Operational Risk Management using a Fuzzy Logic Inference System

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge or imprecise information are valuable inputs. One of the solutions provided by engineering is a Fuzzy Logic Inference System (FLIS). Despite the goal of the FLIS model for OR is its assessment, it is not an end in itself. The choice of a FLIS results in a convenient and sound use of qualitative and quantitative inputs, capable of effectively articulating risk management's identification, assessment, monitoring and mitigation stages. Different from traditional approaches, the proposed model allows evaluating mitigation efforts ex-ante, thus avoiding concealed OR sources from system complexity build-up and optimizing risk management resources. Furthermore, because the model contrasts effective with expected OR data, it is able to constantly validate its outcome, recognize environment shifts and issue warning signals.Operational Risk, Fuzzy Logic, Risk Management Classification JEL:G32, C63, D80

Fuzzy segmentation for geographic object-based image analysis

Image segmentation partitions remote sensing images into image objects before assigning them to categorical land cover classes. Current segmentation methods require users to invest considerable time and effort in the search for meaningful image objects. As an alternative method we propose 'fuzzy' segmentation that offers more flexibility in dealing with remote sensing uncertainty. In the proposed method, original bands are processed using regression techniques to output fuzzy image regions which express degrees of membership to target land cover classes. Contextual properties of fuzzy regions can be measured to indicate potential spectral confusion. A 'defuzzification' process is subsequently conducted to produce the categorical land cover classes. This method was tested using data sets of both high and medium spatial resolution. The results indicate that this approach is able to produce classification with satisfying accuracy and requires very little user interaction

Induction, complexity, and economic methodology

This paper focuses on induction, because the supposed weaknesses of that process are the main reason for favouring falsificationism, which plays an important part in scientific methodology generally; the paper is part of a wider study of economic methodology. The standard objections to, and paradoxes of, induction are reviewed, and this leads to the conclusion that the supposed ‘problem’ or ‘riddle’ of induction is a false one. It is an artefact of two assumptions: that the classic two-valued logic (CL) is appropriate for the contexts in which induction is relevant; and that it is the touchstone of rational thought. The status accorded to CL is the result of historical and cultural factors. The material we need to reason about falls into four distinct domains; these are explored in turn, while progressively relaxing the restrictions that are essential to the valid application of CL. The restrictions include the requirement for a pre-existing, independently-guaranteed classification, into which we can fit all new cases with certainty; and non-ambiguous relationships between antecedents and consequents. Natural kinds, determined by the existence of complex entities whose characteristics cannot be unbundled and altered in a piecemeal, arbitrary fashion, play an important part in the review; so also does fuzzy logic (FL). These are used to resolve two famous paradoxes about induction (the grue and raven paradoxes); and the case for believing that conventional logic is a subset of fuzzy logic is outlined. The latter disposes of all questions of justifying induction deductively. The concept of problem structure is used as the basis for a structured concept of rationality that is appropriate to all four of the domains mentioned above. The rehabilitation of induction supports an alternative definition of science: that it is the business of developing networks of contrastive, constitutive explanations of reproducible, inter-subjective (‘objective’) data. Social and psychological obstacles ensure the progress of science is slow and convoluted; however, the relativist arguments against such a project are rejected.induction; economics; methodology; complexity

A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments

Integrated supplier selection and order allocation is an important decision for both designing and operating supply chains. This decision is often influenced by the concerned stakeholders, suppliers, plant operators and customers in different tiers. As firms continue to seek competitive advantage through supply chain design and operations they aim to create optimized supply chains. This calls for on one hand consideration of multiple conflicting criteria and on the other hand consideration of uncertainties of demand and supply. Although there are studies on supplier selection using advanced mathematical models to cover a stochastic approach, multiple criteria decision making techniques and multiple stakeholder requirements separately, according to authors' knowledge there is no work that integrates these three aspects in a common framework. This paper proposes an integrated method for dealing with such problems using a combined Analytic Hierarchy Process-Quality Function Deployment (AHP-QFD) and chance constrained optimization algorithm approach that selects appropriate suppliers and allocates orders optimally between them. The effectiveness of the proposed decision support system has been demonstrated through application and validation in the bioenergy industry

The outsourcing of household tasks and labour contract in domestic work

This paper empirically illustrates that flexible work arrangements may be found unsuitable for outsourcing certain household tasks. For this purpose we analyse the relationship between tasks performed by domestic workers and the nature of the labour contract. Our study draws on a dataset from a sample of Portuguese domestic workers, and uses a fuzzy clustering approach to identify bundles of tasks together with contract features. The results achieved suggest a segmentation of domestic workers into four groups: two comprising carers, who enjoy a standard type of contract, and another two groups of cleaners with flexible and informal work arrangements. However, there is no distinct boundary between these groups. The overlapping that occurs between tasks and contracts justifies the use of the fuzzy approach to data analysis.FC

Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization

We reflect on a four-year engagement with transport authorities and others involving a large dataset describing the use of a public bicycle-sharing scheme. We describe the role visualization of these data played in fostering engagement with policy makers, transport operators, the transport research community, the museum and gallery sector and the general public. We identify each of these as ‘channels’ – evolving relationships between producers and consumers of visualization – where traditional roles of the visualization expert and domain expert are blurred. In each case, we identify the different design decisions that were required to support each of these channels and the role played by the visualization process. Using chauffeured interaction with a flexible visual analytics system we demonstrate how insight was gained by policy makers into gendered spatio-temporal cycle behaviors, how this led to further insight into workplace commuting activity, group cycling behavior and explanations for street navigation choice. We demonstrate how this supported, and was supported by, the seemingly unrelated development of narrative-driven visualization via TEDx, of the creation and the setting of an art installation and the curating of digital and physical artefacts. We assert that existing models of visualization design, of tool/technique development and of insight generation do not adequately capture the richness of parallel engagement via these multiple channels of communication. We argue that developing multiple channels in parallel opens up opportunities for visualization design and analysis by building trust and authority and supporting creativity. This rich, non-sequential approach to visualization design is likely to foster serendipity, deepen insight and increase impact

Life Cycle Based Sustainability Assessment And Decision Making For Industrial Systems

Increasing concern with the environmental impact resulted from human activities has led to a rising interest in sustainable development that will not only meet the needs of current development but also protect the natural environment without compromising the needs of future generations. This leads to the necessity of a systems approach to decision-making in which economic, environmental and social factors are integrated together to ensure the triple bottom lines of sustainability. Although current studies provide a variety of different methodologies to address sustainability assessment and decision-making, the increasing size and complexity of industrial systems results in the necessity to develop more comprehensive systems approaches to ensure the sustainable development over a long time period for industrial systems. What\u27s more, current research may conduct results based on one or only a few stages of the manufacturing process without considering all the stages of a product’s life. Therefore, the results could be bias and sometimes not feasible for the whole life-cycle. In the meanwhile, life cycle analysis (LCA) which has been widely adopted in a variety of industries does provide an effective approach to evaluate the environmental impact. The lack of life-cycle based economic and social sustainability assessment results in the difficult to conduct more comprehensive sustainability assessment. To address these challenges, three fundamental frameworks are developed in this dissertation, that is, life cycle based sustainability assessment (LCBSA) framework, life cycle based decision-making (LCBDM) framework, and fuzzy dynamic programming (FDP) based long-term multistage sustainable development framework. LCBSA can offer a profound insight of status quo of the sustainability performance over the whole life cycle. LCSA is then applied to assess the industrial system of automotive coating manufacturing process from raw material extraction, material manufacturing, product manufacturing to the recycle and disposal stage. The following LCBDM framework could then prioritize the sustainability improvement urgency and achieve comprehensive sustainable development by employing a two-phase decision-making methodology. In addition, FDP based long-term multistage sustainable development framework offers a comprehensive way to ascertain the achievement of long time sustainable development goal of complex and dynamic industrial systems by combining decision-making and sustainability assessment together

The Quantification of Perception Based Uncertainty Using R-fuzzy Sets and Grey Analysis

The nature of uncertainty cannot be generically defined as it is domain and context specific. With that being the case, there have been several proposed models, all of which have their own associated benefits and shortcomings. From these models, it was decided that an R-fuzzy approach would provide for the most ideal foundation from which to enhance and expand upon. An R-fuzzy set can be seen as a relatively new model, one which itself is an extension to fuzzy set theory. It makes use of a lower and upper approximation bounding from rough set theory, which allows for the membership function of an R-fuzzy set to be that of a rough set. An R-fuzzy approach provides the means for one to encapsulate uncertain fuzzy membership values, based on a given abstract concept. If using the voting method, any fuzzy membership value contained within the lower approximation can be treated as an absolute truth. The fuzzy membership values which are contained within the upper approximation, may be the result of a singleton, or the vast majority, but absolutely not all. This thesis has brought about the creation of a significance measure, based on a variation of Bayes' theorem. One which enables the quantification of any contained fuzzy membership value within an R-fuzzy set. Such is the pairing of the significance measure and an R-fuzzy set, an intermediary bridge linking to that of a generalised type-2 fuzzy set can be achieved. Simply by inferencing from the returned degrees of significance, one is able to ascertain the true significance of any uncertain fuzzy membership value, relative to other encapsulated uncertain values. As an extension to this enhancement, the thesis has also brought about the novel introduction of grey analysis. By utilising the absolute degree of grey incidence, it provides one with the means to measure and quantify the metric spaces between sequences, generated based on the returned degrees of significance for any given R-fuzzy set. As it will be shown, this framework is ideally suited to domains where perceptions are being modelled, which may also contain several varying clusters of cohorts based on any number of correlations. These clusters can then be compared and contrasted to allow for a more detailed understanding of the abstractions being modelled

Identifying cost, schedule, and performance risks in project planning and control: A fuzzy logic approach.

A review of risk identification and quantification methods revealed the need for additional methods to assess cost, schedule, and performance estimation. A risk model was developed using fuzzy set theory. The risk model was tested using a sample radar development project. The results obtained from the model proved that a practical approach incorporating subject-matter expert assessment and fuzzy set theory could be used to both identify and quantify project risks. Outputs from the model had sufficient fidelity for decision-makers to determine areas for additional surveillance and/or control.In a "real" project management environment historical cost, schedule, and performance data are often not available. The lack of historical data requires the estimation of cost, schedule, and performance parameters. The uncertainties associated with parameter estimation results in inherent project risks. The identification and quantification of project risks associated with parameter estimation requires analytical tools that are effective and usable in project planning and control