A simulation platform prototype for evaluating alternative scenarios of members integration in virtual organizations

Companies are facing growing challenges motivated by globalization. A globalized market means that the number of companies with which they will need to compete to maintain or enlarge their market share is increased. Additionally, it also brings greater opportunities to conquer new markets and increase existing market shares, since the geographical, political and economical boundaries are gradually being removed. This paper proposes and illustrates the use of a platform prototype for enabling the evaluation and selection of attractive members for being included as members in a virtual organization. An extended simulation study is described and results obtained presented which show advantage of a used dynamic evaluation and selection model, based on a varying set of criteria.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

An integrated framework for supporting fuzzy decision-making in networked manufacturing environments

In this paper we propose an integrated framework, based on smart objects to support fuzzy decision-making processes applied to manufacturing environments. The processes involved range from factory-production level up to higher decision-making levels, either in the context of traditional single enterprises, up to the one of supply chains and distributed and ubiquitous manufacturing environments. Therefore, the proposed framework promotes contributions for solving different kind of problems, including, among others: networked supply chain management; production planning and control; factory supervision and productivity management; real-time monitoring; data acquisition and processing. The web access via different middleware devices and tools at different process levels, along with the use of integrated algorithms and smart objects, which is possible and will promote an optimized use of knowledge and resources for supporting better decision-making. Moreover, the proposed framework also aims at promoting a wider collaboration process among various groups of stakeholders.This work was supported by FCT “Fundação para a Ciência e a Tecnologia” under the program: PEst20152020.info:eu-repo/semantics/publishedVersio

A Knowledge Based System for Supporting Sustainable Industrial Management in a Clothes Manufacturing Company Based on a Data Fusion Model

In this paper we propose a knowledge based system (KBS), based on smart objects and a data fusion model to support industrial management decision making applied to a clothes manufacturing enterprise. The management processes cover factory-production levels to higher decision-making levels. Therefore, the proposed KBS contributes to solving different kind of decision problems, including factory supervision, production planning and control, productivity management, real-time monitoring, and data acquisition and processing. The web access via different middleware devices and tools at different process levels, along with the use of integrated algorithms, decision methods, and smart objects, promote an optimized use of knowledge and resources. In this paper the proposed KBS is introduced and an example of its use is illustrated with an example of a clothes manufacturing resources selection, using the embedded dynamic multi-criteria fusion model.This work was supported by FCT “Fundação para a Ciência e a Tecnologia” under the program: PEST2015-2020, reference: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Spatial-temporal business partnership selection in uncertain environments

Small and Medium (SME) companies are facing growing challenges while trying to implement globalized business strategies. Contemporary business models need to account for spatial-temporal changeable environments, where lack of confidence and uncertainty in data are a reality. Further, SMEs are finding it increasingly difficult to include all required competences in their internal structures; therefore, they need to rely on reliable business and supplier partnerships to be successful. In this paper we discuss a spatial-temporal decision approach capable of handling lack of confidence and imprecision on current and/or forecast data. An illustrative case study of business' partner selection demonstrates the approach suitability, which is complemented by a statistical analysis with different levels of uncertainty to assess its robustness in uncertain environments.The authors wish to acknowledge the support of the Fundacao para a Ciencia e Tecnologia (FCT), Portugal, through the grant: "Projeto Estrategico - PEst2015-2020, reference: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Multiple attribute decision making (MADM) based scenarios

Decision making takes into account a myriad of factors about the future topics, which often prove challenging and quite complicated. Multiple Attribute Decision-Making (MADM) methods still have not become powerful enough to help decision makers to adopt the best solutions regarding future issues. Different scenarios are suitable for developing an appropriate outlook toward different probable futures. Scenarios are not inherently quantitative, but recently different integrated quantitative methods have been incorporated with the processes in various studies. Previously, different types of scenario-based MADM methods have been presented in different studies, but they just considered each case separately. In those studies, MADM methods were only applied to evaluate the situation in scenario-based MADM. This research concentrates on another paradigm in applying scenarios to upcoming events, MADM methods in the new area are explored, and the concept, which is called MADM based scenarios, is presented. In different situations and scenarios, different MADM models will happen. New concepts about most useful criterion and applicable alternatives are introduced in this new approach for decision-making about the future. In addition, a general framework is proposed for applying MADM-based scenarios for unpredictable scenarios and situations, which can be almost controlled future in practice

State-of-the-Art dynamischer Methoden zur multikriteriellen Entscheidungsunterstützung

Die Methoden der multikriteriellen Entscheidungsunterstützung (MCDA) bieten die Möglichkeit eine Vielzahl an Kriterien unterschiedlicher Natur im Zuge der Entscheidungsfindung simultan einzubeziehen. Bestimmte Entscheidungen, insbesondere im strategischen Bereich, zeichnen sich zudem durch eine hohe Komplexität aus, da die zugrundeliegenden Annahmen sowie die Auswirkungen der Entscheidung mit Unsicherheiten behaftet sind. Das Ziel dieser Arbeit war es, durch ein strukturiertes Literaturreview herauszustellen, welche Ansätze zur Erfassung einer solchen dynamischen Entscheidungskomponente es bislang gibt. Zur Identifikation relevanter Literatur wurden themenrelevante, wissenschaftliche Verlage wie ELSEVIER, sowie die EBSCO Datenbank genutzt. Auch Dissertationen, Konferenzberichte sowie vorherige Reviewartikel wurden inkludiert. Insgesamt wurden 60 Zeitschriftenartikel aus 31 verschiedenen Zeitschriften, 6 Konferenz-Paper, 11 Buchquellen und eine Dissertation gefunden. Die Literatur wurde anschließend nach dem zugrundeliegenden Verständnis der dynamischen Komponente, sowie deren methodischer Erfassung klassifiziert. Hierbei offenbarten sich drei Gruppen von Ansätzen Dynamik in die MCDA zu integrieren: (1) Szenario-basierte Ansätze, (2) Eine Kombination von MCDA mit Lebenszyklusmodellen (LCA), sowie (3) die direkte Einbeziehung von Dynamik in der Problemformulierung über mehrere Datensätze (DMCDA). Ein kritischer Vergleich dieser zeigt eine fortgeschrittene Entwicklung mit vielen Anwendungsbeispielen im Forschungsstrang der Szenario-basierten Ansätze. Eine Kombination von MCDA mit LCA kommt vor allem in Nachhaltigkeitsfragen und bei der Beurteilung von Energietechnologien zum Einsatz. Das Gebiet der DMCDA-Ansätze erweist sich als vergleichsweise jüngerer Forschungsstrang mit Ansatzpunkten für zukünftige Forschungsvorhaben. Die Methoden der multikriteriellen Entscheidungsunterstützung (MCDA) bieten die Möglichkeit eine Vielzahl an Kriterien unterschiedlicher Natur im Zuge der Entscheidungsfindung simultan einzubeziehen. Bestimmte Entscheidungen, insbesondere im strategischen Bereich, zeichnen sich zudem durch eine hohe Komplexität aus, da die zugrundeliegenden Annahmen sowie die Auswirkungen der Entscheidung mit Unsicherheiten behaftet sind. Das Ziel dieser Arbeit war es, durch ein strukturiertes Literaturreview herauszustellen, welche Ansätze zur Erfassung einer solchen dynamischen Entscheidungskomponente es bislang gibt. Zur Identifikation relevanter Literatur wurden themenrelevante, wissenschaftliche Verlage wie ELSEVIER, sowie die EBSCO Datenbank genutzt. Auch Dissertationen, Konferenzberichte sowie vorherige Reviewartikel wurden inkludiert. Insgesamt wurden 60 Zeitschriftenartikel aus 31 verschiedenen Zeitschriften, 6 Konferenz-Paper, 11 Buchquellen und eine Dissertation gefunden. Die Literatur wurde anschließend nach dem zugrundeliegenden Verständnis der dynamischen Komponente, sowie deren methodischer Erfassung klassifiziert. Hierbei offenbarten sich drei Gruppen von Ansätzen Dynamik in die MCDA zu integrieren: (1) Szenario-basierte Ansätze, (2) Eine Kombination von MCDA mit Lebenszyklusmodellen (LCA), sowie (3) die direkte Einbeziehung von Dynamik in der Problemformulierung über mehrere Datensätze (DMCDA). Ein kritischer Vergleich dieser zeigt eine fortgeschrittene Entwicklung mit vielen Anwendungsbeispielen im Forschungsstrang der Szenario-basierten Ansätze. Eine Kombination von MCDA mit LCA kommt vor allem in Nachhaltigkeitsfragen und bei der Beurteilung von Energietechnologien zum Einsatz. Das Gebiet der DMCDA-Ansätze erweist sich als vergleichsweise jüngerer Forschungsstrang mit Ansatzpunkten für zukünftige Forschungsvorhaben.  Keywords: Multikriterielle Entscheidungsunterstützung, DMCDA, uncertainty, dynamic decision making, MAD

Data Normalization in Decision Making Processes

With the fast-growing of data-rich systems, dealing with complex decision problems is unavoidable. Normalization is a crucial step in most multi criteria decision making (MCDM) models, to produce comparable and dimensionless data from heterogeneous data. Further, MCDM requires data to be numerical and comparable to be aggregated into a single score per alternative, thus providing their ranking. Several normalization techniques are available, but their performance depends on a number of characteristics of the problem at hand i.e., different normalization techniques may provide different rankings for alternatives. Therefore, it is a challenge to select a suitable normalization technique to represent an appropriate mapping from source data to a common scale. There are some attempts in the literature to address the subject of normalization in MCDM, but there is still a lack of assessment frameworks for evaluating normalization techniques. Hence, the main contribution and objective of this study is to develop an assessment framework for analysing the effects of normalization techniques on ranking of alternatives in MCDM methods and recommend the most appropriate technique for specific decision problems. The proposed assessment framework consists of four steps: (i) determining data types; (ii) chose potential candidate normalization techniques; (iii) analysis and evaluation of techniques; and (iv) selection of the best normalization technique. To validate the efficiency and robustness of the proposed framework, six normalization techniques (Max, Max-Min, Sum, Vector, Logarithmic, and Fuzzification) are selected from linear, semi-linear, and non-linear categories, and tested with four well known MCDM methods (TOPSIS, SAW, AHP, and ELECTRE), from scoring, comparative, and ranking methods. Designing the proposed assessment framework led to a conceptual model allowing an automatic decision-making process, besides recommending the most appropriate normalization technique for MCDM problems. Furthermore, the role of normalization techniques for dynamic multi criteria decision making (DMCDM) in collaborative networks is explored, specifically related to problems of selection of suppliers, business partners, resources, etc. To validate and test the utility and applicability of the assessment framework, a number of case studies are discussed and benchmarking and testimonies from experts are used. Also, an evaluation by the research community of the work developed is presented. The validation process demonstrated that the proposed assessment framework increases the accuracy of results in MCDM decision problems.Com o rápido crescimento dos sistemas ricos em dados, lidar com problemas de decisão complexos é inevitável. A normalização é uma etapa crucial na maioria dos modelos de tomada de decisão multicritério (MCDM), para produzir dados comparáveis e adimensionais a partir de dados heterogéneos, porque os dados precisam ser numéricos e comparáveis para serem agregados em uma única pontuação por alternativa. Como tal, várias técnicas de normalização estão disponíveis, mas o seu desempenho depende de uma série de características do problema em questão, ou seja, diferentes técnicas de normalização podem resultar em diferentes classificações para as alternativas. Portanto, é um desafio selecionar uma técnica de normalização adequada para representar o mapeamento dos dados de origem para uma escala comum. Existem algumas tentativas na literatura de abordar o assunto da normalização, mas ainda há uma falta de estrutura de avaliação para avaliar as técnicas de normalização sobre qual técnica é mais apropriada para os métodos MCDM.Assim, a principal contribuição e objetivo deste estudo são desenvolver uma ferramenta de avaliação para analisar os efeitos das técnicas de normalização na seriação de alternativas em métodos MCDM e recomendar a técnica mais adequada para problemas de decisão específicos. A estrutura de avaliação da ferramenta proposta consiste em quatro etapas: (i) determinar os tipos de dados, (ii) selecionar potenciais técnicas de normalização, (iii) análise e avaliação de técnicas em problemas de MCDM, e (iv) recomendação da melhor técnica para o problema de decisão. Para validar a eficácia e robustez da ferramenta proposta, seis técnicas de normalização (Max, Max-Min, Sum, Vector, Logarithmic e Fuzzification) foram selecionadas - das categorias lineares, semilineares e não lineares- e quatro conhecidos métodos de MCDM foram escolhidos (TOPSIS, SAW, AHP e ELECTRE). O desenho da ferramenta de avaliação proposta levou ao modelo conceptual que forneceu um processo automático de tomada de decisão, além de recomendar a técnica de normalização mais adequada para problemas de decisão. Além disso, é explorado o papel das técnicas de normalização para tomada de decisão multicritério dinâmica (DMCDM) em redes colaborativas, especificamente relacionadas com problemas de seleção de fornecedores, parceiros de negócios, recursos, etc. Para validar e testar a utilidade e aplicabilidade da ferramenta de avaliação, uma série de casos de estudo são discutidos e benchmarking e testemunhos de especialistas são usados. Além disso, uma avaliação do trabalho desenvolvido pela comunidade de investigação também é apresentada. Esta validação demonstrou que a ferramenta proposta aumenta a precisão dos resultados em problemas de decisão multicritério

Dynamic MCDM with future knowledge for supplier selection

Dynamic multi-criteria decision making (DMCDM) is an emerging subject in the decision-making area and in the last decade the challenge to consider time as an important variable has become important. Some frameworks already exist in this area but when compared with other types of decision-making models, DMCDM needs more work to be applicable in real industrial problems. In this work we extend a dynamic spatial-temporal framework, designed to deal with historical data (feedback), to address the problem of considering future information/knowledge (feed-forward). The main objective is to enrich dynamic decision-making models with explicit knowledge (existing historical data) and tacit knowledge (e.g. expert predictions) in time-evolving problems, such as supplier selection. Considering supplier-predicted information for future situations (e.g. investments in capacity) and, simultaneously, learning from historical data can help a company to find less risky and consistent alternatives. The proposed model is successfully implemented in a real case study for supplier selection in one automotive industry to demonstrate the capability and applicability of the model.- (undefined

Plataforma para suporte ao ciclo de vida de organizações virtuais e seleção dinâmica de parceiros e fornecedores

Tese de Doutoramento em Engenharia Industrial e de SistemasAs empresas enfrentam atualmente desafios crescentes motivados pela globalização. Por um lado, veêm crescer o número de concorrentes com os quais terão que competir para manterem a sua quota de mercado, e com quem terão que lidar no contexto de qualquer estratégia de crescimento. Por outro lado, encontram um conjunto alargado de oportunidades de entrada em novos mercados e de potencial aumento de penetração nos mercados em que operam, devido à crescente mitigação de barreiras geográficas, políticas e económicas. As estratégias de diversificação de mercados criam nas empresas a motivação para produzir novos produtos e disponibilizar novos serviços aos seus clientes. Adicionalmente, as estratégias de desenvolvimento de mercado assentes na conquista de clientes em novos segmentos geográficos e demográficos, ou na conquista de clientes potenciais nos segmentos existentes, podem ser suportadas por uma maior diversificação de produtos e maior agilidade ao nível do negócio. A diversificação de produtos e o aumento da agilidade dos processos requerem das empresas a posse de um leque cada vez mais amplo de competências de produção, logísticas, comerciais e de gestão. As pequenas e médias empresas têm cada vez maiores dificuldades em dispor deste leque de competências, de forma integral, na sua estrutura interna, pelo que promovem a criação de relações mais próximas com seus parceiros de negócio e fornecedores, que as complementam. As redes colaborativas são uma importante ferramenta para apoiar as empresas neste processo. Das redes colaborativas de fornecimento até às empresas ágeis virtuais são várias as opções disponíveis para as empresas otimizarem a sua relação com os seus fornecedores e parceiros. Para as implementar, de forma eficiente, as empresas necessitam de ferramentas tecnológicas que lhes permitam identificar, avaliar e selecionar os melhores parceiros e fornecedores num contexto de negócio dinâmico, em mudança constante. Nesta tese de doutoramento é proposta uma plataforma, baseada em agentes de software, para suportar o ciclo de vida das redes colaborativas e organizações virtuais, bem como uma abordagem dinâmica de avaliação de parceiros e fornecedores, que pode apoiar as empresas na otimização das suas relações com os parceiros de negócio.Companies are facing growing challenges motivated by globalization. A globalized market means that the number of companies with which they will need to compete to maintain or enlarge their market share is increased. Additionally, it also brings greater opportunities to conquer new markets and increase existing market shares, since the geographical, political and economical boundaries are gradually being removed. Strategies of market diversification push companies to provide novel products and services to customers, belonging to new geographic and demographic segments. Additionally, market development strategies targeting non-buying customers in selected segments or new buyers in new segments may be paired with increased product diversification and improved business agility. To fulfil the requirements associated with manufacturing a wider range of products and increased agility at process level implies having a wider set of competences available. Small and Medium (SME) companies find it increasingly difficult to have all the required competences in their internal structures; therefore, they need to rely on strategic business partnerships and suppliers to be successful. Collaborative networks are an important tool to assist companies creating and improving their business partnerships. From collaborative supply chains to agile virtual enterprises, companies have several options at their disposal to optimize their relations with business partners and suppliers. When a company decides to develop such a network, it needs decision support tools to select the best partners or suppliers, particularly in today’s spatial-temporal changeable global environments. In this doctoral thesis, a platform based on software agents is proposed, to support the lifecycle of collaborative networks and virtual organizations, along with a dynamic decision approach for supplier and business partner evaluation and selection, which can prove valuable to enable companies to establish strong collaborative business networks