Single Evaluation Model Research Articles

Impact of Corporate Sustainability Performance on Financial Performance of South African Industries Via Brand Value and Competitiveness

Sustainability managers and stakeholders unanimously recognise that a single, systematic and integrated model for measuring corporate sustainability performance significantly enhances firms’ competitive advantage, profit and performance. Nevertheless, serious concerns exist in literature on the non-existence of a single, systematic and integrated model for measuring the impact of corporate sustainability performance (i.e. environmental, social, and economic) on the financial performance of South African industries via brand value/reputation and competitiveness. Thus, the objective is to develop this model by collecting qualitative and quantitative data through a census of 70 out of 74 companies of Johannesburg Stock Exchange, active in Socially Responsible Investment Index. Factor analysis was used for data reduction, Chi-square tested for association between categorical variables, and Cronbach alpha for internal consistency and reliability of instruments. Findings revealed positive impacts between environmental, social and economic aspects of corporate sustainability performance, brand value/reputation, competitiveness and financial performance. Results also showed that the elements/factors of corporate social performance like ethical behaviour, genuine community initiatives and human rights can translate to brand value/reputation. Likewise, elements/factors of brand value/reputation like competitive edge, comparative advantage, innovation, reputation, employees’ loyalty and non-market effect can translate to competitiveness and financial performance. By implication, the model can be useful to potential managers in South Africa or world for making easy and prompt decisions on past, pending and future environmental, social and economic sustainability. Hence, the research contributes to development science a single, systematic and integrated evaluation model adaptable to companies, businesses and governments/municipalities for achieving performance targets.

Optimizing deficit drip irrigation to improve yield,quality, and water productivity of apple in Loess Plateau of China

ContextDeficit drip irrigation is an advanced agricultural technology widely promoted and applied around the world to maintain/increase yield and quality while simultaneously minimizing water consumption. However, there is still a lack of comprehensive research on the effects of deficit drip irrigation at different stages on apple yield, water productivity, and quality. ObjectiveThe aim was to investigate the effects of deficit drip irrigation treatments (DDITs) on apple yield, quality, and water productivity (WP), and optimize the deficit drip irrigation strategy for apples in the Loess Plateau of China. Methodsa two-year field experiment was conducted with 17 irrigation treatments, including a control treatment (CK, full irrigation (FI)) and four DDITs (D15%, D30%, D45%, D60%) during the bud burst to leafing stage (I), flowering to fruit set stage (II), fruit expansion stage (III), and fruit maturation stage (IV). The optimal irrigation management was determined using the fuzzy Borda combined model, which was based on four different single evaluation models of the TOPSIS method, principal component analysis (PCA), grey correlation analysis (GCA), and membership function analysis (MFA). ResultsThe DDITs at stage IV decreased midday leaf water potential (Ψs) by 2.57–20.00% compared with CK, and the difference of Ψs between IV-D30%, IV-D45%, IV-D60%, and CK were significant in two years (P<0.05). DDITs at stages I, II, and IV improved fruit weight (FW), fruit shape index (FSI), and fruit firmness (FN), but did not significantly affect fruit water content (FWC). The DDITs at stages III and IV significantly enhanced total soluble solids (TSS), soluble sugars (SS), and vitamin C (Vc) in 2021 and 2022, but a notable decrease in titratable acidity (TA) was observed. ConclusionsDDITs at stage IV exhibited the highest comprehensive benefit of apple, followed by DDITs at stages I and II, and DDITs at stage III had the lowest values. The DDITs of D15%, D30%, CK, and D15% at stages I, II, III, and IV were identified as the optimal irrigation strategy to improve apple yield, quality, and WP. ImplicationsOur findings can guide the development of appropriate irrigation strategies for apple cultivation in Loess Plateau of China, promoting efficient and sustainable apple production.

Towards machine learned generative design

Machine learned generative design is an extension of the generative design process, addressing its inherent limitations, particularly those of interoperability. The proposed approach uses machine learning-based surrogate models, trained on computational model data, to replicate design evaluations and integrate them into a common environment. In this way, design alternatives can be generated and tested that satisfy all design requirements and considerations. The effectiveness of this approach is demonstrated by the design and optimisation of the enclosure structure for the New Robotic Telescope. Its complexity is characterised by multiple operating states that the enclosure can assume, in particular the closed state and the opening/closing state, each of which has a different structural behaviour. Using our approach, the results from each state were replicated with machine learning models and combined into a single evaluation model. This resulted in finding multiple solutions that outperformed the benchmark design. The results demonstrate not only the success of our method over conventional strategies, but also highlight its potential to redefine future design optimisation processes.

Reform and Exploration on Teaching of Java Programming Course Based on The Concept of OBE

The Java programming course is a professional basic course for computer majors. This course has problems such as unclear teaching objectives, outdated teaching models, and single course evaluation models. In order to change a series of problems caused by traditional education methods, the OBE education concept is introduced into the teaching of Java programming courses, with results as the guide and students as the center, using the OBE concept to formulate teaching goals, refine the course goals, and adopt online and offline methods. The reform of the blended teaching model, the strengthening of school - enterprise cooperation, and the adoption of a diversified curriculum evaluation system. It is found in practice that the teaching concept based on OBE can improve students' learning effect and engineering practice ability.

Selection of Third-Party Reverse Logistics Service Provider Based on Intuitionistic Fuzzy Multi-Criteria Decision Making

The scientific selection of a third-party reverse logistics service provider (3PRLP) is helpful for enterprises to obtain the expected ecological and economic benefits. Different enterprises have different requirements for 3PRLP selection and, thus, should adopt personalized and simplified evaluation criteria. However, there is a lack of research on criteria screening. Therefore, this paper proposes a criteria screening method based on a rough set for the first time. The 3PRLP selection is a multi-criteria decision making (MCDM) problem in essence, and different criteria can be expressed in different forms. The existing research mostly uses one method for 3PRLP selection, lacking the comprehensive application of various methods. In this paper, various criteria values are transformed into intuitionistic fuzzy numbers (IFNs) for the comparison and combination of various intuitionistic fuzzy MCDM methods. In terms of criteria weighting, a subjective weighting method based on an analytical network process (ANP) is proposed due to the possible correlation between the criteria at the same level. Meanwhile, an objective weighting method based on intuitionistic fuzzy entropy is proposed. The subjective and objective weights are integrated to form the more scientific combination weights. Combining the modeling principles of different intuitionistic fuzzy MCDM methods, the representative methods under each principle are chosen to build a combination evaluation idea that integrates multiple single evaluation models, and the specific evaluation steps are given, including the single evaluation, Kendall compatibility test, combination evaluation, and Spearman consistency test. An illustrative example of 3PRLP selection is provided to verify the feasibility of the methods of criteria screening and weighting and the combination evaluation idea.

Is it Feasible to Use BP Neural Network for Academic Journal Evaluation: an Attempt Based on 6 Single Objective Weighting Methods

ABSTRACTIn the new era, the analysis of academic journal evaluation methods and the comprehensive comparison of the advantages, disadvantages, and stability of different methods can help to provide some reference for academic journal evaluation. In this study, single model evaluation was carried out for 6 weighting methods without comprehensive evaluation value, and fuzzy comprehensive evaluation is performed on the results passing the nonparametric test. Based on the evaluation, BP neural network is introduced, and BP neural network evaluation model is established. The results show that the fuzzy Borda evaluation can integrate the evaluation value and evaluation order of single models, and has higher accuracy compared with single evaluation models. The prediction rate of the network model based on the gradient descent optimization algorithm can reach more than 80%, and the weights obtained from the continuous self‐learning of the neural network training set can reduce the subjectivity and mutual interference between indicators.

Construction of comprehensive evaluation index system of water-saving irrigation project integrating penman Montei the quation

Abstract In order to solve the post evaluation system construction of high-efficiency and water-saving irrigation projects, this paper puts forward the research significance of post evaluation technology of high-efficiency and water-saving irrigation projects, summarizes the research status and development trends of post evaluation of projects at home and abroad, and analyzes the existing post evaluation theories and research methods. Based on the actual situation of high-efficiency water-saving irrigation projects, the post evaluation index system of high-efficiency water-saving irrigation projects is constructed from five aspects: construction process, operation management, economy, impact, goal and sustainability by using the method of combining expert questionnaire survey and statistical theory; Study the advantages, disadvantages and scope of application of the existing post project evaluation index weight determination methods and evaluation methods, put forward five single post evaluation standards: post construction process evaluation, post operation management evaluation, post economic evaluation, post impact evaluation, post objective and sustainability evaluation, determine the evaluation process and form five single post evaluation models. Based on the five individual post evaluation models, the project success evaluation model is constructed to form the "5 + 1" post evaluation model of high-efficiency water-saving irrigation project. Taking a high-efficiency water-saving irrigation project as an example, the application process of post evaluation index system and evaluation model of high-efficiency water-saving irrigation project is given, and the feasibility of applying the index system and evaluation model proposed in this paper to production practice is analyzed.

Customer knowledge management competence evaluation of tourism enterprises based on ahp-fuzzy comprehensive evaluation method

With the fierce competition under the background of knowledge-based economy, tourism enterprises are increasingly aware that they must turn their focus from traditional products to customers for the sake of survival and development. Integrating the customer relationship management and knowledge management, the customer knowledge management (CKM) has aroused higher attention from the tourism enterprises. As for how to determine the factors influencing the Customer knowledge management competence (CKMC) of tourism enterprises and their weights, an index system was established for evaluating CKMC of tourism enterprises based on the balanced score card (BSC) and knowledge management process, the weight design and consistency check of the indexes were implemented using the analytic hierarchy process (AHP), and the overall evaluation value and concrete index scores at all levels were obtained via the fuzzy comprehensive evaluation model. In the end, the scientificity and operability of the evaluation model were verified through an empirical analysis of China Youth Travel Service (CYTS). The results show that: (1) The business process, customer communication, system support, and market performance are important level I indexes used to measure the CKMC; (2) The key Level II factors influencing the CKMC of enterprises include customer knowledge sharing mechanism, timeliness of customer communication, degree of importance attached by senior leadership, and customer acquisition rate; (3) The evaluation model based on AHP and fuzzy evaluation method can objectively describe the overall up-to-standard degree of enterprises’ CKMC, and clearly identify the strengths and weaknesses. This research shows that the combination of AHP and fuzzy evaluation-based method is capable of more scientific and complete evaluation of CKMC, compensates for the deficiencies of single evaluation model, and provides a new method for the effective improvement of enterprises’ CKMC.

Management of competitiveness of organizations through improved product quality

Analysis and evaluation of approaches to management of quality of food products, development of directions of their management are shown. Also the goal is selection of tools for transformation of competitive position through the formation of a single evaluation model. The current economic situation in Russia, complicated by economic sanctions, shows the importance of studying consumer preferences for the analysis of product quality at a new level. The development of the market of domestic bakery products is mainly hampered by non-compliance with the basic parameters of non-price competition. Therefore, there is a need to systematize the factors affecting competitiveness, their analysis and ranking, decision-making, taking into account the capabilities of a particular enterprise. The methods of analysis of quality management of bakery products to ensure a competitive position at the regional level are implemented. The most significant parameters influencing formation of consumer preferences and perception of quality of bakery products are defined. The econometric model of dependence of parameters of quality of food products which description allows defining and carrying out coordination of strategic decisions on operational management competitiveness of the enterprises of the food industry is developed.

A multi-parameter ultrasonic evaluation of mean grain size using optimization

An evaluation model based on multiple ultrasonic parameters was developed to control mean absolute error between an actual measured value and a model calculation value. Superalloy GH4169 was used to validate the presented model. A multi-dimensional ultrasonic parameter set was built for each sample by ultrasonic and nonlinear ultrasonic test and parameter calculation. The feature scale of different parameters was restricted to the same range using normalization. The dimensionality of the parameter set was descended by a correlation metric with grain size. A new one-dimensional ultrasonic characteristic parameter was comprised of the select normalized set of ultrasonic parameters by quadratic polynomial, exponential, Gauss kernel and linear mapping functions with undetermined coefficients. The relationship between the new ultrasonic multiple eigenvalues mapping parameter and estimated grain size was expressed using a linear fitting function. An optimization problem aimed at minimizing the mean absolute error between the estimated and quantitative measured grain size was solved using evolutionary algorithms to obtain coefficients for the mapping and fitting functions. These calculations were used to generate four full multi-parameter ultrasonic evaluation models, which were benchmarked against three single parameter evaluation models (i.e. velocity, attenuation coefficient, and backscattering). The performance analysis and verification using leave-one-out cross-validation demonstrate the utility of the MUE model through its high precision, good stabilization, super anti-interference and practicality.

Combining simulation experiments and analytical models with area-based accuracy for performance evaluation of manufacturing systems

Simulation is considered as one of the most practical tools to estimate manufacturing system performance, but it is slow in its execution. Analytical models are generally available to provide fast, but biased, estimates of the system performance. These two approaches are commonly used distinctly in a sequential approach, or one as alternative to the other, for assessing manufacturing system performance. This article proposes a method to combine simulation experiments with analytical results in a single performance evaluation model. The method is based on kernel regression and allows considering more than one analytical methods. A high-fidelity model is combined with low-fidelity models for manufacturing system performance evaluation. Multiple area-based low-fidelity models can be considered for the prediction. The numerical results show that the proposed method is able to identify the reliability of low-fidelity models in different areas and provide estimates with higher accuracy. Comparison with alternative approaches shows that the method is more accurate in a studied manufacturing application.

An Improved Data-Driven Multiple Criteria Decision-Making Procedure for Spatial Modeling of Mineral Prospectivity: Adaption of Prediction–Area Plot and Logistic Functions

Assigning realistic weights to targeting criteria in order to synthesize various geo-spatial datasets is one of the most important challenging tasks for mineral prospectivity modeling (MPM). Techniques for multiple criteria decision-making (MCDM), like MPM, are deeply concerned with combining a large-scale exploration dataset into a single evaluation model for localizing prospects of a certain deposit type. In this paper, we develop the data-driven TOPSIS procedure, as a GIS-based MCDM technique for MPM. Because weighting and integrating various exploration evidence layers are influenced by intricacy and vagueness of ore mineralization process, imprecise selection of targeting criteria may reduce the possibility of exploration success. To address this problem, we applied prediction–area plot for prioritizing, recognizing and weighting efficient and inefficient targeting criteria. In addition, normalized density (Nd) index was then used for assigning significant weights to fractal-based discretized classes of each targeting criterion. After recognition of efficient and inefficient targeting criteria, data-driven TOPSIS procedure was adapted based on participation of only efficient targeting criteria as well as all targeting criteria for porphyry-Cu prospectivity in Varzaghan district, NW Iran. For quantitative assessment, a success rate curve for each of the two prospectivity models generated in this study was drawn. The results prove the superiority of the predictive model based on using efficient targeting criteria.

A bus route evaluation model based on GIS and super-efficient data envelopment analysis

ABSTRACTWhen compared to large cities in developed countries, the shares of public transportation in most Chinese cities are low. Increasing the competitiveness of urban public transportation remains an urgent problem. A capable evaluation method for public transportation is required to assist the development of urban transit systems. This paper focuses on the bus system. Being devoid of standard criteria, it is difficult to determine the efficiency of a transit system or any bus line using a single evaluation index. This paper proposes a comparative analysis to evaluate bus lines so as to filter out candidates for further optimization. From the viewpoints of transit planning, operation and quality of service, this paper establishes 10 subordinate evaluation indices and then uses geographical information system tools, global positioning system data and smart card data to assist the index definition and calculation. Super-efficient data envelopment analysis (DEA) method is adopted for the proposed single factor and comprehensive evaluation models. Finally, the bus system in Shenzhen, China is used as a case study. The comparable significant results validate the capability of the proposed model.

Comprehensive Evaluation of the Technology Innovation Capability of China’s High-tech Industries Based on Fuzzy Borda Combination Method

Technology innovation capability is a main driver in improving a country’s industrial competitiveness. Development prospects and development speed are strongly dependent on industry innovation capability. Therefore, how to effectively and correctly evaluate the innovation capability of industry is of great importance. On the basis of previous research, this paper establishes an index system for evaluating the technology innovation capability of China’s high-tech industries. The index is characterized by the objective evaluation of objects with measurable indexes. The fuzzy Borda method uses precise digital methods to handle fuzzy evaluation objects; therefore, more scientific and pragmatic quantitative criteria are obtained with qualitative and quantitative evaluation results. This paper is the first attempt to apply the fuzzy Borda combination method to high-tech industry innovation capability evaluation. We establish a fuzzy Borda combination model based on four kinds of single evaluation models. By making a combination evaluation, the disadvantages of a single evaluation method are avoided. In the end, based on the fuzzy Borda combination evaluation model, the real technology innovation data of 2013 is analyzed and the innovation capability of individual industry is ranked, which will provide useful guidance for decision-making administrators.

Evaluation of banks’ commercial credit applications using the analytic hierarchy process and Grey relational analysis: A comparison between public and private banks

The purpose of this study is to develop an evaluation model that considers the quantitative and qualitative criteria for the appropriate selection of firms demanding commercial credit for both public and private banks. In this paper, the authors propose an integrated model that combines the Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) into a single evaluation model. The model is illustrated with a case study on bank experts to demonstrate the effectiveness of this integrated method for four firms that applied for a commercial loan. In this study, AHP is applied to determine the weight of the criteria, and GRA is performed to determine the most appropriate firm. The results of this study indicate that, whereas firm morality and news criteria are the main criteria with the highest priority, sale and marketing constructions are the main criteria with the lowest priorities for both public and private banks. In addition, according to the results of GRA, the most appropriate firm for a public bank is Firm 1, and the most appropriate firm for a private bank is Firm 2.

Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping

Techniques for GIS-based multicriteria decision-making (GIS-MCDM), like mineral prospectivity mapping (MPM), are concerned with combining information from several criteria into a single evaluation model to solve certain problems in the fields of geosciences. In this paper, we introduce the geometric average method for MPM, as a GIS-MCDM approach, and demonstrate its advantage over the expected value MPM method. The comparative analysis shows that the geometric average MPM method yields better prediction of mineral prospectivity and it overcomes the limitation of the expected value method in terms of using spatial evidence values with the same unit.

Evaluation of banks' commercial credit applications using the Analytic Hierarchy Process and Grey Relational Analysis: A comparison between public and private banks

The purpose of this study is to develop an evaluation model that considers the quantitative and qualitative criteria for the appropriate selection of firms demanding commercial credit for both public and private banks. In this paper, the authors propose an integrated model that combines the Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) into a single evaluation model. The model is illustrated with a case study on bank experts to demonstrate the effectiveness of this integrated method for four firms that applied for a commercial loan. In this study, AHP is applied to determine the weight of the criteria, and GRA is performed to determine the most appropriate firm. The results of this study indicate that, whereas firm morality and news criteria are the main criteria with the highest priority, sale and marketing constructions are the main criteria with the lowest priorities for both public and private banks. In addition, according to the results of GRA, the most appropriate firm for a public bank is Firm 1, and the most appropriate firm for a private bank is Firm 2.

The Staged Decision Model of Key Grid Project: Based on the Real Option Theory

With the continuous development of electric power construction and electricity market reform, power grid investment are facing more and more uncertainties. As to the Key Project investment evaluation for grid, single stage evaluation model is too simple to make an appropriate decision, which overlooked the option value of the investment project. Therefore, in view of the periodical characteristics of investment decision-making process of the grid company’s key project, this article based on the real option analysis methods, and divide the power grid’s key projects into multiple stage according to time sequence, to evaluate each stage’s option value, and to determine the overall value of the grid company’s key investment projects, which has also provided an new train? of thought for the assessment of risk investment projects.

Research on Effects of Environment Policies and Systems on Environmental Protection Industry

Environment polices/systems play vital roles in and are motive powers for the development of environmental protection industry. In order to delve into the facilitating influences of different environment polices/systems at various development stages of the environmental protection industry, the paper analyzes the forces acting on the development of environmental protection industry in combination with the theory of industrial economics and features of the environmental protection industry. Environment polices/systems facilitating the development of environmental protection industry are divided into three major categories: industry demand based, industry supply based and industry regulation based, which are further classified into 24 items. The Delphi method and single factor evaluation model are adopted for the evaluation of the influence degrees imposed by environment policies/systems at different stages of the environmental protection industry on the industrys development and for empirical analyses on the urban sewage treatment industry.

Extracting performance rules of suppliers in the manufacturing industry: an empirical study

Performance evaluation of suppliers is increasingly recognized as a critical indicator in supply chain cooperation. Traditional performance evaluation methods have the problems of a simple buy/sell relation and in one's subjective views between manufacturers and suppliers, and they lack objective automatic evaluation processes in the supply chain considered. Statistical techniques used for evaluation rely on the restrictive assumptions of linear separability, multivariate normality, and independence of the predictive variables. Unfortunately, many of the common models of performance evaluation of suppliers violate these assumptions. The study proposes an integrated model by combining K-means clustering, feature selection, and the decision tree method into a single evaluation model to assess the performance of suppliers and simultaneously tackles the above-mentioned shortcomings. The integrated model is illustrated with an empirical case study of a manufacturer for an original design manufacturer (ODM) to demonstrate the model performance. The experimental results indicate that the proposed method outperforms listed methods in terms of accuracy, and three redundant attributes can be eliminated from the empirical case. Furthermore, the extracted rules by the decision tree C4.5 algorithm form an automatic knowledge system for supplier performance evaluation.