Fuzzy Estimates Research Articles

Managing the humanitarian supply chain: a fuzzy logic approach

PurposeHumanitarian supply chain management (HSCM) in today’s environment faces the challenges such as information availability, inventory management, collaboration, logistics related issues and preparedness. The purpose of this paper is to evaluate the HSCM performance, considering the consequences in terms of operation, recovery and responsiveness based on the fuzzy estimates of the components presented.Design/methodology/approachIn the study, triangulation approach was adapted for collecting data and developing a hierarchical structure for humanitarian supply chain performance assessment. The relationships between HSCM performance and its suddenness and required preparedness are depicted by cause and effect diagrams. The concepts of fuzzy association and fuzzy composition are applied to identify relationships.FindingsIn the hierarchy presented, the performance in a disaster situation, preparedness and suddenness of the situation and factors that influence the above are modeled. The taxonomy is developed for describing the relationship between factors, their likelihoods and impacts to achieve consistent quantification.Research limitations/implicationsThe study considers case studies from Indian conditions; however, conditions in other countries and their practices for the disaster management may vary to certain extent.Practical implicationsA methodology presented for evaluating the exposures in considering the consequences in terms of responsiveness, operations, recovery, mitigation and emergency response. The study may help the humanitarian relief practitioners to understand the insights of the disaster situations using the proposed framework.Originality/valueA common language for describing the different factors of HSCM is presented, which includes terms for quantifying likelihoods and impacts. The concept of fuzzy association and fuzzy composition has been applied to identify relationships between sources and consequences on HSCM performance. The use of descriptive linguistic variables is ensured through the implementation of fuzzy logic.

РАЗРАБОТКА МЕТОДА ОЦЕНКИ СОСТОЯНИЯ БАЗОВЫХ СТАНЦИЙ ДЛЯ ОПРЕДЕЛЕНИЯ НЕОБХОДИМОСТИ ИХ МОДЕРНИЗАЦИИ

The article consideres the problems of determining the need to modernize the base stations of the cellular network based on the mathematical apparatus of the theory of fuzzy sets. To improve the quality of telecommunications services the operators should send significant funding for upgrading the equipment of base stations. Modernization can improve and extend the functions of base stations to provide cellular communication, increase the reliability of the base station in operation and the functionality of its individual elements, and reduce the cost of maintenance and repair when working on a cellular network. The complexity in collecting information about the equipment condition is determined by a large number of factors that affect its operation, as well as the imperfection of obtaining and processing the information received. For a comprehensive assessment of the need for modernization, it is necessary to take into account a number of indicators. In the structure of indicators of the need for modernization, there were introduced the parameters reflecting both the degree of aging and obsolescence(the technical gap and the backlog in connection with the emergence of new technologies and standards). In the process of a problem solving, the basic stages of decision-making on modernization have been allocated. Decision-making on the need for modernization is based not only on measuring information that takes into account the decision-makers, but also on linguistic and verbal information. Therefore, to determine the need for upgrading the base stations, the theory of fuzzy sets is used, with the help of which experts can be attracted to this issue. They will be able to formulate additional fuzzy judgments that help to take into account not only measuring characteristics, but also poorly formalized fuzzy information. To do this, the main indicators of the modernization need have been defined, and fuzzy estimates of the need for modernization for all indicators and a set of indicators reflecting the need for upgrading the base stations have been formulated.

Hybrid Fuzzy Estimation of System Reliability for Multi-State System

This article is purpose a hybrid estimation of the fuzzy system reliability for the Non-repairable multi-state series-parallel system (NMSS). Considering the fuzzy parameter of NMSS are prior fuzzy parameters. Then the posterior fuzzy parameters of NMSS are constructed by fuzzy Bayesian point estimate of fuzzy system reliability. Moreover, an approach to construct interval estimation of the fuzzy system reliability of NMSS will be used in estimation of the prior fuzzy confidence interval and posterior fuzzy confidence interval of fuzzy system reliability. Finally, the coverage probability and the expected length that it is used to interpret the efficiency of both fuzzy confidence intervals are presented.

Formulation and Anticipated Approach for Developing a New Business through Integrated Strategic Morphological Analysis and Integrated Fuzzy Approach and Estimate the Cost of the Integration of PSO and BP Neural Network in the Plastic Injection Molding Industry

In this article we have tried to identify the factors by which industry experts predict medium-term future, then the relationship between the environmental factors determined by comparing the morphology characterized couple, this relationship is obtained through interviews with experts economy. The experts in economic conditions and environmental factors determine the medium-term future. Finally, according to industry experts on space environmental conditions, inter-organizational scenarios to determine the ranking. Using fuzzy decision-making through the evaluation and ranking of organizational standpoint, the most appropriate one is selected that has the features, dimensions and unique circumstances applicable to the environment. Due to the globalization of business in recent years, managers and business owners are looking to cut costs and accurate and realistic estimates of cost, due to its ability to make the right decision about the products and the future of their business. At the end of a cost estimate for superior business model that obtained by ranking methods propagation is back propaganda neural network Particle swarm optimization. It is also complex and covers defects traditional methods. Hybrid algorithm can not only take advantage of the ability to search for a strong global particle swarm optimization, but also be robust search capability regional propagation neural network as well. The corresponding operation in MATLAB software environment (MATLAB) is implemented. Finally, model related to the choice of business, business model and cost estimates provided kitchenware and printing and packaging businesses are adaptable to future requirements and trends toward this part of the industry for the benefit of the organization.

INTEGRATIVE ESTIMATE OF AIR TRAFFIC CONTROL STUDENTS ATTITUDE TO THE DANGER OF LIGHT LEVEL NORMS VIOLATION

Taking into account influence of human factor on flight safety; multiplicative approach to determination of integral estimate of air traffic controllers attitude to aircrafts separation norms violation; was developed firstly in area of air traffic controllers professional activity researches. Harrington desirability function adapted during researches allows to generalize indexes of main decision taking dominants; desirability levels; and fuzzy estimates for flight separation norms violation risks according to ICAO scale. Inexpedience of using desirability coefficients to determine air traffic controllers attitude to single flight separation norm is proved. Corresponding index varies in j (S)=0;69–0;74 limits; and greater violations are allowed for bigger norms by air traffic control students. With help of priority rearrangement method weight coefficients of these norms were defined that allowed to find integral index of attitude to all flight level norms violation which is equal to j ( S )=0;94.

Fuzzy-Interval Choice of Alternatives in Collective Expert Evaluation

Abstract. The authors consider the problem of determining the values of collective expert evaluation of alternative decisions on the basis of models whose structure is described by fragments of the Kolmogorov---Gabor polynomial. The approach is proposed that allows us to formalize the uncertainly of the definition of model parameters of multifactor evaluation based on fuzzy intervals; to define collective fuzzy estimates of alternatives and use them to range the alternatives in accordance with the decomposition of fuzzy intervals at the ?-levels.

Fuzzy fault-tolerant-predictive control for a class of nonlinear uncertain systems

ABSTRACTIn this paper, a fault-tolerant fuzzy model-predictive control with the integral action method for a class of nonlinear uncertain systems is proposed. Nonlinear uncertain systems subject to actuators and/or sensors faults are represented by the Takagi–Sugeno (T-S) fuzzy model. The objective is to design a stable, robust and efficient fault-tolerant controller based on a T-S fuzzy observer with measurable premise variables. The proposed T-S fuzzy observer estimates state vector and faults. Based on Lyapunov theory, the trajectory tracking performances and the closed-loop system stability are analysed. The gains of the fuzzy observer and the pre-stabilized control law are obtained by solving linear matrix inequalities. Simulation results illustrate the robustness of the proposed controller with respect to uncertainties on an academic mathematical system.

Operating with Fuzzy Probability Estimates in Decision Making Processes with Risk

This paper considers different techniques of operating with fuzzy probability estimates of relevant random events in decision making tasks. The recalculation of posterior probabilities of states of nature based on the information provided by indicator events is performed using a fuzzy version of Bayes’ theorem. The choice of an optimal decision is made on the basis of fuzzy expected value maximisation.

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series–parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.

Normal Estimation for Mass Point Clouds of Irregular Model in the 3D Reconstruction based on Fuzzy Inference

This paper presents a fuzzy normal estimate for mass point clouds of irregular models in reconstruction. The irregular model is complex object that some part is smooth and some parts are irregular including sharp features. Therefore, we put kNN and curvature of mass point clouds to fuzzy inference system to divide the kind of point clouds and the output of FIS can determine which part of tooth point clouds belong to. For different kinds point clouds, corresponding algorithm is given. Point clouds in the smooth area are estimated normal by PCA directly and ones in other regions of thin or sharp area are estimated by checker and attach points. This method is simpler than those complex methods used on the whole point clouds directly. The experiment results show that much time is saved and surface reconstruction is very fine than PCA and WLOP.

A method for aggregation of trapezoidal fuzzy estimates under group decision-making

This paper introduces a simple method for group decision-making, where experts' opinions are expressed by trapezoidal fuzzy numbers. The method is based on the metric approach providing an easy procedure for achieving a one-valued consensus. A new approach for determining experts' degrees of importance is proposed. A theoretical basis is established, showing that the method is correct under the whole class of metrics on the set of trapezoidal fuzzy numbers. A specific fuzzy aggregation operator is constructed for the realization of the proposed approach. It is shown that despite its simplicity the proposed method preserves such important properties as Agreement Preservation, Order Independence, Linearity property etc. A comparative appraisal of the results obtained by the proposed approach and some other methods is given as well.

Market demand estimation for new product development by using fuzzy modeling and discrete choice analysis

Market demand estimation is an important process to assess the financial feasibility of new product development (NPD) projects. The development of models for market demand estimation involves market potential estimation and choice modeling. Previous studies commonly used conjoint analysis to develop utility functions which were then used in discrete choice models to generate market share models. The jury of executive opinion method is commonly used in industries wherein a number of experts and/or consultants are always involved in the market potential estimation. However, a high degree of fuzziness always exists in the data obtained from conjoint surveys and the market potential estimation because of the subjective judgments of respondents and experts. However, ignorance of the fuzziness would lead to the over-estimation of market demands. This research aims to tackle the fuzziness associated with market potential estimation and survey data in the development of market demand models. In this paper, a new methodology of developing fuzzy market demand models for NPD is proposed to address the fuzziness by which market demands can be estimated for the worst, normal, and best scenarios. The proposed methodology involves fuzzy choice modeling based on fuzzy regression and discrete choice analysis, and fuzzy estimate generation of market potential. To evaluate the effectiveness of the proposed methodology, a case study of market demand estimation of a new tablet PC is conducted based on the proposed methodology. The results of the implementation are compared with those based on a popular multinominal logit (MNL) based demand model. From the comparison, it can be noted that the estimated market demand based on the MNL model is very close to that for the normal scenario based on the proposed fuzzy demand model. However, the MNL model cannot provide estimates for other scenarios.

Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry

In this research, a framework combining lean manufacturing principles and fuzzy bow-tie analyses is used to assess process risks in chemical industry. Lean manufacturing tools and techniques are widely used for eliminating wastes in manufacturing environments. The five principles of lean (identify value, map the value stream, create flow, establish pull, and seek perfection) are utilized in the risk assessment process. Lean tools such as Fishbone Diagram, and Failure Mode and Effect Analysis (FMEA) are used for risk analysis and mitigation. Lean principles and tools are combined with bow-tie analysis for effective risk assessment process. The uncertainty inherent with the risks is handled using fuzzy logic principles. A case study from a chemical process industry is provided. Main risks and risk factors are identified and analyzed by the risk management team. Fuzzy estimates are obtained for the risk factors and bow-tie analysis is used to calculate the aggregated risk probability and impact. The risks are prioritized using risk priority matrix and mitigation strategies are selected based on FMEA. Results showed that the proposed framework can effectively improve the risk management process in the chemical industry.

Construction of nonreciprocal fuzzy preference relations with the use of preference functions

In order to model the preferences of a decision-maker (DM) by means of fuzzy preferencerelations, a DM can utilize different preference formats (such as ordering of the alternatives, utility values, multiplicative preference relations, fuzzy estimates, and reciprocal as well as nonreciprocal fuzzy preference relations) to express his/her judgments. Afterward, the obtained information is utilized to constructfuzzy preference relations. Here we introduce a procedure that allows the use of so-called preference functions (which is a preference format utilized in the methods of PROMETHEE family) to construct nonreciprocal fuzzy preference relations. With diverse preference formats being offered, a DM can select the onethat is the most convenient to articulate his/her preferences. In order to demonstrate the applicability of theproposed procedure a multicriteria decision-making problem related to the site selection for constructing anew hospital is considered here.

Fuzzy-TLX: using fuzzy integrals for evaluating human mental workload with NASA-Task Load indeX in laboratory and field studies

The aim of this study was to assess mental workload in which various load sources must be integrated to derive reliable workload estimates. We report a new algorithm for computing weights from qualitative fuzzy integrals and apply it to the National Aeronautics and Space Administration -Task Load indeX (NASA-TLX) subscales in order to replace the standard pair-wise weighting technique (PWT). In this paper, two empirical studies were reported: (1) In a laboratory experiment, age- and task-related variables were investigated in 53 male volunteers and (2) In a field study, task- and job-related variables were studied on aircrews during 48 commercial flights. The results found in this study were as follows: (i) in the experimental setting, fuzzy estimates were highly correlated with classical (using PWT) estimates; (ii) in real work conditions, replacing PWT by automated fuzzy treatments simplified the NASA-TLX completion; (iii) the algorithm for computing fuzzy estimates provides a new classification procedure sensitive to various variables of work environments and (iv) subjective and objective measures can be used for the fuzzy aggregation of NASA-TLX subscales. Practitioner Summary: NASA-TLX, a classical tool for mental workload assessment, is based on a weighted sum of ratings from six subscales. A new algorithm, which impacts on input data collection and computes weights and indexes from qualitative fuzzy integrals, is evaluated through laboratory and field studies. Pros and cons are discussed.

On the significance testing of fuzzy regression applied to the CAPM: Canadian commodity futures evidence

This paper is written with two congruent objectives. The first is to develop a framework for individual tests of significance of a fuzzy regression model by employing a simple probabilistic estimation procedure. The proposed test, based on two-phase fuzzy regression estimates, is simple and robust. The capital asset pricing model (CAPM), with induction of price limits, serves as the essential component of our analysis, due to its ability to illuminate and determine the risk premiums in a commodity futures market. The second objective is to estimate and test for the significance of the systematic risk of Canadian commodity futures and to illustrate the benefits of the significancetesting approach.

CLOUD DELPHI METHOD

Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM.

Evaluating lifetime performance for the Pareto model with censored and imprecise information

A lifetime capability index L tp has been proposed to measure the business lifetime performance, wherein output lifetime measurements are assumed to be precise from the Pareto model with censored information. In the present study, we study a more realistic situation where the lifetime output data are imprecise. The approach developed by Buckley [Fuzzy system, Soft Comput. 9 (2005), pp. 757–760; Fuzzy statistics: Regression and prediction, Soft Comput. 9 (2005), pp. 769–775] incorporated with some extensions (a set of confidence intervals, one on top of the other), is used to construct the triangular-shaped fuzzy number for the fuzzy estimate of the L tp. With the sampling distribution of the unbiased estimator of the L tp, two useful fuzzy inference criteria, its critical value and fuzzy p-value are obtained to assess the lifetime performance. The presented methodology can handle the lifetime performance assessment on the condition that sample lifetime data are involved with imprecise information, classifying the lifetime performance with the three-decision rule. With different preset requirements and a certain degree of imprecise data, we also develop a four quadrants decision-making plot where managers can easily simultaneously visualize several important features of lifetime performance for making a decision. An example of business lifetime data is given to illustrate the applicability of the proposed method.

Manufacturing process performance evaluation for fuzzy data based on loss-based capability index

Process capability indices have been introduced for measuring process reproduction capability of a manufacturing industry. The loss-based capability index $$C_{pm}$$ takes into account the degree of process targeting (centering), which essentially measures the process performance on the basis of average process loss. Generally, the underlying manufacturing process data are obtained from the output responses of continuous quantities, which are always assumed to be real numbers. However, in a practical situation, the data collected by output process measurements are often imprecise (fuzzy data). We propose a constructive methodology for obtaining the fuzzy estimate of $$C_{pm}$$ using fuzzy data, which is based on “resolution identity” in fuzzy sets theory. We propose four decision rules to judge the process condition by simultaneously introducing randomness and fuzziness. Finally, we present a sequence of testing steps for assessing manufacturing process performance using the critical value of $$C_{pm}$$ with fuzzy data and can be easily implemented in real situations.

基于投影寻踪方法的模糊综合估计与聚类的 工程项目风险评估<br>The Project Risk Estimate Model Based on the Projection Pursuit and Fuzzy Clustering

本文基于投影寻踪的方法以模糊综合估计与聚类方法为核心，研究了工程项目风险量化评估问题。首先使用投影寻踪方法求出各个风险指标项的权重；接下来，检验各个指标项评分的偏离程度，对其中偏离度高的使用区间评分法；然后，检验各专家的评分，剔除其中偏离程度较大的专家评分项。最后通过IT项目实例计算该项目风险评分。 The objective of this paper is to construct a model which mainly based on projection pursuit model and use fuzzy estimates and clustering to raise the precision of the risk evaluation. Firstly, use projection pursuit model to compute the weight of each one of the risk factors, then check the deviation of each factor’s score and use the interval point-rating method to reevaluate the score of these ones; secondly check the given scores of every expert and eliminate the deviated ones; finally, the risk of IT project is presented at the end for instance in steps, following that using projection pursuit model to verify the weight matrix to testify the feasibility of the model.