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Journal articles on the topic 'Multi-criteria estimation'

Author: Grafiati

Published: 30 May 2022

Last updated: 19 July 2025

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Sveshnikov, Sergey, Victor Bocharnikov, Anatoly Pavlikovsky, and Andrey Prima. "Estimating the potential willingness of the state to use military force based on the Sugeno fuzzy integral." Yugoslav Journal of Operations Research, no. 00 (2022): 2. http://dx.doi.org/10.2298/yjor210515002s.

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Estimation of the potential willingness of the state to use military force is an integral part of the analysis of international relations and the preparation of key decisions in security sphere. Our problem was to develop a method for numerically estimating the potential willingness of any state to use military force. This method should take into account a large number of quantitative and qualitative criteria, the uncertainty of their relationships, as well as the uncertainty of the initial data, some of which can only be obtained with the help of experts. Our analysis has shown that the known methods have a number of serious shortcomings. We proposed to solve this problem based on the representation of partial estimations of states in the form of fuzzy sets, and the importance of criteria in the form of a fuzzy measure. We also proposed to aggregate the partial estimations using the Sugeno fuzzy integral. We developed a hierarchical structure of estimation criteria, determined the importance of the criteria, built an observation channel based on the Harrington curve to obtain input estimations, and also developed an aggregation algorithm. As a result, we calculated estimations for 137 states and examined their potential willingness to use military force. The results disclose new aspects of using fuzzy-integral calculus to construct hierarchical models of multi-criteria estimating, and also demonstrate the possibility of using artificial intelligence methods to obtain numerical estimations in the sphere of international relations.

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Natalia, Bordiug. "Criteria and formation levels of professional competences of specialists of the nature protection branch in ecological monitoring." ScienceRise: Pedagogical Education, no. 4(31) (July 31, 2019): 4–7. https://doi.org/10.15587/2519-4984.2019.174106.

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During the study there were elaborated criteria and estimation levels of the multi-step system of training specialists of the nature protection branch for ecological monitoring: for future specialists and professionals by the formation level of professional competences in ecological monitoring; for specialists of the nature protection branch by the level of readiness to the professional activity. There was constructed a criteria-level scale for estimating future competences formation of future specialists that includes motivational, theoretical-methodological and analytical-practical criteria. The estimation is realized by reproductive, sufficient and high levels. For future professionals in ecological monitoring, the criteria-level scale for estimating professional competence formation includes motivation-value, activity-practical, scientific-research criteria and interpreting, constructive, research levels. For the professional development of specialists of the nature protection branch in the system of postgraduate education, a criteria-level scale was developed for estimating the level of readiness to the professional activity, including the following criteria: motivational-axiological for estimating formation motives of the professional nature protective activity; competence for estimating professionalism; professional attitude for estimating a personal attitude to the professional activity, especially independence in making decisions, in using different approaches to solving complicated ecological situations, in elaborating and planning projects for improving the environment quality. Corresponding to peculiarities of the professional development, there were determined readiness levels, namely basic, integrative-projective, creative. There were elaborated and substantiated criteria, parameters and levels that provide the estimation of the multi-level training system of specialists of the nature protection branch for ecological monitoring through the formation of professional competences and readiness to the professional activity

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Livneh, B., and D. P. Lettenmaier. "Multi-criteria parameter estimation for the unified land model." Hydrology and Earth System Sciences Discussions 9, no. 4 (2012): 4417–63. http://dx.doi.org/10.5194/hessd-9-4417-2012.

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Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the Continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operation Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting model, is the basis for these experiments. Calibrations were made using each of the criteria individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large-scales calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.

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Livneh, B., and D. P. Lettenmaier. "Multi-criteria parameter estimation for the Unified Land Model." Hydrology and Earth System Sciences 16, no. 8 (2012): 3029–48. http://dx.doi.org/10.5194/hess-16-3029-2012.

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Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operational Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting Model, is the basis for these experiments. Calibrations were made using each of the data sets individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large scales, calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.

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Brigadnov, Igor A. "Multi-criteria Estimation of Load-Bearing Capacity of Solids." Journal of Elasticity 140, no. 1 (2020): 121–33. http://dx.doi.org/10.1007/s10659-019-09762-8.

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KaurSehra, Sumeet, Yadwinder Singh Brar, and Navdeep Kaur. "Multi Criteria Decision Making Approach for Selecting Effort Estimation Model." International Journal of Computer Applications 39, no. 1 (2012): 10–17. http://dx.doi.org/10.5120/4783-6989.

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Kędzior, Anna, and Konrad Kułakowski. "Multiple-Criteria Heuristic Rating Estimation." Mathematics 11, no. 13 (2023): 2806. http://dx.doi.org/10.3390/math11132806.

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One of the most widespread multi-criteria decision-making methods is the Analytic Hierarchy Process (AHP). AHP successfully combines the pairwise comparisons method and the hierarchical approach. It allows the decision-maker to set priorities for all ranked alternatives. But what if, for some of them, their ranking value is known (e.g., it can be determined differently)? The Heuristic Rating Estimation (HRE) method proposed in 2014 tried to bring the answer to this question. However, the considerations were limited to a model only considering a few criteria. This work analyzes how HRE can be used as part of the AHP hierarchical framework. The theoretical considerations are accompanied by illustrative examples showing HRE as a multiple-criteria decision-making method.

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Nefedov, A. S., V. A. Shakirov, and S. M. Ignatieva. "Multi-criteria selection of the mix of generating plants in local energy systems based on a modified analytic hierarchy process." iPolytech Journal 26, no. 3 (2022): 451–64. http://dx.doi.org/10.21285/1814-3520-2022-3-451-464.

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This paper presents a modification of the analytic hierarchy process in order to increase its efficiency for a multi-criteria comparison of mixes of generating plants in local energy areas during their development. The multi-criteria problem of selecting the most effective ratio of rated capacities is considered for power plants representing a single mix of generating plants during the development of a local energy area in the Khabarovsk Krai. The energy sources are represented by thermal, solar, wind and diesel power plants. The following estimation criteria for alternative solutions were accepted: levelized cost of electricity, ecological effeciency estimation; estimation of public opinion about the consequences involved with the establishment of power plants. In order to solve the multi-criteria problem, the analytic hierarchy process (AHP) was used. When using the original AHP for the set problem, a large quantity of alternatives at the stage of pairwise comparisons were found to represent a perceptible load on a decision maker. Thus, already during the estimation of 10 alternatives according to 5 criteria, decision makers should conduct 225 pairwise comparisons, which may eventually result in an unacceptable consistency of the results. In addition, this requires a procedure accounting for the uncertainty of the decision maker's preferences. The proposed solution represents a method of forming matrices of pairwise comparisons upon criteria. This method consists in generating an interval or fuzzy model of the decision maker's preferences for evaluating pairs of estimates according to the criterion. The proposed method was verified using a numerical example of solving the set problem. The obtained optimum mix of power plants consists of thermal, solar and diesel plants with a power of 30, 35 and 39 mW, respectively. The proposed method ensures a high consistency of the results obtained during alternative pairwise comparisons. In addition, the modified analytic hierarchy process takes into account the non-linear nature of the decision maker's preferences for estimating alternatives according to criteria.

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Coit, D. W., T. Jin, and N. Wattanapongsakorn. "System Optimization With Component Reliability Estimation Uncertainty: A Multi-Criteria Approach." IEEE Transactions on Reliability 53, no. 3 (2004): 369–80. http://dx.doi.org/10.1109/tr.2004.833312.

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Benkesmia, Yamina, Nadjla Bentekhici, and Lahcen Wahib Kebi. "ESTIMATION AND MAPPING OF INFILTRATION BASED ON A MULTI-CRITERIA ANALYSIS." Acta Geobalcanica 3, no. 1 (2017): 27–33. http://dx.doi.org/10.18509/agb.2017.03.

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Kamel, Isaam, and lina Khalid. "On Bayesian Estimation in Multi - Component Inverted Exponential Stress-Strength Model." Journal of Physics: Conference Series 2322, no. 1 (2022): 012038. http://dx.doi.org/10.1088/1742-6596/2322/1/012038.

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Abstract In this paper we find the mathematical formula of the reliability system for Multi component in stress-strength model Rp, when the stress and strength are identical random variables distributed as inverted exponential distribution, in addition estimated the reliability Rp using : the maximum likelihood estimator (MLE) and the Bayes (B) method, and made a comparisons among the estimation methods using Monte-Carlo simulation depend on the mean square Error (MSE) criteria.

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Ganicheva, A. V., and A. V. Ganichev. "VECTOR MODEL FOR MULTI-CRITERIA SCORING." Scientific Review: Theory and Practice 10, no. 10 (2020): 2245–53. http://dx.doi.org/10.35679/2226-0226-2020-10-10-2245-2253.

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To assess the performance of economic systems, criteria and performance indicators are used that show the degree of achievement of the desired performance result. To characterize complex systems in most real cases, one criterion is not enough. Therefore, several criteria are used to comprehensively characterize the system. The multi-criteria problem raises the problem of matching individual criteria when used together. There are many ways to select alternatives in the face of multiple decision criteria. This article discusses the multi-criteria comparison of objects with Pareto dominance, vector estimates with arbitrary non-negative coordinates of vectors. The advantages of vector estimates in comparison with average estimates are considered. In this work, three important theorems of multi-criteria estimation are proved. The theorems define the conditions for the equivalence of three options for comparing objects by a variety of attributes: 1) arithmetic mean values; 2) the lengths of the corresponding vectors; 3) scalar products of vectors. As the research results, the application of the developed methods for aggregating the quality indicators of the educational process into a single assessment is shown. The following indicators of the educational process were used: coefficient of diligence, coefficient of interest, coefficient of difficulty of mastering the studied material, assessment of academic performance. A four-criteria optimization problem is formulated. It is shown that the objective function reaches its extreme value at the point that is as close as possible to the point of the optimal solution of the formulated problem. The issues of multi-criteria assessment developed in the article can find application in the management of the educational process in the formation of individual trajectories of trainees, in the tasks of working with the personnel of firms and organizations (selection, training, retraining), in assessing the quality of products and in other areas of activity.

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Abonazel, Mohamed R., Mohamed Abdallah, and El-Housainy A. Rady. "On New Two-Step GMM Estimation of the Panel Vector Autoregressive Models with Missing observations." WSEAS TRANSACTIONS ON MATHEMATICS 21 (September 20, 2022): 671–83. http://dx.doi.org/10.37394/23206.2022.21.79.

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Few estimation methods were discussed to handle the missing data problem in the panel data models. However, in the panel vector autoregressive (PVAR) model, there is no estimator to handle this problem. The traditional treatment in the case of incomplete data is to use the generalized method of moment (GMM) estimation based on only available data without imputation of the missing data. Therefore, this paper introduces a new GMM estimation for the PVAR model in case of incomplete data based on the mean imputation. Moreover, we make a Monte Carlo simulation study to study the efficiency of the proposed estimator. We compare between two GMM estimators based on the mean squared error (MSE) and relative bias (RB) criteria. The first is the GMM estimation based on the list-wise (LW) and the second is the GMM estimation using the mean imputation (MI) at multi-missing levels. The results showed that the MI estimator provides more efficiency than the LW estimator.

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Belev, S. G., V. V. Veterinarov, and E. O. Matveev. "Estimation of electronic procedures effects in public procurement under favoritism." Voprosy Ekonomiki, no. 9 (September 6, 2023): 47–64. http://dx.doi.org/10.32609/0042-8736-2023-9-47-64.

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This paper investigates the effects of electronic procedures for supplier selection in public procurement multi criteria auctions (with a selection of a winner based not only on the price criterion) for the results of research and development (R&D) work in Russia. The electronic procedures in public procurement can increase the number of bidders and lead to lower final contract prices. However, in the presence of favoritism, the effect of electronic procedures may be limited. To identify and evaluate the effects of introduction of electronic procedures, we collected microdata on 4517 composite auctions for R&D results for the period from 12/16/2016 to 12/20/2021. Until 2019, there were practically no electronic multi criteria auctions, and starting from 2019, all multi criteria auctions were in electronic form, which creates quasi-experimental conditions. Favoritism is evaluated based on the frequency of interaction between the customer and the supplier. The results of econometric modeling suggest that the introduction of the electronic procedures do increase competition in multi criteria auctions and lead to lower final contract prices, but the effect is weaker in auctions with potentially affiliated customer and supplier. Moreover, one of the main channels of the negative effect of favoritism is inflated quality criterion scores of affiliated suppliers.

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Alfares, Hesham K., and Salih O. Duffuaa. "Simulation-Based Evaluation of Criteria Rank-Weighting Methods in Multi-Criteria Decision-Making." International Journal of Information Technology & Decision Making 15, no. 01 (2016): 43–61. http://dx.doi.org/10.1142/s0219622015500315.

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This paper presents a simulation study to assess the performance of the five known methods for converting ranks of several criteria into weights in multi-criteria decision-making. The five methods assessed for converting criteria ranks into weights are: rank- sum (RS) weights, rank reciprocal (RR) weights, rank order centroid (ROC) weights, geometric weights (GW), and variable-slope linear (VSL) weights. The methods are compared in terms of weight estimation accuracy considering different numbers of criteria and decision makers’ (MS) preference structures. Alternative preference structures are represented by different probability distributions of randomly generated criteria weights, namely the uniform, normal, and exponential distributions. Results of the simulation experiments indicate that no single method is consistently superior to all others. On average, RS is best for uniform weights, VSL is best for normal weights, and ROC is best for exponential weights. However, for any multi-criteria decision-making (MCDM) problem, the best method for converting criteria ranks into weights depends on both the number of criteria and the weight distribution.

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Petrov, Konstantin, Igor Kobzev, Oleksandr Orlov, Victor Kosenko, Alisa Kosenko, and Yana Vanina. "Devising a method for identifying the model of multi-criteria expert estimation of alternatives." Eastern-European Journal of Enterprise Technologies 4, no. 3(112) (2021): 56–65. http://dx.doi.org/10.15587/1729-4061.2021.238020.

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An approach to constructing mathematical models of individual multicriterial estimation was proposed based on information about the ordering relations established by the expert for a set of alternatives. Structural identification of the estimation model using the additive utility function of alternatives was performed within axiomatics of the multi-attribute utility theory (MAUT). A method of parametric identification of the model based on the ideas of the theory of comparative identification has been developed. To determine the model parameters, it was proposed to use the midpoint method that has resulted in the possibility of obtaining a uniform stable solution of the problem. It was shown that in this case, the problem of parametric identification of the estimation model can be reduced to a standard linear programming problem. The scalar multicriterial estimates of alternatives obtained on the basis of the synthesized mathematical model make it possible to compare them among themselves according to the degree of efficiency and, thus, choose "the best" or rank them. A significant advantage of the proposed approach is the ability to use only non-numerical information about the decisions already made by experts to solve the problem of identifying the model parameters. This enables partial reduction of the degree of expert’s subjective influence on the outcome of decision-making and reduces the cost of the expert estimation process. A method of verification of the estimation model based on the principles of cross-validation has been developed. The results of computer modeling were presented. They confirmed the effectiveness of using the proposed method of parametric model identification to solve problems related to automation of the process of intelligent decision making.

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Konstantin, Petrov, Kobzev Igor, Orlov Oleksandr, Kosenko Victor, Kosenko Alisa, and Vanina Yana. "Devising a method for identifying the model of multi-criteria expert estimation of alternatives." Eastern-European Journal of Enterprise Technologies 4, no. 3 (112) (2021): 56–65. https://doi.org/10.15587/1729-4061.2021.238020.

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Owais, Mahmoud, Ghada S. Moussa, and Khaled F. Hussain. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach." Operations Research Perspectives 6 (2019): 100100. http://dx.doi.org/10.1016/j.orp.2019.100100.

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Zubrilina, E. M., and O. S. Babenko. "Application of Methods for Solving Multi-Criteria Problems when Choosing Precision Seeders." E3S Web of Conferences 398 (2023): 01032. http://dx.doi.org/10.1051/e3sconf/202339801032.

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This article presents the process of choosing a precise seeding drill model using the methods of hierarchy analysis and generalized estimation. The results of the analysis represent preferred alternatives and recommendations for decision-making. The importance of the role of experts and their subjective opinions when using these methods is also emphasized.

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Dmitrenko, Alexei, Sergei Karasev, Alexandra Kalidova, and Dmitry Sivitsky. "Estimation of conditions of using combined single-track and double-track railways at high-speed traffic." MATEC Web of Conferences 216 (2018): 02010. http://dx.doi.org/10.1051/matecconf/201821602010.

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An important condition when arranging high-speed railway transportation with insignificant traffic is saving of capital investments in construction of new lines or the reconstruction the existing ones. Development of methods for multi-criteria estimation of the possibility of using fully single-track blocks on a line with high-speed traffic, as well as single-track blocks with double-track inserts, is a relevant issue. The purpose of the study is to develop the criteria and the method for estimation of the feasibility of using single-track elements on a line with high-speed traffic. Methods of simulation modeling, mathematical statistics, regression analysis were used. A method that includes estimation of the technological conditions of using single-track elements on a line with high-speed traffic instead of double-track blocks was developed. The method can be used for multi-criteria optimization of the configuration of a combined single-track and a double-track line using single-track elements in high-cost areas.

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Kumar, Ajay. "Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making." JUCS - Journal of Universal Computer Science 30, no. (2) (2024): 221–41. https://doi.org/10.3897/jucs.110051.

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For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project's development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.

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Singh, Seema, D. S. Hooda, and S. C. Malik. "Estimation of missing data in intuitionistic fuzzy soft matrix (IFSM) with application in MCDM." Journal of Statistics and Management Systems 27, no. 3 (2024): 589–603. http://dx.doi.org/10.47974/jsms-1022.

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The theory of fuzzy soft as well as intuitionistic soft set generally depends on complete information. But there are situations in which the complete information is not available due to various different reasons like data erroneous, in sufficiency of data, lacking of evidence and illegibility of data. Missing data restrict fuzzy soft matrix and intuitionistic fuzzy soft matrix and that causes more ambiguous uncertainty in the process of decision making. Thus, estimation of incomplete information data plays an important role in multi criteria decision making process which has been developed recently. Here, a novel method of missing data estimation in intuitionistic fuzzy soft matrix is described. Firstly, we estimate the missing values using the information given in data. After estimation the missing values in IFSM thus obtained is applied in a multi-criteria decision-making problem with illustration by an example.

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Bharadwaj, Madhu, Prajwal Rao, and Sowmianarayanan Srinivasan. "Implementation of Multi-Axial Fatigue Theory in FE Packages." Key Engineering Materials 719 (November 2016): 46–54. http://dx.doi.org/10.4028/www.scientific.net/kem.719.46.

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Many mechanical or structural components are subjected to multi-axial, irregular cyclic loading during service. The direction and amplitude of principal stress and strain vary over a period of time results in non-proportional cyclic loading on the component. At geometrical discontinuities, even a monotonic load will result in multi-axial state of stress. In general, the life of the components subjected to multi-axial stress loadings, are evaluated using classical yield theories. The Tresca and von Mises criterions along with Basquin-Coffin and Manson life curve are widely used in commercially available Finite Element Analysis (FEA) tools. These classical methods are conservative and may not yield good experimental correlation at all the loading conditions and this augments the need for robust life estimation methodology.There are many commercially available FEA tools to estimate the multi-axial fatigue life viz. nCODE® which uses Wang-Brown method [1]. However, it has been found that for shear dominated fatigue material Fatemi-Socie criteria is more suitable. So an attempt is made to develop a an algorithm to implement Fatemi-Socie criteria in a commercially available generic FEA software in a cost effective way. This paper discusses how to estimate the life of a sample specimen subjected to multi-axial and non-proportional loading conditions. The classical yield criteria based on von-Mises stress with Basquin-Coffin and Manson equation and critical plane method viz, Fatemi-Socie criteria are implemented in to commercial FEA tool, ANSYS. This paper also attempt to see how these theories compare with experimental data. Results of this study would help in leveraging the established process of implementing custom based life estimation method in ANSYS for the estimation of the life of the mechanical components.

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Jia, Jianmin, Mohamed Ibrahim, Mohammed Hadi, Wallied Orabi, and Yan Xiao. "Multi-Criteria Evaluation Framework in Selection of Accelerated Bridge Construction (ABC) Method." Sustainability 10, no. 11 (2018): 4059. http://dx.doi.org/10.3390/su10114059.

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Accelerated Bridge Construction (ABC) is bridge construction that uses innovative planning, design and construction methods in a safe and cost-effective manner, which reduces construction mobility and environmental impacts, and contributes to city sustainable planning and development. To deal with the pressing need to support the decisions associated with the selection between the ABC and conventional bridge construction, this paper presents the development of a multi-criteria evaluation framework. Methods are developed and identified to estimate the construction, agency, and user costs associated with the construction methods. A novel model was developed to allow the estimation of the construction and agency costs of ABC relative to conventional construction. This paper also demonstrates the estimation of user costs, including those associated with mobility, reliability, safety, and emissions, utilizing combinations of the proposed prediction method. The paper then compares the use of the return-on-investment and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) Multi-Criteria Decision Making (MCDM) evaluation approaches in the decision to select between ABC and conventional bridge construction. The results from the employment of the two approaches to a case study demonstrate the advantage of using the TOPSIS approach, which is also applicable in the urban planning process.

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Kumar, Ajay. "Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making." JUCS - Journal of Universal Computer Science 30, no. 2 (2024): 221–41. http://dx.doi.org/10.3897/jucs.110051.

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&nbsp;For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project&#39;s development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.

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Wagh, Rupali S., and Deepa Anand. "Legal document similarity: a multi-criteria decision-making perspective." PeerJ Computer Science 6 (March 23, 2020): e262. http://dx.doi.org/10.7717/peerj-cs.262.

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The vast volume of documents available in legal databases demands effective information retrieval approaches which take into consideration the intricacies of the legal domain. Relevant document retrieval is the backbone of the legal domain. The concept of relevance in the legal domain is very complex and multi-faceted. In this work, we propose a novel approach of concept based similarity estimation among court judgments. We use a graph-based method, to identify prominent concepts present in a judgment and extract sentences representative of these concepts. The sentences and concepts so mined are used to express/visualize likeness among concepts between a pair of documents from different perspectives. We also propose to aggregate the different levels of matching so obtained into one measure quantifying the level of similarity between a judgment pair. We employ the ordered weighted average (OWA) family of aggregation operators for obtaining the similarity value. The experimental results suggest that the proposed approach of concept based similarity is effective in the extraction of relevant legal documents and performs better than other competing techniques. Additionally, the proposed two-level abstraction of similarity enables informative visualization for deeper insights into case relevance.

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Jindal, Aparna, Rakesh Garg, and Ashu Bansal. "Ranking of Software Effort Estimation Selection Criteria Based On Fuzzy Set Theory." International Journal of Advance Research and Innovation 4, no. 1 (2016): 57–62. http://dx.doi.org/10.51976/ijari.411609.

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The selection and evaluation of Software effort estimation models has always been a challenging task for the software developers and the project managers. A lot of research has been done by various researchers on this by considering it as multi-criteria decision making problem. So, a better understanding of various selection criteria and their importance in this regard is required. In this paper, first the identification of the various software effort estimation model selection criteria is done, then by applying fuzzy set theory the local and global weights of these selection criteria are calculated and finally the selection criteria are ranked according to their global weights showing the importance of each criterion.

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Janušaitis, Rytis. "ESTIMATION OF ALTERNATIVE WALL INSULATING SOLUTIONS USING THE METHOD OF TECHNOLOGICAL NETWORK MODEL “CUTTING OUT” JUNCTIONS/SIENŲ ŠILTINIMO ALTERNATYVIŲ SPRENDIMŲ ĮVERTINIMAS, TAIKANT TECHNOLOGINIO TINKLINIO MODELIO MAZGŲ “IŠPJOVIMO” METODĄ." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 4, no. 2 (1998): 161–70. http://dx.doi.org/10.3846/13921525.1998.10531397.

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The article investigates the problem of the dwelling house wall insulation of many-variant decisions estimation. At present multi-criteria evaluation methods are used. They provide the formal algorithm of the final effective selection of solution. However, during the optimisation of the decisions it is expedient to analyse the efficiency of intermediate decisions and those influencing the final findings. With that end in view the author suggests the multi- criteria problem of the evaluations of the variant decision using the method or technological network model of “cutting out” junctions. The main points of this method are: To cut out a comparable variant junction. Comparable variant junction is such a junction that contains at least two technological process variants. To determine comparable partial processes to the junction. To carry out a multi-criteria evaluation. To eliminate non-rational variants from further calculations. To pick out the most effective decision for the junction. From such decisions the complex process variants of wall insulation are made and multi-criteria evaluation is repeated. This method increases the abilities to raise the number of the partial alternative decisions for establishing the effectiveness ot the intermediate decisions, to receive and verify quickly final results of the decision evaluation and to decrease the amount of the calculations. “Cutting out” junction method can be used for estimating the decisions of building thermal renovation at the designing stage.

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Petrovic, Nikola, Branko Djedovic, and Dragana Petrovic. "Project estimation and selection using the cost: Effectiveness analysis and multi-criteria analysis." Vojnotehnicki glasnik 60, no. 3 (2012): 168–87. http://dx.doi.org/10.5937/vojtehg1203168p.

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Mohammadpour, M., M. H. Kalajahi, R. H. Oskouei, and M. Shakouri. "Fatigue life estimation of coach peel riveted joints using multi-axial fatigue criteria." Materials & Design (1980-2015) 62 (October 2014): 327–33. http://dx.doi.org/10.1016/j.matdes.2014.05.033.

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Cavalcante, Charles Casimiro, and João Marcos T. Romano. "Multi-user pdf estimation based criteria for adaptive blind separation of discrete sources." Signal Processing 85, no. 5 (2005): 1059–72. http://dx.doi.org/10.1016/j.sigpro.2004.11.023.

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Swaid, Bashar, Eleonora Bilotta, Pietro Pantano, and Roberta Lucente. "Emergence nonlinear Multifractal architecture by Hypervolume estimation algorithm for evolutionary multi-criteria optimisation." International Journal of Parallel, Emergent and Distributed Systems 32, sup1 (2017): S101—S113. http://dx.doi.org/10.1080/17445760.2017.1390094.

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Bostenaru Dan, M. D. "Multi-criteria decision model for retrofitting existing buildings." Natural Hazards and Earth System Sciences 4, no. 4 (2004): 485–99. http://dx.doi.org/10.5194/nhess-4-485-2004.

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Abstract. Decision is an element in the risk management process. In this paper the way how science can help in decision making and implementation for retrofitting buildings in earthquake prone urban areas is investigated. In such interventions actors from various spheres are involved. Their interests range among minimising the intervention for maximal preservation or increasing it for seismic safety. Research was conducted to see how to facilitate collaboration between these actors. A particular attention was given to the role of time in actors' preferences. For this reason, on decision level, both the processural and the personal dimension of risk management, the later seen as a task, were considered. A systematic approach was employed to determine the functional structure of a participative decision model. Three layers on which actors implied in this multi-criteria decision problem interact were identified: town, building and element. So-called 'retrofit elements' are characteristic bearers in the architectural survey, engineering simulations, costs estimation and define the realms perceived by the inhabitants. This way they represent an interaction basis for the interest groups considered in a deeper study. Such orientation means for actors' interaction were designed on other levels of intervention as well. Finally, an 'experiment' for the implementation of the decision model is presented: a strategic plan for an urban intervention towards reduction of earthquake hazard impact through retrofitting. A systematic approach proves thus to be a very good communication basis among the participants in the seismic risk management process. Nevertheless, it can only be applied in later phases (decision, implementation, control) only, since it serves verifying and improving solution and not developing the concept. The 'retrofit elements' are a typical example of the detailing degree reached in the retrofit design plans in these phases.

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Berezkin, А., R. Vivchar, and R. Kirichek. "Multi-Criteria Evaluation of UAV Control Efficiency in Hybrid Communication Networks." Proceedings of Telecommunication Universities 10, no. 1 (2024): 18–25. http://dx.doi.org/10.31854/1813-324x-2024-10-1-18-25.

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Currently, there is an active growth in the use of unmanned systems in various spheres of activity. The qualitative fulfillment by unmanned systems of their target tasks depends on the efficiency of information exchange channels for their control, which consists of several criteria, which makes the problem of its evaluation a multi-criteria one. This article presents the corresponding evaluation methodology, which is based on the use of the probability of achieving a set of functioning goals as a generalized efficiency indicator. To determine the probability, the widely used Rosenblatt-Parzen nuclear estimation method is used. The main stages of the methodology are described and recommendations for its use in the framework of ensuring qualitative fulfillment by unmanned systems of their target tasks are offered.

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Pažek, Karmen, and Črtomir Rozman. "Tourist Farm Service Quality Assessment." Journal for Geography 5, no. 2 (2010): 149–58. http://dx.doi.org/10.18690/rg.5.2.4038.

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Farm tourism shows a structure different from agriculture, forestry, and fishery; farm tourism is expanding, labour intensive, generates an influx of money, and maintains the service base in the region. Farm tourism is one of the most important supplementary activities and generates considerable secondary income. This paper presents the developed methodology that will enable the ranking of tourist farms based on their service quality. This is accomplished through the use of the expert system based on the DEXi decision-making method. Using DEXi, the multi-criteria decision model for the assessment of farm tourism's service quality was developed. The model was applied in practice on 7 vacation farms. The data from the questionnaires completed by farm guests and hosts was used as input data in the multi-attribute model and as an arbitrary estimation for the farm. The results of the model are shown as the assessments for individual farms. Out of seven chosen farms, four achieved the best possible estimation. Two of them achieved middle estimation. For the last tourist farm, where a data deficit was present, the model showed two final possible estimations (very good or good and bad - depends on value of missing attributes).

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Isayev, K. H., B. Kh Bekirova, E. Sh Garagozov, and S. A. Ilyasov. "Theoretic-practical approaches to the implementation of method of balanced system of parameters in evaluation of labour productivity in oil-gas developing enterprises." Azerbaijan Oil Industry, no. 4 (April 15, 2021): 43–47. http://dx.doi.org/10.37474/0365-8554/2021-4-43-47.

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Nowadays, it is necessary to consider non-financial aspects in the models of efficiency evaluation towards the estimation of business success in all respects. With this purpose, the paper presents multi-criteria models for the elimination of shortcomings and drawbacks of traditional efficiency estimation systems. Therefore, the purpose of the research is to create balanced system of parameters considering importance of multi-dimensional models for efficiency evaluation. The criterion specified by OGPD for Balanced Scorecard almost reach the values forecasted from the aspects of financies, consumers and staff training and development.

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Hernández, Heber. "A semiautomatic multi criteria method for mineral resources classification." Applied Earth Science: Transactions of the Institutions of Mining and Metallurgy 133, no. 4 (2024): 211–23. https://doi.org/10.1177/25726838241298187.

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Mineral resources are classified as measured, indicated, or inferred based on the geological confidence derived from estimation results and the quality and quantity of available data. In the mining industry, there are numerous accepted criteria for this classification. The selection and combination of these criteria, determined through arbitrary decision rules, depend on the characteristics of each project and are the responsibility of a qualified person. The problem with the current methodology is that the same data, using the same criteria, can yield different results depending on the person analyzing them. This contradicts the need for objectivity in the classification process, creates uncertainty in decision-making, and diminishes investor confidence. This article presents an innovative methodology for mineral resource classification using machine learning, which complements the work of the qualified person, reduces time costs, decreases subjectivity, and simplifies the reproducibility and auditability of the process, aligning with the fundamental principles of classification. The proposed methodology consists of three stages: first, classification criteria are selected, recording variables block by block. Then, blocks are grouped by similarity using the k-prototypes algorithm. Finally, the blocks are smoothed using a multilayer neural network to correct the "spotted dog" effect. This methodology is applied in two cases: a porphyry copper model and a high-sulfidation gold deposit in southern Peru. The results obtained reflect the fundamental principles of classification: transparency, objectivity, reproducibility, and auditability based on quantifiable principles. Additionally, the methodology ensures results consistent with the utilized characteristics, imposes no restriction on the acceptability of any type of input information, reduces time costs, and limits human intervention.

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Shahhoseini, Zahra, Milad Haghani, and Majid Sarvi. "Estimation and application of a multi-class multi-criteria mixed paired combinatorial logit model for transport networks analysis." Transportmetrica B: Transport Dynamics 3, no. 1 (2014): 59–78. http://dx.doi.org/10.1080/21680566.2014.955148.

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Esangbedo, Moses Olabhele, and Sijun Bai. "Grey Regulatory Focus Theory Weighting Method for the Multi-Criteria Decision-Making Problem in Evaluating University Reputation." Symmetry 11, no. 2 (2019): 230. http://dx.doi.org/10.3390/sym11020230.

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Evaluating the reputation of higher institutions is important as a benchmark for the continuous development of universities. In multi-criteria decision-making (MCDM), the estimation of the criteria weight is significant in improving the accuracy of the evaluation results. However, criteria weights assigned by decision makers (DMs) can be inaccurate when not measured from the orientations from which the DMs pursue their decision goals. This paper combines the grey system theory (GST) and regulatory focus theory (RFT) in estimating the weights of criteria by measuring the promotional and prevention focus orientations of the DMs and representing it as grey numbers. In this paper, we showed not all weight measurements from the DMs are symmetrical. In essence, to improve the accuracy of the MCDM weights, the grey regulatory focus theory (GRFT) weighting method is proposed to estimate the weights of an MCDM problem as well as aggregating the weights of the group DMs, thereby overcoming the limitation of a single orientation measurement. Furthermore, the GRFT weighting method is applied in ranking four universities in Xi’an city of China from a sample data from 1,200 students. The most reputable university is consistent with the other ranking indexes in the literature.

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Korshunov, G. I., V. A. Smirnov, and V. M. Milova. "Multi-criteria fuzzy model for system technical condition estimation at the life cycle stages." IOP Conference Series: Materials Science and Engineering 537 (June 18, 2019): 042019. http://dx.doi.org/10.1088/1757-899x/537/4/042019.

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Pei, Huining, Jingru Cao, Man Ding, Ziyu Wang, Yunfeng Chen, and Fanghua Zhao. "A multi-criteria fusion-based pose estimation method for civil aircraft maintenance operation tasks." Advanced Design Research 2, no. 2 (2024): 98–114. https://doi.org/10.1016/j.ijadr.2024.12.001.

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Nord, Erik. "Beyond QALYs: Multi-criteria based estimation of maximum willingness to pay for health technologies." European Journal of Health Economics 19, no. 2 (2017): 267–75. http://dx.doi.org/10.1007/s10198-017-0882-x.

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Amri, Fouad, Omar Bouattane, Tajeddine Khalili, Abdelhadi Raihani, and Abdelkader Bifadene. "Toward an Evolutionary Multi-Criteria Model for the Analysis and Estimation of Wind Potential." Journal of Power and Energy Engineering 03, no. 11 (2015): 14–28. http://dx.doi.org/10.4236/jpee.2015.311002.

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Rouhani, Hamed, Patrick Willems, Guido Wyseure, and Jan Feyen. "Parameter estimation in semi-distributed hydrological catchment modelling using a multi-criteria objective function." Hydrological Processes 21, no. 22 (2007): 2998–3008. http://dx.doi.org/10.1002/hyp.6527.

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Chelariu, Oana-Elena, Ionuț Minea, and Corneliu Iațu. "Geo-hazards assessment and land suitability estimation for spatial planning using multi-criteria analysis." Heliyon 9, no. 7 (2023): e18159. http://dx.doi.org/10.1016/j.heliyon.2023.e18159.

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Jia, Jidong, Minglu Zhang, Xizhe Zang, He Zhang, and Jie Zhao. "Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design." Sensors 19, no. 10 (2019): 2248. http://dx.doi.org/10.3390/s19102248.

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As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

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Polikarpova, Ilze, Dace Lauka, Dagnija Blumberga, and Edgars Vigants. "Multi-Criteria Analysis to Select Renewable Energy Solution for District Heating System." Environmental and Climate Technologies 23, no. 3 (2019): 101–9. http://dx.doi.org/10.2478/rtuect-2019-0082.

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Abstract The research paper is focused on how to choose the most suitable renewable energy solution using multi-criteria analysis for a district heating system. Making choices is based on indicators – economic, environmental and social. In this case, the study used five indicators: resources costs, totals investments, reduction of greenhouse gas emissions, specific weight of renewable energy resources and impact on utilized land. Three situations have been compared – current situation: gas boiler, planned: solar collectors 21 595 m2 + accumulation tank 8000 m3 and alternatives: solar PV panels 5504 m2 – heat pump COP 3 are used in the estimation. The multi-criteria decision-making analysis shows that solar collectors 21 595 m2 + accumulation tank 8000 m3 are considered as the best alternative. The methodology is based on choosing a solution for a district heating company in Latvia.

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Höller, Johannes, Patricia Bickert, Patrick Schwartz, et al. "Parameter Estimation Strategies in Thermodynamics." ChemEngineering 3, no. 2 (2019): 56. http://dx.doi.org/10.3390/chemengineering3020056.

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Many thermodynamic models used in practice are at least partially empirical and thus require the determination of certain parameters using experimental data. However, due to the complexity of the models involved as well as the inhomogeneity of available data, a straightforward application of basic methods often does not yield a satisfactory result. This work compares three different strategies for the numerical solution of parameter estimation problems, including errors both in the input and in the output variables. Additionally, the new idea to apply multi-criteria optimization techniques to parameter estimation problems is presented. Finally, strategies for the estimation and propagation of the model errors are discussed.

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Chattopadhyay, Ayan, and Upasana Bose. "An Assessment of Objectivity Convergence of Fuzzy TOPSIS Method Extended With Rank Order Weights in Group Decision Making." Asian Journal of Managerial Science 7, no. 3 (2018): 26–33. http://dx.doi.org/10.51983/ajms-2018.7.3.1347.

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Group decision making in a multi criteria environment is a familiar business situation where the decision makers identify an ideal choice, among many. The situation gets complex when decision makers do not have crisp data to deal with. The fuzzy TOPSIS method, and its likes, provides solution to such problems and the criteria weight plays a determinant role in the overall priority estimation. This paper presents an extended fuzzy TOPSIS approach by incorporating criteria weights derived from rank order. It considers three criteria weights; the rank order centroid, rank sum and rank reciprocal weights. The criteria weights are calculated separately and integrated with fuzzy TOPSIS method to rank choices. Finally, objectivity convergence of the alternative rankings is tested. The proposed method yields a fairly uniform and consistent result in the case of supply chain management and anticipates wide application in multi criteria environment, concomitant with uncertainty and vagueness.

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Giel, Robert, and Artur Kierzkowski. "A Fuzzy Multi-Criteria Model for Municipal Waste Treatment Systems Evaluation including Energy Recovery." Energies 15, no. 1 (2021): 31. http://dx.doi.org/10.3390/en15010031.

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One of the recent problems on waste sorting systems is their performance evaluation for proper decision making and management. For this purpose, multi-criteria methods can be used to evaluate the sorting system from both operational and financial perspectives. According to a recent literature review, there are no solutions for evaluating waste sorting systems that take into account: sorting point utilisation, sorting efficiency, waste stream irregularity, and technical system availability. In addition, the problem of data uncertainty and the need to use expert judgements indicate the need for the implementation of methods adjusted to the qualitative and quantitative assessment, such as the fuzzy approach. Following this, in order to overcome the presented limitations, the authors introduced the new assessment method for waste sorting systems based on multi-criteria model implementation and fuzzy theory use. Therefore, the developed model was based on a hierarchical fuzzy logic model for which appropriate membership function parameters and inference rules were defined. The specificity of the chosen assessment criteria and their justification was provided. The model has been implemented to evaluate one of the waste sorting plants in Wroclaw, Poland. Tests have been conducted for seven different configurations of waste sorting lines (with variable input parameters). The study focuses on analysing the amount of selected waste at each station in relation to the total stream size of each fraction. Efficiency was measured by the mass of the collected waste and the number of pieces of waste in each fraction. Based on the obtained results, estimations of particular parameters of the model were made, and the results were presented and commented on. It was shown that there is a significant relationship between the level of system evaluation and sorting efficiency and an inverse relationship with the level of RDF obtained. The analysis was based on Pearson’s linear correlation coefficient estimation and linear regression implementation.

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