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In this article, the authors propose a new educational grading system based on fuzzy techniques, which includes five interrelated modules. Through an example to illustrate the process of the proposed fuzzy educational grading system, we find that the proposed system meets the basic requirements of the educational grading system and can overcome the limitations of existing fuzzy educational grading systems. By comparing the proposed fuzzy educational grading system with traditional methods, we find that the proposed evaluation system can more objectively and intelligently evaluate students' answer sheets. The aim of the proposed method is not to replace the existing fuzzy educational grading systems, but to enrich the present systems for students' learning achievement evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Stochastic, as well as fuzzy uncertainty, can be found in most real‐world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision‐makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best‐known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks. [ABSTRACT FROM AUTHOR]

This work investigates individual and social concerns regarding the integral sustainability of an alternative low-carbon cement. The framework of Integral Philosophy was used for the investigation. Social surveys were conducted in Australian, American, and Sri Lankan contexts. The data was collected through both closed-ended and open-ended questions in a questionnaire. Analysis of responses was carried out using fuzzy techniques and grounded theory techniques. Results indicate that individuals and societies in all three countries are more concerned about market price, quality standards, sustainability issues, and recommendations from professionals when selecting a new cement. In all three contexts, they also indicate that there is generally high support among individuals and societies for accepting low-carbon cement as an integrally sustainable cement. Combining the top-down Integral Theory approach with the bottom-up Grounded Theory is a valuable tool for social science research.

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness or other health consequences, property damage, social and economic crisis or environmental degradation. Many places of the world are at risk from one or more disasters. Although many studies have concentrated on single hazards, there is a need for integrated evaluations of multi-hazards for more effective land management. A selection of datasets and methods, such as meteorological data, satellite images, and GIS, were used to create the risk assessment maps. The parameters for multi-hazard assessment are mainly considered as rainfall, slope, elevation, and land use/land cover and create a map in a GIS environment. For a particular region, multi-hazards can be produced by integrating maps of several hazard assessments. The objective of this study is an integration of geospatial and fuzzy-logic techniques for multi-hazard mapping. Extensive parts of Gujarat state (India) experience a wide range of natural hazards: floods, soil erosion, drought, and earthquakes. This research creates and evaluates individual and group multi-hazard maps to visualize the spatial variation of hazards in Gujarat state, India. The calculated four individual hazard maps have been categorised into five classes: very-low, low, moderate, high, and very high. The multi hazard map has been classified into sixteen classes using the GIS unsupervised. This study aims to improve disaster preparedness, enhance land management, or guide decision-making for disaster risk reduction. This study can be helpful in the future to engineers, planners, and local governments in the field of spatial planning and natural disaster management.

Automotive industry particularly, commercial automotive industry, ranks as a key industry in the economic growth. The necessity of investigating the research & development(R & D) activities of digital marketing and innovation in the form of a dynamic system in automotive industry based on the 3 variables: empowerment of supply network, of product innovation, and of digital marketing is quite undisputed. The present research has been done with a view to identifying and evaluating the cause-and-effect interdependent relations governing the variables of R & D of digital marketing and innovation in commercial automotive industry. The research is typically applied, and has been done using the descriptive-survey method. The research community consisted of 50 experts; all with acceptable academic backgrounds and years of experience as executive managers and marketeers in the R & D of automotive industry. To analyze the data, the views of some elected experts on automotive industry, along with Delphi fuzzy and Dematel method were applied. Our findings showed that the variable “Intensity of R & D of digital marketing and innovation” has the most effect on the other variables. The variable “Empowerment of supply network” with the score of 3,25 has the largest amount of interaction with the other variables. Also, the variable “ Empowerment of R & D in digital marketing and innovation” with the score of 1,08 has the smallest amount of interaction with the other variables.

The relevance of research on competitiveness at the meso level is related to the contemporary views of a region as an essential element of the economic space. The development of forecasting and analytical methods at the regional level of the economy is a key task in the process of strategic decision making. This article proposes a method of quantitative assessment of the risks of regional competitiveness. The novelty of this approach is based on both a fixed-point risk assessment and scenario-based predictive analysis. A hierarchical structure of indicators of competitiveness of regions is offered. A method based on the Kolmogorov–Chapman equations was used for the predictive estimation of risks of regional competitiveness. The integrated risk assessment is performed using the modified fuzzy ELECTRE II method. A web application has been implemented to assess the risks of competitiveness of Russian regions. The functionality of this application provides the use of multi-criteria decision-making methods based on a fuzzy logic approach to estimate risks at a specified time, calculating the probability of risk events and their combinations in the following periods and visualizing the results. Approbation of the technique was carried out for 78 Russian regions for various scenarios. The analysis of the results obtained provides an opportunity to identify the riskiest factors of regional competitiveness and to distinguish regions with different risk levels. [ABSTRACT FROM AUTHOR]

The relevance of research on competitiveness at the meso level is related to the contemporary views of a region as an essential element of the economic space. The development of forecasting and analytical methods at the regional level of the economy is a key task in the process of strategic decision making. This article proposes a method of quantitative assessment of the risks of regional competitiveness. The novelty of this approach is based on both a fixed-point risk assessment and scenario-based predictive analysis. A hierarchical structure of indicators of competitiveness of regions is offered. A method based on the Kolmogorov–Chapman equations was used for the predictive estimation of risks of regional competitiveness. The integrated risk assessment is performed using the modified fuzzy ELECTRE II method. A web application has been implemented to assess the risks of competitiveness of Russian regions. The functionality of this application provides the use of multi-criteria decision-making methods based on a fuzzy logic approach to estimate risks at a specified time, calculating the probability of risk events and their combinations in the following periods and visualizing the results. Approbation of the technique was carried out for 78 Russian regions for various scenarios. The analysis of the results obtained provides an opportunity to identify the riskiest factors of regional competitiveness and to distinguish regions with different risk levels.

It is known that, to more adequately describe expert knowledge, it is necessary to go from the traditional (type-1) fuzzy techniques to higher-order ones: type-2, probably type-3 and even higher. Until recently, only type-1 and type-2 fuzzy sets were used in practical applications. However, lately, it turned out that type-3 fuzzy sets are also useful in some applications. Because of this practical importance, it is necessary to design efficient algorithms for data processing under such type-3 (and higher-order) fuzzy uncertainty. In this paper, we show how we can combine known efficient algorithms for processing type-1 and type-2 uncertainty to come up with a new algorithm for the type-3 case. [ABSTRACT FROM AUTHOR]

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper, we analyze the permutation flow shop problem (PFSP) with both stochastic and fuzzy processing times. The main goal is to find the solution (permutation of jobs) that minimizes the expected makespan. However, due to the existence of uncertainty, other characteristics of the solution are also taken into account. In particular, we illustrate how survival analysis can be employed to enrich the probabilistic information given to decision-makers. To solve the aforementioned optimization problem, we extend the concept of a simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated in the PFSP. In order to test our approach, classical PFSP instances have been adapted and extended, so that processing times become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. [ABSTRACT FROM AUTHOR]

In this paper, we have developed the multi-item inventory model in the fuzzy environment. Here we considered the demand rate is constant and production cost is dependent on the demand rate. Set-up- cost is dependent on average inventory level as well as demand. Lead time crashing cost is considered the continuous function of leading time. Limitation is considered on storage of space. Due to uncertainty all cost parameters of the proposed model are taken as generalized trapezoidal fuzzy numbers. Therefore this model is very real. The formulated multi objective inventory problem has been solved by various techniques like as Geometric Programming (GP) approach, Fuzzy Programming Technique with Hyperbolic Membership Function (FPTHMF), Fuzzy Nonlinear Programming (FNLP) technique and Fuzzy Additive Goal Programming (FAGP) technique. An example is given to illustrate the model. Sensitivity analysis and graphical representation have been shown to test the parameters of the model.

This paper selects the daily data of the exchange rates of Chinese Yuan (CNY) over the currencies of 14 countries along the Belt and Road, Shanghai composite index and Shenzhen composite index to study the influence of the Belt and Road Initiative on the linkages between exchange rates and Chinese stock index based on the flow-oriented model and the stock-oriented model. To reflect the fluctuations in daily data and reduce the central bank's interference with the exchange rate, two fuzzy techniques are used to process data, that is, the centroid based measure and the integral based measure. Then we judge the relationship between exchange rate and stock index through the Pearson correlation coefficient and the Granger causality test. Besides, we further compare the results and their differences by the classic crisp method and our two fuzzy techniques, which enable us to judge their correlation more accurately, and provide a reference for a wider application of the proposed fuzzy methods. We find that there is a correlation between exchange rate and stock index under certain conditions, and the Belt and Road initiative strengthens the relationship between the Chinese foreign exchange market and the stock market, more importantly, the fuzzy techniques are effective to judge this relation.

In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that allows us to deal with complex optimization problems with both stochastic and fuzzy uncertainty. This hybrid approach combines simulation, metaheuristics, and fuzzy logic to generate near-optimal solutions to large scale NP-hard problems that typically arise in many transportation activities, including the vehicle routing problem, the arc routing problem, or the team orienteering problem. The methodology allows us to model different components–such as travel times, service times, or customers' demands–as deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which can also be extended to other optimization problems in areas such as manufacturing and production, smart cities, telecommunication networks, etc. [ABSTRACT FROM AUTHOR]

It is known that, to more adequately describe expert knowledge, it is necessary to go from the traditional (type-1) fuzzy techniques to higher-order ones: type-2, probably type-3 and even higher. Until recently, only type-1 and type-2 fuzzy sets were used in practical applications. However, lately, it turned out that type-3 fuzzy sets are also useful in some applications. Because of this practical importance, it is necessary to design efficient algorithms for data processing under such type-3 (and higher-order) fuzzy uncertainty. In this paper, we show how we can combine known efficient algorithms for processing type-1 and type-2 uncertainty to come up with a new algorithm for the type-3 case.

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper, we analyze the permutation flow shop problem (PFSP) with both stochastic and fuzzy processing times. The main goal is to find the solution (permutation of jobs) that minimizes the expected makespan. However, due to the existence of uncertainty, other characteristics of the solution are also taken into account. In particular, we illustrate how survival analysis can be employed to enrich the probabilistic information given to decision-makers. To solve the aforementioned optimization problem, we extend the concept of a simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated in the PFSP. In order to test our approach, classical PFSP instances have been adapted and extended, so that processing times become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones.

This paper suggests an original model for control on teams. The originality of the model is associated with a combination of management concepts and mathematical theories of fuzzy logic and fuzzy sets. In addition to the team control model, the author proposes a toolkit for measurement and evaluation of team performance. The toolkit incorporates fuzzy techniques and tools for quantification of evaluations, decision-making, study of joint influences and hidden effects. The results from testing the team control model at a Bulgarian University are also presented and discussed in this paper.

In medical and other applications, expert often use rules with several conditions, each of which involve a quantity within the domain of expertise of a different expert. In such situations, to estimate the degree of confidence that all these conditions are satisfied, we need to combine opinions of several experts – i.e., in fuzzy techniques, combine membership functions corresponding to different experts. In each area of expertise, different experts may have somewhat different membership functions describing the same natural-language ("fuzzy") term like small. It is desirable to present the user with all possible conclusions corresponding to all these membership functions. In general, even if, for each area of expertise, we have only a 1-parametric family characterizing different membership function, then for rules with 3 conditions, we already have a difficult-to-interpret 3-parametric family of possible consequences. It is thus desirable to limit ourselves to the cases when the resulting family is still manageable – e.g., is 1-parametric. In this paper, we provide a full description of all such families. Interestingly, it turns out that such families are possible only if we allow non-normalized membership functions, i.e., functions for which the maximum may be smaller than 1. We argue that this is a way to go, since normalization loses some information that we receive from the experts. [ABSTRACT FROM AUTHOR]

Garment purchasing through the Internet has become an important trend for consumers. However, in various garment e-shopping systems, it systematically lacks personalized recommendations, like sales advisors in classical shops, in order to propose the most relevant products to different consumers according to their body shapes and fashion requirements. In this paper, we propose a consumer-oriented recommendation system by fuzzy techniques and AHP, which can be used inside a garment online shopping system like a virtual sales advisor. This system has been developed by integrating the professional knowledge of designers and shoppers and taking into account consumers' perception on products. It can effectively help consumers to choose garments from the Internet. Compared with other prediction methods, the proposed method is more robust and interpretable owing to its capacity of treating uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, we have developed an integrated production inventory model for a two echelon supply chain consisting of one manufacturer and one retailer. Purchase cost for the manufacturer is dependent on inventory lot size; production cost of the manufacturer is dependent on production rate. Also purchase cost of the retailer is dependent on demand rate of the customer. The proposed multi objective supply chain inventory model has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership function (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is provided to illustrate the proposed model. Finally sensitivity analysis and graphical representation have been shown. [ABSTRACT FROM AUTHOR]

In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that allows us to deal with complex optimization problems with both stochastic and fuzzy uncertainty. This hybrid approach combines simulation, metaheuristics, and fuzzy logic to generate near-optimal solutions to large scale NP-hard problems that typically arise in many transportation activities, including the vehicle routing problem, the arc routing problem, or the team orienteering problem. The methodology allows us to model different components–such as travel times, service times, or customers’ demands–as deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which can also be extended to other optimization problems in areas such as manufacturing and production, smart cities, telecommunication networks, etc.

Abstract Developing net-zero energy communities powered by renewable energy (RE) resources has become a popular concept. To make the best choices for community-level net-zero energy systems, it is necessary to identify the best energy technologies at local level. Evaluation of RE technologies has to be extended from technical and economic aspects to include environmental and social wellbeing. It is possible to identify the true costs and benefits of energy use by taking a cradle-to-grave life cycle perspective. In this study, a RE screening and multi-stage energy selection framework was developed. A fuzzy multi-criteria decision making approach was used in ranking the technologies to incorporate the conflicting requirements, stakeholder priorities, and uncertainties. Different scenarios were investigated to reflect different decision maker priorities. Under a pro-environment scenario, small hydro, onshore wind, and biomass combustion technologies perform best. Under a pro-economic decision scenario, biomass combustion, small hydro, and landfill gas have the best rankings. Triple bottom line sustainability was combined with technical feasibility through a ruled-based approach to avoid the theoretical pitfalls inherent in energy-related decision making. This assessment is geared towards providing decision makers with flexible tools, and is expected to aid in the pre-project planning stage of RE projects. Highlights • Energy planning needs to be done at community level for optimal outcomes. • Considering life cycle impacts and uncertainty is important in energy planning. • Different decision scenarios were considered in selecting technologies. • Small hydro and biomass perform particularly well for British Columbia. • The developed method provides decision makers with flexible planning tools. [ABSTRACT FROM AUTHOR]

Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements.

Copyright of Serbian Journal of Management is the property of Serbian Journal of Management 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.)

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie 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.)

Currently, the management of water networks is key to increase their sustainability. This fact implies that water managers have to develop tools that ease the decision-making process in order to improve the efficiency of irrigation networks, as well as their exploitation costs. The present research proposes a mathematical programming model to optimize the selection of the water sources and the volume over time in water networks, minimizing the operation costs as a function of the water demand and the reservoir capacity. The model, which is based on fuzzy methods, improves the evaluation performed by water managers when they have to decide about the acquisition of the water resources under uncertain costs. Different fuzzy solution approaches have been applied and assessed in terms of model complexity and computational efficiency, showing the solution accomplished for each one. A comparison between different methods was applied in a real water network, reaching a 20% total cost reduction for the best solution.

The power/energy losses reduction in distribution systems is an important issue during planning and operation, with important technical and economical implications. Thus, the energy losses minimization implies not only the technical improvement of the network, through its renewal with the introduction of the technological innovations in the equipment and circuit components as well as the optimal planning of the design and development of the network, but also requires the use of the methods and software tools to facilitate the operation process. The paper presents a strategy for power/energy saving which replacement of the 6 kV voltage level with 20 kV voltage level in correlation with the extent of using efficient transformers. In this line, different urban distribution networks were analyzed using fuzzy techniques.

In some practical situations - e.g., when treating a new illness - we do not have enough data to make valid statistical conclusions. In such situations, it is necessary to use expert knowledge - and thus, it is beneficial to use fuzzy techniques that were specifically designed to process such knowledge. At first glance, it may seem that in situations when we have large amounts of data, the relative importance of expert knowledge should decrease. However, somewhat surprisingly, it turns out that expert knowledge is still very useful in the current age of big data. In this paper, we explain how exactly (and why) expert knowledge is useful, and we overview efficient methods for processing this knowledge. This overview is illustrated by examples from environmental science, geosciences, engineering (in particular, aircraft maintenance and underwater robots), and medicine. [ABSTRACT FROM AUTHOR]

One of the most empirically successful tools for studying dependence between different quantities in econometrics is the tool of vine copulas. In this paper, we explain this empirical success by showing that the most widely used vine copulas are, in effect, the results of using the general fuzzy methodology. To be more precise, vine copulas correspond to a natural extension of the traditional fuzzy methodology, when we allow several different 'and'-operations (t-norms), and some of these t-norms can be nonassociative. [ABSTRACT FROM AUTHOR]

While many objects and processes in the real world are discrete, from the computational viewpoint, discrete objects and processes are much more difficult to handle than continuous ones. As a result, a continuous approximation is often a useful way to describe discrete objects and processes. We show that the need for such an approximation explains many features of fuzzy techniques, and we speculate on to which promising future directions of fuzzy research this need can lead us. [ABSTRACT FROM AUTHOR]

In the paper, the ordinal optimization method for Marginal Clearing Price (MCP) forecasting is proposed. The basic idea is to use a competition based simulation model, in order to analyze the influence of the bidding strategies on MCP, and to establish the optimal bidding strategies of generators in uncertainty conditions of the nodal loads. For nodal loads, the fuzzy models were used. As a result, this proposed approach has the advantages of a good accuracy of MCP estimation in a deregulated market. [ABSTRACT FROM AUTHOR]