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This paper presents the reliability analysis on the basis of the foundation failure against bearing capacity using the concept of fuzzy set theory. A surface strip footing is considered for the analysis and the bearing capacity is estimated using the conventional Finite Element Method (FEM). The spatial variability of the variables is taken into consideration to capture the physical randomness of the soil parameters for an isotropic field. A variation of the probability of failure (Pf) against a varying limiting applied pressure (q) is presented for different Coefficient of Variation (COV) of the variables and different scale of fluctuation (θ). The results reveal that the friction angle of soil (ϕ) is the most influencing parameter among the other variables. Further, the influence of the scale of fluctuation (θ) on the probability of failure (Pf) is also examined. It is observed that for a particular COV of ϕ, higher value of θ predicts higher Pf whereas, Pf increases as COV of ϕ increases for a particular θ value. Later, a comparison study is accomplished to verify the viability of the present method and it can be noticed that the present method compares well with the other reliability method (First Order Reliability Method) to a reasonably good extent. [ABSTRACT FROM AUTHOR]

• Proposing a physical asset criticality analysis decision model based on BIA. • Risk Appetite and physical asset continuity measures are related. • Defining sub-criteria for impact and likelihood estimation in criticality analysis. The paper tackles the issue of managing physical assets in asset-intensive industries, where continuity plans are crucial for business operations during disruptions. However, resource constraints often hinder companies from implementing these plans for all their assets. Moreover, efforts to maximize resource utilization efficiency and minimize environmental impacts are limited to isolated disciplines, necessitating an integrated approach across natural, social, and technological systems. To address these challenges, this study proposes a framework based on a hybrid fuzzy BWM-TOPSIS methodology for identifying critical physical assets. The framework incorporates Business Impact Analysis (BIA) to determine essential products, a physical asset risk assessment matrix (PARAM) to assess critical assets based on likelihood and impact, and a mathematical model to estimate asset continuity parameters. The framework is applied to a petrochemical company in a case study, offering a guide for estimating the probability and impact of physical asset failure while accounting for inherent uncertainties. By adopting this framework, companies can efficiently manage their physical assets, prioritize continuity plans, and address resource constraints and uncertainties. This research contributes to practical tools and approaches that assist industries in sustaining operations and ensuring business continuity during disruptions. [ABSTRACT FROM AUTHOR]

Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data. [ABSTRACT FROM AUTHOR]

Copyright of Scientific Bulletin of National Mining University is the property of National Mining University, State Higher Educational Institution and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multiple routes of networks in fuzzy environments are essential issues in the project scheduling problems (PSPs) with resource constraints, fuzzy RCPSP-MR. Route assignment to flexible work package defined in a project activity network indicates more complexities in front of canonical PSP. Also, in the last few decades, considering uncertainties' concepts in project schedules have been essential and attracted the attention of researchers and project managers. Therefore, in this article, a new weighted mathematical model is presented under uncertainty conditions, and a new hybrid fuzzy approach is provided via two fuzzy primary methods. Then, a new four-part non-distinct (FPND) approach is proposed based on PSO, binary particle swarm optimization (BPSO) and genetic algorithm (GA) to minimize project end cost. In this approach as the first part and to generate high-quality primary routes for flexible work package, six different rules are investigated, and the appropriate route is chosen. In the second part, initial solutions are generated via PSO. Then, in the third part, initial solutions are improved based on GA. Finally, in the last part, assigned routes are improved with binary PSO. To appraise the effectiveness of the presented approach, influential parameters are tuned by Taguchi method. Finally, to evaluate the performance of FPND, 70 numerical examples are designed in different dimensions, and results are compared with other well-known algorithms. [ABSTRACT FROM AUTHOR]

Based on the analysis of traditional calculation method of RPN (Risk Priority Number), a risk assessment method for CNC lathes based on failure data and fuzzy set theory combining with grey theory was proposed in this paper. Severity, occurrence and detection were treated as linguistic variables. Meanwhile, failure data was applied and maintenance time was introduced as another variable. Risk prioritization of all the failure modes was obtained through steps such as expert rating, solving α level sets, defuzzification and so on. Grey theory was applied to verify the accuracy of the proposed method. The method was used in a real case of risk analysis of spindle system of a CNC lathe, in which consistency and practicability of the results were illustrated. It can be concluded that the proposed method can take advantage of failure data and control subjectivity in analysis. In addition, importance weights of the four variables were taken into consideration. So that the drawbacks of traditional RPN analysis can be avoided and improvement measures could be made by designers and technologists according to the results so as to improve reliability of CNC lathes. [ABSTRACT FROM AUTHOR]

The development of a scientific discipline is partly determined by the growth of its literature. Bibliometrics constitute a set of mathematical and statistical techniques employed to explore, organize, and quantitatively analyze large pools of scientific literature. In this study, 11,433 papers published between 1974 and 2015 are collected and analyzed by applying life-cycle analysis, bibliometrics and text mining in order to provide a full thematic overview of the application of fuzzy theory in operations management. Top countries (i.e. the most productive, influential and collaborative countries), most frequently cited journals, and key research topics are identified by employing bibliometric techniques. Finally, the articles' common research themes and methodologies are determined by combining text mining with experts' input. Results show that the application of fuzzy theory in operations management is still in its infancy, and hence suitable for future research. This study serves as a guide for researchers to identify the hidden patterns, key actors, topics, and methods in this area. [ABSTRACT FROM AUTHOR]

A number of chronic poverty measures are now empirically applied to quantify the prevalence and intensity of chronic poverty, vis‐à‐vis transient experiences, using panel data. Welfare trajectories over time are assessed in order to identify the chronically poor and distinguish them from the non‐poor, or the transiently poor, and assess the extent and intensity of intertemporal poverty. We examine the implications of measurement error in the welfare outcome for some popular discontinuous chronic poverty measures, and propose corrections to these measures that seeks to minimize the consequences of measurement error. The approach is based on a novel criterion for the identification of chronic poverty that draws on fuzzy set theory. We illustrate the empirical relevance of the approach with a panel dataset from rural Ethiopia and some simulations. [ABSTRACT FROM AUTHOR]

Condition information is essential to develop effective facility management (FM) strategies. Visual inspections and walk-through surveys are common practices of condition assessment (CA), generally resulting in qualitative and subjective outcomes such as “poor”, “good”, etc. Furthermore, limited resources of the FM process demand that CA practices be efficient. Given these, the purpose of this study is to develop a resource efficient quantitative CA framework that can be less subjective in establishing a condition rating. The condition variables of the study—mean time between failures, age-based obsolescence, facility condition index, occupant feedback, and preventive maintenance cycle—are identified through different sources, such as a computerized maintenance management system, expert opinions, occupants, and industry standards. These variables provide proxy measures for determining the condition of equipment with the implementation example for heating, ventilating, and air conditioning equipment. Fuzzy sets theory is utilized to obtain a quantitative condition rating while minimizing subjectivity, as fuzzy sets theory deals with imprecise, uncertain, and ambiguous judgments with membership relations. The proposed CA framework does not require additional resources, and the obtained condition rating value supports decision-making for building maintenance management and strategic planning in FM, with a comprehensive and less subjective understanding of condition.

Resumen. Los Recursos Humanos constituyen los únicos elementos vivos presentes en todo tipo de organización, quienes, a partir de sus conocimientos, habilidades y motivaciones, impulsan a las empresas a ser competitivas y a alcanzar sus metas. En aras de lograr dicho objetivo se hace necesario contar con herramientas que ayuden a que el proceso de toma de decisiones concerniente a la gestión de los mismos sea lo más certeros y objetivos. El presente trabajo propone un modelo de gestión de recursos humanos que integra herramientas basadas en la teoría de los subconjuntos borrosos. La investigación se encuentra estructurada en tres epígrafes. El primero analiza los modelos utilizados en Cuba para gestionar los recursos humanos. El segundo presenta el Modelo de Gestión de los Recursos Humanos que se propone, para finalmente, en un tercer epígrafe proceder a validarlo. La novedad del trabajo que se propone, radica en la integración de herramientas de la teoría de los subconjuntos borrosos en los procesos claves de la Gestión de los Recursos Humanos. Entre sus aportes se encuentran: la aplicación del método de alisado exponencial borroso a la planeación del personal, la extrapolación de un modelo de asignación resuelto a través del algoritmo húngaro a la formación del personal, así como la construcción de un sistema de indicadores borrosos para la evaluación de la Gestión de los Recursos Humanos. La propuesta fue validada a través de aplicaciones parciales en empresas seleccionadas así como con criterio de expertos. English abstract. Human resources are the only living elements present in all types of organizations who, from their knowledge, skills and motivations driving companies to be competitive and achieve their objectives and goals. In order to achieve this objective it is necessary to have tools that help the decision-making process regarding the management of human resources is as accurate and objective as possible. This paper proposes a Human resources management model based on fuzzy subsets theory to help reduce subjectivity that may be implied in these processes tools. This paper has been structured in three epigraphs. The first one presents an analysis of the Human Resources management Models used in Cuba. The second present the proposal of the Human Resources Management Model based on fuzzy subsets theory and finally on third epigraph the proposal is validated. The novelty of the proposed work lies in the integration of tools of the theory of fuzzy subsets in the key processes of Human Resources Management. Among the main contributions are: the application of the method of fuzzy to planning staff exponential smoothing, extrapolation of an allocation model solved through the Hungarian algorithm to staff training and building a system of fuzzy indicators for the evaluation of Human Resources Management. The proposal was validated through partial applications in companies in GECOMEX well as expert judgment.

The threat evaluation is significant component in target classification process and is significant in military and non military applications. Small errors or mistakes in threat evaluation and target classification especial in military applications can result in huge damage of life and property. Threat evaluation helps in case of weapon assignment, and intelligence sensor support system. It is very important factor to analyze the behavior of enemy tactics as well as our surveillance. This paper represented a precise description of the threat evaluation process using fuzzy sets theory. A review has been carried out regarding which parameters that have been suggested for threat value calculation. For the first time in this paper, eleven parameters are introduced for threat evaluation so that this parameters increase the accuracy in designed system. The implemented threat evaluation system has been applied to a synthetic air defense scenario and four real time dynamic air defense scenarios. The simulation results show the correctness, accuracy, reliability and minimum errors in designing of threat evaluation system

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Design and performance of stormwater infrastructure systems in urban areas have direct implications in social, environmental and public health problems and are of utmost importance to urban authorities and managers. Risk analysis in urban stormwater systems has become a must because of the extensive consequences of flooding in urban areas and limited funding for the rehabilitation and renovation of stormwater systems. Complexity, multidimensionality, and inherent uncertainties of the urban stormwater systems require the risk analysis approach to be comprehensive and able to address different uncertainties and spatial aspects of the problem. The objective of this study is to provide a comprehensive framework for risk analysis in urban stormwater systems. Multi Criteria Decision Making (MCDM), geographic information systems (GIS), and fuzzy sets theory are used to consider the diverse risk-affecting criteria, facilitate the analysis of spatial data and information and formulate the ambiguity and uncertainty of problem, respectively. The presented framework uses a Fuzzy Analytic Hierarchy Process (FAHP) for determining the weights of risk-affecting criteria (i.e. Hydrological and Hydraulic, Traffic, Social, Economic, Environmental, Structural, and Green space) in the presence of multiple decision makers. The Autodesk Storm and Sanitary Analysis model is used for the Hydrological and Hydraulic simulations. FSAW and FTOPSIS methods are used to provide the final product of the framework, i.e., a risk map that presents a risk level for each channel in the network. The framework is applied to District 11 of the capital city of Iran, Tehran as a real case study. The resulted risk maps indicate a high or very high flooding risk for 17.07 to 41.95 km of the stormwater channels in the study area, covering about 10.5 to 26% of the total length of the channels. The presented framework was found to be a suitable risk analysis tool in urban stormwater systems. Graphical abstract Unlabelled Image Highlights • Urban stormwater systems failure can lead to flooding risk and different problems. • A spatial MCDM based risk analysis framework for these systems is developed. • Inherent uncertainties are considered by applying Fuzzy sets theory. • The framework is employed for risk assessment in a central district in Tehran, Iran. [ABSTRACT FROM AUTHOR]

Abstract Uncertainties in the input parameters of predictive models for uplift capacity of suction caisson lead to uncertain performance. To account for the influence of such uncertainties on the suction caisson stability, a fuzzy model is employed. The input uncertainties are introduced to the predictive formulas by triangular membership functions. To obtain the extreme values of the uplift capacity, optimization problems with many objectives are solved by the coupling of genetic algorithm (GA) with the uplift capacity estimation models. The relationships derived by different methods for the prediction of the uplift capacity, are analyzed by the fuzzy approach using a compiled database. Through the membership functions of several statistical measures, it is inferred that small input uncertainties can significantly affect the responses. Also, the recently proposed "M5-GP" - based prediction models are found to be vulnerable to input uncertainties, hence, a new revision called "Improved M5GP" model is developed. Finally, it is shown that the presented model is the best among various models regarding the reliability as well as the accuracy. Highlights • Effects of input uncertainties on the uplift capacity are assessed by a fuzzy model. • Different available predictive formulae are investigated with a compiled database. • Reliability of models for engineering practice are compared by performance indices. • The M5-GP prediction models for uplift capacity of suction caissons are improved. • The new model is found to be more accurate and reliable than the previous ones. [ABSTRACT FROM AUTHOR]

Best Worst Method (BWM) has recently been proposed as a method for Multi Criteria Decision Making (MCDM). Studies show that BWM compared with other methods such as Analytic Hierarchy Process (AHP), leads to lower inconsistency of the results while reducing the number of required pairwise comparisons. MCDM methods such as BWM require accurate information. However, it often happens in practice that a level of uncertainty accompanies the information. The main aim of this paper is to address this problem and provide an integration of BWM and Z-numbers, namely ZBWM. Providing BWM with Z-numbers enables the BWM method to handle the uncertainty of information of a multi-criteria decision. Additionally, the capabilities of the proposed method in the process of utilizing the linguistic information dealing with big data are highlighted. The proposed method is examined to address a supplier development problem. By experimental results, we show that ZBWM results lower inconsistency when compared with BWM. A Z-number contains subjectivity in its fuzzy part, which can be addressed in future applications of ZBWM. [ABSTRACT FROM AUTHOR]

For transient analysis of a pipe network, the unsteady flow governing equations should be solved to obtain the extreme pressure heads in the system, which may be faced with several uncertainties. To evaluate that to what extent the input uncertainties can affect the system responses, a simulation model based on the fuzzy sets theory is introduced. For this purpose, triangular fuzzy numbers are used to represent the input uncertainties. Then, to obtain the extreme pressure heads in each location of the network and at each level of uncertainty, four independent optimization problems are solved. In these problems, the nodal maximum and minimum pressure heads are the objective functions and the simulation parameters are the decision variables. Accordingly, for fuzzy analysis of a pipe network, a complicated many-objective optimization problem arises. To solve the problem efficiently a many-objective genetic algorithm is coupled to the transient simulation model. To speed up the analysis, a transient simulation model in the frequency domain is used. The proposed model is applied to a pipe network and the results are discussed. The model is found computationally fast and promising for real applications. [ABSTRACT FROM AUTHOR]

This paper presents original solutions from the field of intelligent expert systems for use in behavioral biometrics. They combine possibilities offered by biometric methods with the theory of fuzzy sets and the theory of population-based algorithms. Behavioral biometrics is concerned with learned behaviors such as a way of signing, movement, speaking, etc. However, these attributes trend to change over time. This is particularly important in a variety of fields including identity authentication. In this paper we present a new approach to the analysis of changes in behavioral characteristics. The purpose of the proposed approach is to predict values of features describing the so-called dynamic signature. It is the signature represented by waveforms describing a number of features including pen pressure and velocity. The proposed approach can be particularly useful in cases in which the interval between signature acquisition sessions is long. Our approach was tested with the use of the dynamic signature database called ATVS-SLT DB. It seems that the solutions proposed in this paper offer a new and interesting look at the solution of the problem of variability (aging) of biometric features. [ABSTRACT FROM AUTHOR]

Due to frequent and serious waterlog and environmental pollution in cities in recent years, urban stormwater management (USM) has become an essential issue of urban sustainable development in China. Low Impact Development (LID) technologies are effective and popular measures of USM, and can be used to reduce urban waterlog and the associated environmental pollution. In this paper, in order to identify an optimal strategy of LID technologies based on economic efficiencies and environmental performances, the construction costs and environmental benefits of the four LID technologies (including grass swales, bioretention cells, green roofs, and permeable pavement) were analyzed. Then, due to the uncertain feature of rainfall, concentration of pollutants and many social-economic factors, an inexact fuzzy chance-constrained programming (IFCCP) model was developed. Multiple uncertainties that can be expressed as interval parameters, fuzzy sets, and stochastic distribution can be addressed effectively and incorporated directly into the modeling process. Based on the analysis of the precipitation probability distribution and land use data, the developed model was applied to a university campus with relatively high building density and plot ratio in Beijing. Then, optimal solutions of multiple precipitation probabilities ( p m ) under varying USM goals were obtained. The four LID technologies were chosen to meet multiple USM goals based on multiple p m values. When   p m = 0.1 , 0.05 , 0.02 and   0.015 , the total construction investments would be 1.46 to 4.66, 1.46 to 5.25, 2.31 to 8.54 and 3.95 to 12.7 million yuan, respectively. The relationship between construction costs and p m represented the relationship between economic benefit and system risk. The results indicated that USM was influenced by the rainfall capacity, construction costs would increase with the increase of precipitation, and the risks of constraint violation would decrease as the construction costs increase. When considering the four LID technologies during the planning stage of USM, the preferred selection order would be bioretention cells, permeable pavements, grass swales and green roofs. Therefore, the solutions under each p m level could provide the references for desired USM plans. [ABSTRACT FROM AUTHOR]

A novel two-step knowledge-based exploratory framework is proposed in this paper for studying quality of Italian mobile telecommunication services (MTSs). Particularly, the Delphi technique is initially considered to finalize an overall quality structure of MTSs features, indicators and drivers, herein described on the basis of a comprehensive review of the fundamental references for the field, and also to select the key elements with reference to the Italian context. At the second step, selected key elements are prioritized via the analytic hierarchical process method according to viewpoints of the fundamental stakeholders for the sector. Furthermore, possible uncertainty and ambiguity of involved experts at this step of the study are addressed via a linguistic comparison scale represented by fuzzy numbers. Results of the first step revealed that the quality structure of Italian MTSs includes 15 key indicators with reference to four key MTSs quality features and seven key drivers. On the other hand, results of the second step pointed out that tangible aspects represents the fundamental key MTSs quality feature, whereas network population coverage, price of data services, and internet network performance represent the crucial key indicators. In addition, technological resources and technological innovation as well as informational resources constitute the most important key quality drivers of Italian MTSs. Obtained results may be of interest for MTSs managers and decision makers as well researchers of the field offering important suggestions as to how to evaluate and improve quality of MTSs. [ABSTRACT FROM AUTHOR]

The aim of this work is to introduce a new category of Fuzzy d-algebra called Γ-Fuzzy d-algebra. This notion is more useful than Fuzzy d-algebra because in the old case the degree of membership of any in the Fuzzy set does not depend on the structure of d-algebra X. Therefore, in this work, we introduced a new construction of Fuzzy d-algebra that is depended on structure of d-algebra. Also, we show that for any d-algebra there is a Γ-Fuzzy d-algebra. Further, this new construction helps us to find the interesting relation between Γ-Fuzzy d-algebra and edge d-algebra in this work. For any d-algebra, we determined unique Γ-Fuzzy topological space using Γ-Fuzzy d-algebra. For any , a new class of d-algebra is introduced, where is the set of all equivalence classes . Finally, a new class of intuitionistic fuzzy set is called intuitionistic Γ-Fuzzy d-algebra is investigated and discussed. [ABSTRACT FROM AUTHOR]

This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust. [ABSTRACT FROM AUTHOR]

Vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) method is one of the most popular multiple criteria decision making (MCDM) techniques in which, the optimal alternative is determined based on an agreement between maximum group utility and minimum individual regret. Despite VIKOR presents important advantages regarding other techniques for decision situations, it may result in a situation where an alternative with high measures for most criteria and low value for one criterion is not chosen as the optimal alternative. Thus, VIKOR may lead to unreasonable and inconsistent results and cannot present true situation of alternatives in indices' list. The goal of this paper is to present an improved extended version of interval type-2 fuzzy VIKOR (IT2FVIKOR) method for relieving the drawbacks of usual IT2FVIKOR. The proposed indices present more continuous and logical values than prior one such that better ranking results obtain for the MCDM problems. In order to demonstrate effectuality of the suggested method, a case study and an illustrative example are performed. Eventually, the extracted ranking upshots are analyzed with the others. [ABSTRACT FROM AUTHOR]

The allocation of human resources is considered as one of daily challenges of many organizations to achieve optimal efficiency. After investigating this challenge, in this research, a multi-agent system is used to make a cooperative game between organizational units for allocation of tasks and human resources in an organization. In the game, every unit provides a proposal for promoting his and other unit’s performance. Our model uses fuzzification to facilitate game process and increase probability of reaching an agreement in the game. Meanwhile it helps for simulation of real conditions in organizations. Results of the fuzzy game have shown that for special allocations of human resources and tasks, organizational performance is more desirable, and its productivity will be higher. We used an example to apply the model and show the results. The model can improve the human resources and task allocation in organizations by using a fuzzy game theory.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

With the emerging trend of green supply chain management, supplier selection and order allocation based on green criteria have become very important in this competitive world. During the selection process of the eligible suppliers, qualitative as well as quantitative factors should be considered. In this paper, a novel mathematical model is developed to select a set of suppliers, and assign the order quantity. Due to the importance of environmental concerns, both qualitative and quantitative environmental criteria are taken into account in this research. The proposed model comprises two phases namely a two-stage QFD, and a stochastic multi-objective mathematical model. The stochastic (scenario) approach helps to manage the uncertainty in the order allocation process. Furthermore, trapezoidal fuzzy numbers are utilized to handle the vagueness in human thoughts. The application of the proposed model is shown in beverages industry. [ABSTRACT FROM AUTHOR]

The article is devoted to the 66th Shakespeare sonnet. It deals with the relationships between original text and its translations into another verbal language and language of another arts. The article focuses on intensive dramatic processes in the work caused by changes in the density of sound events and directly linked to the establishment of composition. The results of study allow to say with certainty that it is advisable to apply quantitative methods to evaluation the adequacy of literary translation. [ABSTRACT FROM AUTHOR]

There are a lot of quantitative and qualitative factors about establishment and planning for facilities on cities. The effect of factors on facilities may be different and meantime, because of positive and negative autocorrelation efficacies of land uses, adjustment and non adjustment between city land uses, special factors for each of them apply in land use mosaics settings. This process is complicated for strong dependence between various city land use and impressments of beneficiaries and several factors in activity Land use city increase complexity of subject. On the other hand, collective nature of city activities and organic non deterministic extant in priorities and opinions of planners and necessity to use of various priorities and opinions of planners in this process have necessitated to use simultaneously collective make decision method and various tools for supporting to decision as Geographic Information System(GIS). Thus in present research has presented Fuzzy AHP Model and has evaluated city educational uses adjustment as for all effective factors in land use adjustments on basis of synthetic viewpoint Multi – Criteria Decision Models (AHP) and theory of Fuzzy. The results demonstrate that all high schools in Gorgan hold on a almost adjustment range (0.052-0.026) in view of adjustment to factors that studied in this research. The results also showed more prices and different levels of adjustments dedicate to Gorgan high schools uses and to efficiency of designed model for evaluation of city land use adjustment.

Multiple objective optimization of the initial conditions, maximal rotation speed and amplitude for a batch Saccharomyces cerevisiae cultivation using impulse and vibromixing systems is developed in this paper. The single objective function corresponds to the process productiveness and the residual glucose concentration. The multiple objective optimization problems are transformed to a single objective function with weight coefficients. A combined algorithm is applied for solving the single optimization. After this optimization the useful process productiveness increases and the residual glucose concentration at the end of the process decreases. The developed optimization and obtained results have shown that the impulse mixing systems have a better productiveness and better glucose assimilation. In addition, this system is easier for realization. The combined algorithm does not have a feedback and it does not guarantee robustness to process disturbances. For that purpose model predictive control for guarantee robustness to process disturbances is developed. The developed control algorithm - combined multiple objective optimization problem and model predictive control ensures maximal production at the end of the process and guarantees a feedback on disturbance as well as robustness to process disturbances.

A multiple objective optimization is applied for finding an optimum policy of fed-batch processes of whey fermentation and L-lysine production. The multiple objective optimization problems are transformed to a standard problem of optimization with single objective function by a general utility function with weight coefficients for each single utility coefficient criteria. A combined algorithm is applied when solving the maximizing decision problem. The algorithm includes a method for random search of finding an initial point and a method based on the fuzzy sets theory, combined in order to find the best solution of the optimization problem. The application of the combined algorithm eliminates the main disadvantage of the used fuzzy optimization method, namely it decreases the number of discrete values of the control variables. Thus, the algorithm allows problems with larger scale to be solved. After this multiple optimization, the useful product quality rises and the residual substrate concentration at the end of the process decreases. In this way, the process productivity is increased.