Architecture value mapping: using fuzzy cognitive maps as a reasoning mechanism for multi-criteria conceptual design evaluation

The conceptual design phase is the most critical phase in the systems engineering life cycle. The design concept chosen during this phase determines the structure and behavior of the system, and consequently, its ability to fulfill its intended function. A good conceptual design is the first step in the development of a successful artifact. However, decision-making during conceptual design is inherently challenging and often unreliable. The conceptual design phase is marked by an ambiguous and imprecise set of requirements, and ill-defined system boundaries. A lack of usable data for design evaluation makes the problem worse. In order to assess a system accurately, it is necessary to capture the relationships between its physical attributes and the stakeholders\u27 value objectives. This research presents a novel conceptual architecture evaluation approach that utilizes attribute-value networks, designated as \u27Architecture Value Maps\u27, to replicate the decision makers\u27 cogitative processes. Ambiguity in the system\u27s overall objectives is reduced hierarchically to reveal a network of criteria that range from the abstract value measures to the design-specific performance measures. A symbolic representation scheme, the 2-Tuple Linguistic Representation is used to integrate different types of information into a common computational format, and Fuzzy Cognitive Maps are utilized as the reasoning engine to quantitatively evaluate potential design concepts. A Linguistic Ordered Weighted Average aggregation operator is used to rank the final alternatives based on the decision makers\u27 risk preferences. The proposed methodology provides systems architects with the capability to exploit the interrelationships between a system\u27s design attributes and the value that stakeholders associate with these attributes, in order to design robust, flexible, and affordable systems --Abstract, page iii

A new integrated modeling approach to support management decisions of water resources systems under multiple uncertainties

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational framework. Such integrative research to link different knowledge domains faces several practical challenges. The complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. This thesis aims to overcome some of these challenges, and to demonstrate how new modeling approaches can provide successful integrative water resources research. It focuses on the development of new integrated modeling approaches which allow integration of not only physical processes but also socio-economic and environmental issues and uncertainties inherent in water resources systems. To achieve this goal, two new approaches are developed in this thesis. At first, a Bayesian network (BN)-based decision support tool is developed to conceptualize hydrological and socio-economic interaction for supporting management decisions of coupled groundwater-agricultural systems. The method demonstrates the value of combining different commonly used integrated modeling approaches. Coupled component models are applied to simulate the nonlinearity and feedbacks of strongly interacting groundwater-agricultural hydrosystems. Afterwards, a BN is used to integrate the coupled component model results with empirical knowledge and stakeholder inputs. In the second part of this thesis, a fuzzy-stochastic multiple criteria decision analysis tool is developed to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrates physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approaches are applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structures. The results show the effectiveness of the proposed methods. The first method can aid in the impact assessment of alternative management interventions on sustainability of aquifer systems while accounting for economic (agriculture) and societal interests (employment in agricultural sector) in the study area. Results from the second method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. The new approaches can be applied to address the complexities and uncertainties inherent in water resource systems to support management decisions, while serving as a platform for stakeholder participation

Multi-Criteria Decision Making in software development:a systematic literature review

Abstract. Multiple Criteria Decision Making is a formal approach to assist decision makers to select the best solutions among multiple alternatives by assessing criteria which are relatively precise but generally conflicting. The utilization of MCDM are quite popular and common in software development process. In this study, a systematic literature review which includes creating review protocol, selecting primary study, making classification schema, extracting data and other relevant steps was conducted. The objective of this study are making a summary about the state-of-the-art of MCDM in software development process and identifying the MCDM methods and MCDM problems in software development by systematically structuring and analyzing the literature on those issues. A total of 56 primary studies were identified after the review, and 33 types of MCDM methods were extracted from those primary studies. Among them, AHP was defined as the most frequent used MCDM methods in software development process by ranking the number of primary studies which applied it in their studies, and Pareto optimization was ranked in the second place. Meanwhile, 33 types of software development problems were identified. Components selection, design concepts selection and performance evaluation became the three most frequent occurred problems which need to be resolved by MCDM methods. Most of those MCDM problems were found in software design phase. There were many limitations to affect the quality of this study; however, the strictly-followed procedures of SLR and mass data from thousands of literature can still ensure the validity of this study, and this study is also able to provide the references when decision makers want to select the appropriate technique to cope with the MCDM problems

Using GIS-based methods of multicriteria analysis to construct socio-economic deprivation indices

<p>Abstract</p> <p>Background</p> <p>Over the past several decades researchers have produced substantial evidence of a social gradient in a variety of health outcomes, rising from systematic differences in income, education, employment conditions, and family dynamics within the population. Social gradients in health are measured using deprivation indices, which are typically constructed from aggregated socio-economic data taken from the national census – a technique which dates back at least until the early 1970's. The primary method of index construction over the last decade has been a Principal Component Analysis. Seldom are the indices constructed from survey-based data sources due to the inherent difficulty in validating the subjectivity of the response scores. We argue that this very subjectivity can uncover spatial distributions of local health outcomes. Moreover, indication of neighbourhood socio-economic status may go underrepresented when weighted without expert opinion. In this paper we propose the use of geographic information science (GIS) for constructing the index. We employ a GIS-based Order Weighted Average (OWA) Multicriteria Analysis (MCA) as a technique to validate deprivation indices that are constructed using more qualitative data sources. Both OWA and traditional MCA are well known and used methodologies in spatial analysis but have had little application in social epidemiology.</p> <p>Results</p> <p>A survey of British Columbia's Medical Health Officers (MHOs) was used to populate the MCA-based index. Seven variables were selected and weighted based on the survey results. OWA variable weights assign both local and global weights to the index variables using a sliding scale, producing a range of variable scenarios. The local weights also provide leverage for controlling the level of uncertainty in the MHO response scores. This is distinct from traditional deprivation indices in that the weighting is simultaneously dictated by the original respondent scores and the value of the variables in the dataset.</p> <p>Conclusion</p> <p>OWA-based MCA is a sensitive instrument that permits incorporation of expert opinion in quantifying socio-economic gradients in health status. OWA applies both subjective and objective weights to the index variables, thus providing a more rational means of incorporating survey results into spatial analysis.</p

Mathematical programming with uncertainty and multiple objectives for sustainable development and wildfire management

Mathematical Programming is a field of Operations Research well located for tackling problems as diverse as those arising in Logistics and Disaster Management. The main objective of Mathematical Programming is the selection of an optimal alternative satisfying a series of constraints. Traditionally alternatives are usually judged by a single criterion (for example, minimizing cost); however, it is also common that multiple objectives have to be considered simultaneously, leading to Multicriteria Decision Making. When the conditions to be satisfied by an alternative, or the evaluation of that alternative relies on random or unknown factors, there is a context of Optimization under uncertainty. The first chapters of this thesis study the field of Multicriteria Decision Making and Optimization under uncertainty, in two application in the context of sustainable development and disaster management. Optimization with uncertainty is presented with an application to rural electrification. It is common, especially in rural areas, that the access to electricity is provided via solar systems installed on the homes of the users. These systems have to be repaired when they malfunction. Consequently, the decision of how to size and locate a maintenance network is affected by uncertainty. A mathematical programming model is developed, treating the uncertainty in a non-explicit way, whose goal is to obtain a maintenance network at minimum cost. Such model is then used as a tool for obtaining more straightforward rules that are able to predict maintenance cost using limited information. The model is validated using information from a real program implemented in Morocco. When studying Multicriteria Decision Making a problem in wildfire management is considered. To mitigate the effect of wildfires, it is common the modification of forest, with what is known as fuel management. This technique, consisting in the felling or controlled burns of vegetation in selected areas, results on more manageable fires when they inevitably occur. Unfortunately, modifying flora can affect existing fauna, and thus it is sensible to search for solutions that improve the landscape wildfire-related, without substantial damage to existing species. That is, there are multiple criteria to take into account when optimizing. A mathematical programming model is developed, suggesting which areas to burn and when, taking into account the conflicting criteria. This model is applied to a series of realistic simulated cases. After that, a theoretical study of the field of Multiobjective Stochastic Programming (MSP) is performed, in which problems which simultaneously have multiple criteria and uncertainty are considered. In that chapter, a new concept of solution for MSP problems with risk-aversion is developed, its properties are studied, and a linear programming model is formulated for obtaining such a solution. A computational study of the model is also performed, applying it to a variant of the well-known knapsack problem. Finally, prescribed burning is studied again, considering this time the existing uncertainty due to not knowing how many prescribed burns can be completed within a year, caused by the limited time-window in which prescribed burns can be performed. The problem is solved using the risk-averse multiobjective stochastic methodology developed in the previous chapter. Lastly, the resulting model is applied to a real case located in the south of Spain

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version

BENCHMARKING CLASSIFIERS - HOW WELL DOES A GOWA-VARIANT OF THE SIMILARITY CLASSIFIER DO IN COMPARISON WITH SELECTED CLASSIFIERS?

Digital data is ubiquitous in nearly all modern businesses. Organizations have more data available, in various formats, than ever before. Machine learning algorithms and predictive analytics utilize the knowledge contained in that data, in order to help the business related decision-making. This study explores predictive analytics by comparing different classification methods – the main interest being in the Generalize Ordered Weighted Average (GOWA)-variant of the similarity classifier. The target for this research is to find out how what is the GOWA-variant of the similarity classifier and how well it performs compared to other selected classifiers. This study also tries to investigate whether the GOWA-variant of the similarity classifier is a sufficient method to be used in the busi-ness related decision-making. Four different classical classifiers were selected as reference classifiers on the basis of their common usage in machine learning research, and on their availability in the Sta-tistics and Machine Learning Toolbox in MATLAB. Three different data sets from UCI Machine Learning repository were used for benchmarking the classifiers. The benchmarking process uses fitness function instead of pure classification accuracy to determine the performance of the classifiers. Fitness function combines several measurement criteria into a one common value. With one data set, the GOWA-variant of the similarity classifier per-formed the best. One of the data sets contains credit card client data. It was more complex than the other two data sets and contains clearly business related data. The GOWA-variant performed also well with this data set. Therefore it can be claimed that the GOWA-variant of the similarity classifi-er is a viable option to be used also for solving business related problems

Fuzzy Sets in Business Management, Finance, and Economics

This book collects fifteen papers published in s Special Issue of Mathematics titled “Fuzzy Sets in Business Management, Finance, and Economics”, which was published in 2021. These paper cover a wide range of different tools from Fuzzy Set Theory and applications in many areas of Business Management and other connected fields. Specifically, this book contains applications of such instruments as, among others, Fuzzy Set Qualitative Comparative Analysis, Neuro-Fuzzy Methods, the Forgotten Effects Algorithm, Expertons Theory, Fuzzy Markov Chains, Fuzzy Arithmetic, Decision Making with OWA Operators and Pythagorean Aggregation Operators, Fuzzy Pattern Recognition, and Intuitionistic Fuzzy Sets. The papers in this book tackle a wide variety of problems in areas such as strategic management, sustainable decisions by firms and public organisms, tourism management, accounting and auditing, macroeconomic modelling, the evaluation of public organizations and universities, and actuarial modelling. We hope that this book will be useful not only for business managers, public decision-makers, and researchers in the specific fields of business management, finance, and economics but also in the broader areas of soft mathematics in social sciences. Practitioners will find methods and ideas that could be fruitful in current management issues. Scholars will find novel developments that may inspire further applications in the social sciences