An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.     

Visitor management and ecological integrity: one example of an integrated management approach using Decision Analysis

In this paper we argue in favour of using a decision analysis framework for more integrated decision-making when managing protected areas. Such an approach will enable agencies to balance between the frequently conflicting goals of visitor management and ecological integrity. We present a case study from the West Coast Trail in Pacific Rim National Park Reserve, BC, Canada, in which we use a hybrid of ELECTRE and AHP to establish a ranking of several management options. We conclude by suggesting that such a more formal framework constitutes a more objective decision support tool, assists in framing relevant management questions and tradeoffs, and at the same time provides guidance for data collection.     

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.     

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.     

Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.     

Risk-Based Evaluation of Turkish Agricultural Strategies using Fuzzy AHP and Fuzzy ANP

In this article, we propose a model for selection of an agricultural strategy for Turkey by using fuzzy analytical hierarchy process (AHP) and analytical network process (ANP) based on linguistic terms. Fuzzy AHP-based methodology will be discussed to tackle the different decision criteria such as risk factors, ecological structure, socioeconomic structure, and technological structure involved in the selection of an agricultural strategy for Turkey and ANP will represent an effective tool for providing a suitable solution for managers and government administrators of this case. The linguistic levels of comparisons produced by the experts for each comparison are tapped in the form of triangular fuzzy numbers in order to construct fuzzy pairwise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy that contains 4 criteria and 15 sub-criteria.     

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

Continuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise?

Continuous integrated bioprocessing has elicited considerable interest from the biopharma industry for the many purported benefits it promises. Today many major biopharma manufacturers around the world are engaged in the development of continuous process platforms for their products. In spite of great potential, the path toward continuous integrated bioprocessing remains unclear for the biologics industry due to legacy infrastructure, process integration challenges, vague regulatory guidelines, and a diverging focus toward novel therapies. In this article, we present a review and perspective on this topic. We explore the status of the implementation of continuous integrated bioprocessing among biopharmaceutical manufacturers. We also present some of the key hurdles that manufacturers are likely to face during this implementation. Finally, we hypothesize that the real impact of continuous manufacturing is likely to come when the cost of manufacturing is a substantial portion of the cost of product development, such as in the case of biosimilar manufacturing and emerging economies.     

Information pyramids for informed biodiversity conservation

We discuss a paradigm for informed ecosystem management that provides a quantitative and rigorous foundation for informing conservation decisions and sustainable ecosystem management. Information pyramids incorporate conceptual and technological advances in ecosystem depiction and provide a framework for the integration and generalization of raw data into forms that are spatially extensive and at the appropriate level of generalization for a particular use. The basic tenets of the pyramid are: (1) Higher levels of the pyramid are entirely derived from a foundation of underlying data. (2) The process of generalization and integration upward should be objective and explicit. (3) Pyramids for different purposes often overlap, with common data and common methods for integration. (4) All levels of the pyramid should be developed together, including base data, methods and kinds of integration, and algorithms for using the information for planning and decision-making. Information pyramids are a powerful approach to organizing research science, and provide a mechanism by which research, data collection, storage and generalization can be focused on conservation outcomes. Common data and methods lead to increased efficiency, while also allowing for separate disciplines and programs. A case study of an integrated pyramid from New Zealand is discussed, which illustrates the characteristics of information pyramids. Components of this pyramid are discussed that provide examples of integration and generalization at various levels of the pyramid, from base data, to derived data, to spatial predictions and classifications, to a method of integrating this information into conservation decisions.     

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.     

深圳城市生态大数据智慧管理和服务平台技术集成与应用研究

生态环境是人类赖以生存和发展的基础,城市生态大数据智慧管理和服务平台建设是生态城市和美丽城市建设的需要。以深圳市为例,借助物联网、移动互联网、计算机、数据库和网络地理信息系统技术,以及时空地理大数据整合和共享、大数据挖掘分析和云端一体化业务协同等关键技术,结合城市生态系统评估分析决策模型/方法/对策库,在实现深圳市“空-地-网-统计-众源”等多源异构生态大数据有效集成的基础上,搭建了 “数据采集-信息提取-知识发现-决策生成-快速服务”全流程、一体化的深圳市生态大数据智慧管理和服务平台,构建了面向业务部门和科研人员等专业用户的生态野外数据调查采集系统和城市生态监测与评估管理决策分析系统,以及面向社会大众的深圳生态大调查APP。平台首次揭示了深圳市1979年以来不同生态系统的格局、构成、过程、服务和健康状况的变化,提升了深圳市生态环境综合决策科学化、生态环境监管精准化、生态环境公共服务便民化水平。平台有效降低了用户数据收集与处理和操作专业模型的难度,突破了原始数据应用的瓶颈和难点,提高了专业模型在业务部门中的使用率。未来,依靠“生态大数据+生态专业模型”的技术方案实现从数据到知识的挖掘,是实现城市生态大数据智慧化和专业化管理的关键,也是全面提高城市生态环境保护信息化服务水平的重要途径。     

Fuzzy rule‐based decision support system for evaluation of long‐established forest restoration projects

Although the importance of monitoring and evaluation of restoration actions is increasingly acknowledged, availability of accurate, quantitative monitoring data is very rare for most restoration areas, particularly for long‐established restoration projects. We propose using fuzzy rule‐based expert systems to evaluate the degree of success of restoration actions when available information on project results and impacts largely relies on expert‐based qualitative assessments and rough estimates of quantitative values. These systems use fuzzy logic to manage the uncertainty present in the data and to integrate qualitative and quantitative information. To illustrate and demonstrate the potential of fuzzy rule‐based systems for restoration evaluation, we applied this approach to seven forest restoration projects implemented in Spain between 1897 and 1952, using information compiled in the REACTION database on Mediterranean forest restoration projects. The information available includes both quantitative and expert‐based qualitative data, and covers a wide variety of indicators grouped into technical, structural, functional, and socioeconomic criteria. The fuzzy rule‐based system translates expert knowledge of restoration specialists and forest managers into a set of simple logic rules that integrate information on individual indicators into more general evaluation criteria. The rule‐based approach proposed here can be readily applicable to any kind of restoration project, provided that some information, even if vague and uncertain, is available for a variety of assessment indicators. The evaluation of long‐established forest restoration projects implemented in Spain revealed important asymmetries in the degree of restoration success between technical, structural, functional, and socioeconomic criteria.     

医院信息系统多元化集成发展的探讨

????? 阐述了医院信息系统的应用主线和系统当前面临的挑战,探讨了运用数据整合、统一平台以及面向服务的技术架构等方法,完成多元化信息集成交换平台的解决方案,对数据进行集成与利用,从而达到系统间的数据共享与交换、支持管理决策与分析的目的。

     

Using integrated state-of-environment reporting as a planning tool for resource management

The success of an ecosystems approach to river basin management depends greatly on the integration ofa wide range of information concerning the various components of the ecosystem, the formulation of realisticsolutions to complex problems, and finally, the support of all interested parties throughout the implementation phase:integrated State-of-Environment Reporting (SOER) can be an effective tool for meeting these challenges. Theecosystems approach is critical to the St. Lawrence Action Plan, a Canadian program which focuses on the protectionand conservation of the St. Lawrence River. The St. Lawrence Centre of Environment Canada not only acts asa catalyst in developing environmental partnerships among segments of the population, but synthesizes the resultsof these collective efforts. Its detailed evaluations on the state of the river environment perhaps best illustrate thenature of this task, because these reports must meet the particular environmental informational needs of many targetaudiences, which range from the general public to corporate decision-makers. SOER's practical nature facilitatesthe collection of data and the establishment of analysis targets and SOE indicators used in environmental monitoring.The integrated SOER framework developed at the St. Lawrence Centre represents a major challenge in the collection,integration, dissemination and use of environmental information. This framework is based in three majorobjectives: (1) information and education; (2) trend analysis and prediction; and (3) influence. Integrated SOER canbe a powerful tool: decision-makers will find it useful for designing action plans, while the general public can useit for assessing the state of the environment and the success of water management plans. Lastly, integrated SOERis also central to increasing public participation in the management of the environment, a factor essential to thesuccessful application of the ecosystems approach to water management.     

Risk-Sensitivity in Bayesian Sensorimotor Integration

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects'' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.     

Evaluating Environmental Performance of Pulp and Paper Manufacturing Using the Analytic Hierarchy Process and Life-Cycle Assessment

This article addresses the need for a structured and compre-hensive methodology for assessing the environmental perfor-mance of manufacturing processes. The analytic hierarchy pro-cess (AHP) is used as the basic framework for analyzing environmental impacts and improvement options following a streamlined life-cycle assessment (LCA) approach that is fo-cused on the manufacturing operation. The multicriteria de-cision analysis approach of the AHP is consistent with the LCA concept because the environmental factors can be hierarchi-cally structured into impacts and improvement options. Its po-tential as a valuation tool for impact and improvement assess-ment addresses both qualitative and quantitative issues in environmental decision making.
Through application to a pulp and paper manufacturing case study, the viability of the AHP for evaluating environmen-tal impacts and prioritizing process improvement options rela-tive to these impacts is demonstrated. AHP was used to pro-vide a quantitative tool for the design of a set of weighting factors for impact and improvement analyses.     

A proposal of a fuzzy rule-based system for the analysis of health and health environments in Brazil

A challenging activity in the promotion of sustainable development is to synthesise indicators that can support decision-making. In developing countries such as Brazil, the use of information in support of the health and health-related environment of humans needs improvement. This paper presents a proposal for a system that can evaluate human health and urban environment sanitation in an integrated manner. The indicators selected for human health were child mortality rate, mortality rate due to acute diarrhoea for all ages and 1–4-year-old mortality rate due to acute respiratory tract infections. Environmental sanitation has been represented by indicators of sanitation coverage, such as sewerage. The paper describes the design of a fuzzy linguistic model that synthesises these indicators. The designed fuzzy rule-based system has proved to be a useful tool to integrate information and assist in the public planning of interrelated areas.     

Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria

Material flow analysis (MFA) is a widely applied tool to investigate resource and recycling systems of metals and minerals. Owing to data limitations and restricted system understanding, MFA results are inherently uncertain. To demonstrate the systematic implementation of uncertainty analysis in MFA, two mathematical concepts for the quantification of uncertainties were applied to Austrian palladium (Pd) resource flows and evaluated: (1) uncertainty ranges expressed by fuzzy sets and (2) uncertainty ranges defined by normal distributions given as mean values and standard deviations. Whereas normal distributions represent the traditional approach for quantifying uncertainties in MFA, fuzzy sets may offer additional benefits in relation to uncertainty quantification in cases of scarce information. With respect to the Pd case study, the fuzzy representation of uncertain quantities is more consistent with the actual data availability in cases of incomplete databases, and fuzzy sets serve to highlight the effect of uncertainty on resource efficiency indicators derived from the MFA results. For both approaches, data reconciliation procedures offer the potential to reduce uncertainty and evaluate the plausibility of the model results. With respect to Pd resource management, improved formal collection of end‐of‐life (EOL) consumer products is identified as a key factor in increasing the recycling efficiency. In particular, the partial export of EOL vehicles represents a substantial loss of Pd from the Austrian resource system, whereas approximately 70% of the Pd in the EOL consumer products is recovered in waste management. In conclusion, systematic uncertainty analysis is an integral part of MFA required to provide robust decision support in resource management.     

Integrated Modeling Framework for Manufacturing Systems: A Unified Representation of the Physical Process and Information System

The task of process modeling in a manufacturing environment centers around controlling and improving the flow of materials. This flow comprises a complicated web of control and physical systems. Despite a variety of manufacturing system modeling approaches, more rigorous process modeling is required. This paper presents an integrated modeling framework for manufacturing systems (IMF-M). Conceptual modeling of physical materials flow supported by a graphical representation facilitates improvement of operations in manufacturing environments. A declarative and executable representation of control information systems helps to improve information management by managing a variety of information models with improved readability and reusability. A unified representation of the physical process and information system provides a common modeling milieu in which efforts can be coordinated among several groups working in the different domains of scheduling, shop floor and logistics control, and information system. Since the framework helps adapt to the changes of the physical process and information system affecting each other in a consistent manner, the modeling output enhances integration of the manufacturing system.