregation operators. By conducting a systematic evaluation, these methods classified different alternatives to SWM, evaluating them according to criteria such as their environmental impact, cost-effectiveness, waste reduction efficiency, feasibility of implementation, health safety, and public acceptance. The operators C$ q $-ROFECIWA and C$ q $-ROFECIWG perform better than previous approaches in the effective management of multifaceted and dynamic SWM scenarios. The comparison study demonstrates that the integration of these operators with LOPCOW and the Choquet integral offers decision-making conclusions that are more reliable and sustainable. The study conducted in Singapore successfully finds the most feasible SWM alternatives and emphasizes the possibility of implementing more environmentally sustainable practices in the urban environment. This research offers practical insights for policymakers and emphasizes the need to improve and enhance these approaches to improve SWM in various urban environments.</p>

A New Integrated Criteria Weighting Approach for Determining Production Priority in Demand Management

In the demand management process; it is tried to balance the customers' needs with the company's supply possibilities. Effective demand management starts with effective stock control and tracking of customer orders and ends with customer satisfaction. Correctly determining production priorities is one of the ways to increase customer satisfaction. In this context, a new approach has been developed to determine the production priority needed in the demand management process. A new integrated approach based on Multi-Criteria Decision Making (MCDM) is proposed to determine production priorities. In the first stage of this three-stage approach, customer alternatives are determined. In the second stage, production priority criteria are determined depending on customer characteristics. In the third stage; with LBWA (Level Based Weight Assessment) and CRITIC (The Criteria Importance Through Intercriteria Correlation) methods, objective and subjective criterion weights are calculated by considering expert opinions and real data. Then, integrated criterion weights are determined with the SOWIA (Subjective and Objective Weight Integrated Approach) method using objective and subjective criterion weights. In the literature; few studies determine production priority using the MCDM method. Additionally, no study has been found in the literature in which quantitative and qualitative methods are used integrated in the ceramic industry. Therefore, it is thought that this study will contribute to the literature.

PROJECT PERFORMANCE ANALYSIS OF TURKISH UNIVERSITIES BY LOPCOW-CRADIS METHODS

The encouraging regulations put forward by the countries in the fields of science and technology make quite positive contributions to the efforts of the relevant stakeholders. The effort to benefit from these financial supports, which has a motivational role for scientists and industrial organizations focusing on research and development activities, has become a competitive issue and performance indicator today. In this study, the performance of 200 state and foundation universities operating in Turkiye is examined in terms of project submission and execution capacities within the scope of Research Support Programs Directorate (ARDEB). In order to weight the evaluation criteria, the LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) method was used and the criterion "Number of Project Submissions" is determined as the prominent one. The performance order of the universities is determined by the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method where Middle East Technical University, Ege University, Istanbul Technical University, Hacettepe University, Ankara University, Istanbul University, Koç University, İhsan Doğramacı Bilkent University, İzmir Technology Institute and Erciyes University are determined in the first ten order. It has also been determined that especially the universities established in recent are far from a competitive position.

Analysis Of Elementary School Students' Difficulties In Solve Mathematical Questions On Object Weight Measurement Material

The ability to measure the weight of objects is a skill needed in various fields, such as trade, industry and health. However, various studies mention that many students have difficulty in understanding and solving math problems related to measuring the weight of objects effectively. This study aims to analyze the difficulties faced by grade 6 students of UPT SDN 025 JANJI, Doloksanggul in solving mathematics problems measuring the weight of objects. Researchers made questions in the form of measuring the weight of objects and found various student errors in answering each question. The method used in this research is quantitative research, which is a research method that focuses on the collection, analysis, and interpretation of numerical data. Based on the results of research and analysis conducted by researchers on 13 sixth grade students at UPT SDN 025 JANJI above, students often face various errors in solving weight measurement problems, namely (1) errors in reading the problem, (2) understanding information, (3) choosing the right mathematical operation, and (4) performing calculations. Students often face various errors in solving weight measurement problems, including errors in reading the problem, understanding the information, choosing the right mathematical operation, and performing calculations. These errors can be categorized into several types, such as reading errors, comprehension errors, transformation errors, process skill errors, and other errors.

DETERMINING THE HUMAN CAPITAL PERFORMANCE OF LATIN AMERICAN COUNTRIES AND TÜRKİYE WITH CRITIC-BASED ARAS METHOD

The aim of this study is to determine the human capital performance of Latin American Countries and Türkiye using the CRITIC (Criteria Importance Through Intercriteria Correlation) and ARAS (Additive Ratio Assessment) techniques. In the analysis, CRITIC-based ARAS method has been used. This method is a combination two methods. The CRITIC method has provided the objective weights of the criteria, and human capital performances and ranking of the countries are obtained by ARAS method. In order to evaluate the human capital stock of the countries the following criteria are utilized: the infant mortality rate (per 1,000 live births), unemployment rate (percentage of the total labor force), average life expectancy at birth, total (years), labor force participation rate (percentage of the total population aged 15-64), current health expenditure (percentage of GDP), internet users (percentage of the total population), and population aged 15-64 (percentage of the total population). The analysis covers the years of 2019, 2020 and 2021. According to the CRITIC method results, the infant mortality rate has been the least significant criterion for every year under study, whereas the unemployment rate is the most significant. The ARAS method suggests that in every year examined, Cuba has done the best in terms of human capital. Chile and Uruguay rank second and third, respectively, after Cuba. Throughout the years under study, Guinea has performed the worst, followed by Haiti. Venezuela in 2021 and Honduras in 2019 and 2020 rank third-worst. Among the 22 nations, Türkiye ranks 15th in 2019, 12th in 2020, and 13th in 2021.

Weight illusions explained by efficient coding based on correlated natural statistics

In our everyday experience, the sizes and weights of objects we encounter are strongly correlated. When objects are lifted, visual information about size can be combined with haptic feedback about weight, and a naive application of Bayes’ rule predicts that the perceived weight of larger objects should be exaggerated and smaller objects underestimated. Instead, it is the smaller of two objects of equal weight that is perceived as heavier, a phenomenon termed the Size-Weight Illusion (SWI). Here we provide a normative explanation of the SWI based on principles of efficient coding, which dictate that stimulus properties should be encoded with a fidelity that depends on how frequently those properties are encountered in the environment. We show that the precision with which human observers estimate object weight varies as a function of both mass and volume in a manner consistent with the estimated joint distribution of those properties among everyday objects. We further show that participants’ seemingly “anti-Bayesian” biases (the SWI) are quantitatively predicted by Bayesian estimation when taking into account the gradient of discriminability induced by efficient encoding. The related Material-Weight Illusion (MWI) can also be accounted for on these principles, with surface material providing a visual cue that changes expectations about object density. The efficient coding model is further compatible with a wide range of previous observations, including the adaptability of weight illusions and properties of “non-illusory” objects. The framework is general and predicts perceptual biases and variability in any sensory properties that are correlated in the natural environment.

The Influence of Stability in New Power Systems with the Addition of Phase Modulation Functions in Thermal Power Units

The addition of phase modulation function technology to thermal power units is one of the most effective measures to solve dynamic reactive power shortages in the construction process of new power systems. In this paper, the influence of the phase modulation function transformation of thermal power units on the stability of a new power system is studied. Firstly, the new power system stability index is deeply analyzed, and an evaluation system for power system transient stability is constructed from five key dimensions: transient voltage, static voltage, power angle stability, power flow characteristics, and grid support. Secondly, a fuzzy comprehensive evaluation method considering the subjective and objective comprehensive weights is proposed, and the influence of the phase modulation transformation of the thermal power unit on the stability of the receiving-end power grid is quantitatively analyzed. Finally, a CEPRI36 node example model was built based on the PSASP v.7.91.04.9258 (China Electric Power Research Institute, Beijing, China) platform to verify the accuracy and effectiveness of the proposed method. The results show that the proposed method can quantitatively analyze the impact of adding a phase modulation function to thermal power units on the stability of the power system. At the point of renewable energy connection, the static voltage stability index improved by 42.9%, the transient power angle stability index improved by 32.1%, the multi-feed effective short-circuit ratio index improved by 33.9%, and the comprehensive evaluation score improved by 14.7%. These results further indicate that adding a phase modulation function to thermal power units can provide a large amount of dynamic reactive power support and improve the voltage stability and operational flexibility of the system.

Measurement of Synergy Management Performance in Prefabricated Building Project Supply Chain

Prefabricated building (PB) involves many participating enterprises. Its implementation faces many challenges, mainly in the areas of technology, cost, construction management, management of supply chain (SC), and sustainability. The purpose of synergy management of a prefabricated building project supply chain (PBPSC) is to help the whole SC obtain better benefits. This study establishes a measurement index system from the perspective of green sustainability in terms of cost control, technology, information, reliability of SC, and environmental protection. The COWA-CRITIC ideal point method is used to calculate the subjective and objective combination weights, and a cloud model based on this combination weight is constructed for measurement, which is validated by taking the project of a residential building as an example. The results are compared with those of other measurement models to verify the applicability of the measurement model of this study in the synergy management performance of the PBPSC. The sensitivity of the indexes within the five subsystems is analyzed using the one-way rotation OAT method to allow decision makers to identify the most sensitive indexes. This study shows that the synergy management performance of SC in this project is better. The measurement model used in this study is consistent with the results obtained from other measurement models and is in line with the actual situation. Sensitivity analysis using the one-factor rotation OAT method shows that among the secondary indexes, specialized technical staff is the most sensitive to changes in weights; among the primary indexes, cost control is the most sensitive to changes in weights. This provides a new method for measuring the synergy management performance of the PBPSC. Based on the results of this study, corresponding countermeasures are proposed for the synergy management of the PBPSC, which will provide a reference for the synergy management of SCs with similar projects in the future.

Comparative Analysis of the Flight Characteristics and Energy Efficiency of a Quadcopter and an Octacopter

This publication is part of a study aimed at finding the optimal configuration (tricopter, quadcopter, octacopter, etc.) in terms of flight stability, payload and energy efficiency. For this purpose, full non-linear quadcopter and octacopter models have been created. Data from real objects (linear and weight characteristics, parameters of the engine-propeller system) were used to create them. Quadcopter and Octacopter use the same motors and propellers. In the simulations, the mass of the two objects is the same. Both models are controlled with PID controllers in four channels (Yaw, pitch, roll and height). The initial adjustment of the regulators was carried out according to the second method of Ziegler-Nichols. A secondary adjustment of the regulators was carried out, with the goal of minimal over-adjustment, speed and accuracy. A parallel simulation of the nonlinear models in the Simulink environment was performed, on the basis of which the conclusions about the stability and energy efficiency of the quadcopter and the octacopter were made.

Proximity Index Value for Supplier Selection Using Compromise Weighting of Stepwise Weight Assessment Ratio Analysis (SWARA) Analytical Hierarchy Process (AHP) and MEREC: A Case Study in Indonesian Leather Industry

Procurement of new raw materials is needed when product demand increases, and raw material suppliers must be determined to meet the company's needs. This research examines what criteria a company needs when selecting criteria using Delphi. The weighting of criteria cannot be separated from the element of the decision maker's subjectivity; therefore, it is necessary to compromise between subjective and objective criteria. Therefore, the study used The Method of Removal Effects of Criteria (objective weighting of criteria) and Stepwise Weight Assessment Ratio Analysis (subjective weighting of criteria) in weighting criteria. Then, considering the weight of the criteria, the Proximity Index Value (PIV) is used to evaluate and rate the suppliers. The offered methodology is applied to a real case study from a leather manufacturing company in Indonesia to verify its applicability with a sensitivity analysis performed on different scenarios. The findings indicated that the proposed model is dependable and that the rankings are resilient to fluctuations in the criterion weights.

Gender Differences in the Relation Between Suicidal Risk and Body Dissatisfaction Among Bariatric Surgery Patients: A Cross-Lagged Analysis.

This study aimed to develop a gender-specific model to understand the causal relationship between body image dissatisfaction, emotional eating, and suicide risk among bariatric surgery patients. A secondary objective was to evaluate gender differences in the associations between these variables. It was hypothesized that, independent of objective weight loss, body dissatisfaction and emotional eating would lead to increased suicide risk. A total of 109 participants completed self-report measures of suicidal ideation, body image dissatisfaction, and emotional eating before and after bariatric surgery. Cross-lagged analysis indicated that pre-surgery suicide ideation significantly predicts body dissatisfaction primarily among men, independent of the extent of weight loss. High levels of pre-surgery suicide risk correlated with post-surgery body image dissatisfaction in men. The autoregressive effect of suicide ideation was stronger than that of body dissatisfaction for both genders; however, the latter was stronger among women, indicating that past dissatisfaction levels significantly influenced future dissatisfaction. The complex interplay between gender, body dissatisfaction, emotional eating, and suicide risk warrants further research.

The weights of drivers' on-route concerns in car-following behaviors

Among the available studies run on reproducing, learning, and classifying driving styles, only few have quantified the weights of drivers' on-route concerns. A new method is proposed to quantify the issue at hand in car-following behaviors, in which a multi-objective optimization model with crash risk, comfort, and travel time objectives as the driver’s decision-making process is trained. This model is trained based on different car-following trajectory real data, distinguished from each other by the driving phase and behavior of the following vehicle, where the best-matched weights for the model's objectives are identified. The results indicate that the weights of the crash risk and comfort objectives are of more concern compared to travel time. The obtained weights of the crash risk, comfort, and travel time objectives are 0.4, 0.4, and 0.2, respectively. In deceleration driving phases, timid and aggressive drivers weigh the model’s objectives differently.

Lateral stability control for distributed drive autonomous vehicle with extreme path tracking

To enhance the path-tracking accuracy and lateral stability of autonomous vehicles under extreme conditions, this paper proposes a coordinated control strategy based on an Active Front Steering (AFS) system and Direct Yaw Moment Control (DYC). First, a path-tracking controller based on Model Predictive Control (MPC) is designed, which includes a Kalman filter to correct lateral stiffness in real-time and calculates the front wheel steering angle output based on path-tracking errors and current direct yaw moment. Next, we formulated the Direct Yaw Moment Control objective as a linear combination of stability and maneuverability objectives, and used a fuzzy controller to dynamically coordinate the weights of stability and maneuverability objectives. This process not only improves the adaptability of the control strategy but also enhances vehicle performance under various road conditions. Furthermore, a Sliding Mode Control (SMC) is employed to calculate the required direct yaw moment. This approach ensures vehicle stability during high-speed driving or changes in road surface adhesion. To optimize torque distribution, we employed a constrained quadratic programming method to minimize tire load rates, thereby improving vehicle energy efficiency and handling. Finally, through Hardware-in-the-Loop (HIL) testing, we validated that the proposed control strategy achieves excellent tracking accuracy, real-time performance, and lateral stability under different road adhesion conditions and vehicle speeds. The control strategy developed in this study not only exhibits theoretical innovation but also demonstrates significant effectiveness in practical applications, providing valuable insights for path-tracking and lateral dynamic control strategies of autonomous vehicles under extreme conditions.

Intelligent evaluation and operation recommendation system of sintering process based on analytic hierarchy process–entropy weight method–technique for order preference by similarity to an ideal solution (AHP–EWM–TOPSIS)

Fluctuations in the sintering process have a significant impact on the overall performance and outcomes of sintering production. It is crucial to gain a comprehensive understanding of the sintering operation in a timely manner in order to minimise process fluctuations and ensure the stability of sinter production. In this paper, a sintering process evaluation model is presented, which is based on the analytic hierarchy process–entropy weight method–technique for order preference by similarity to an ideal solution (AHP–EWM–TOPSIS) and is constructed using data from a steel plant sintering production process. Firstly, the evaluation indices input to the system are selected by combining the principle of the sintering process and actual production requirements. The subjective and objective weights of the indices are then calculated using the AHP–EWM method, and the weights are combined through the principle of minimum discriminatory information to construct the set of weighted indices. Subsequently, the sintering process was evaluated by the TOPSIS model, and the results of this evaluation were compared with the actual fluctuation situation at the sintering site. This was done by combining the model results with the differential level method. It was found that the model can accurately reflect the fluctuation situation that occurs in the sintering process. Furthermore, the comprehensive matching rate reached 95.42%, which demonstrates that the model is capable of accurately evaluating the operation of the sintering process. The statistical analysis of historical data provides a basis for the optimised operation of the sintering production process by summarising the optimal range of each parameter when the sintering operates efficiently. Ultimately, a sintering process evaluation system is constructed through the use of computer technology, thereby facilitating the transformation of sintering production into an intelligent system. The model is capable of rapidly ascertaining the actual status of the sintering process in real time. The established system is designed to facilitate the optimisation of sintering production operations and to promote long-term stability in sintering.

Design of Hybrid Renewable Energy Systems: Integrating Multi‐Objective Optimization Into a Multi‐Criteria Decision‐Making Framework

ABSTRACTResearch into hybrid renewable energy systems (HRESs) fulfills the need for the development of sustainable and environmentally friendly energy systems to supply house‐holds. The design of HRESs is a challenging endeavor requiring the optimization of multiple objectives considered over multiple criteria. This paper presents a new multi‐criteria decision‐making framework (MCDM) to automate the design. The proposed framework initially uses a metaheuristic multi‐objective (MO) optimization algorithm to generate optimal candidate configurations and then objectively evaluates candidates to select the best configuration. A combination of the MO particle swarm optimization and a newly developed MO leaders‐and‐follower algorithms (MO‐LaF/PSO) is used to generate optimal configurations based on minimal levelized cost of energy (LCOE), renewable energy (RE) power abandonment, and CO2 emissions, while maintaining an acceptable level of reliability. The evaluation phase applies the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) ranking method that uses objective criteria weights calculated using MEREC (MEthod based on the Removal Effects of Criteria). This method is applied to a case‐study of an off‐grid Wind/PV/Diesel/Battery HRES. The results reveal that this newly proposed framework generates a unique top‐ranking configuration with an LCOE of 0.199 $/kWh, 0% wastage of RE, and 982 tons of CO2.