Best practice factory for freight transport in Europe: demonstrating how ‘good’ urban freight cases are improving business profit and public sectors benefits

The objective of this study is to better understand why selected urban freight solutions represent innovations that are technically feasible, economically profitable in different contexts, sustainable, transferable, and with tangible beneficial impacts. A total of 15 solutions are evaluated in the fields of Urban Consolidation Centre, clean and electric vehicles, IT solutions, use of urban waterways, and others. Three solutions are analysed more thoroughly, the Cityporto Padova, the Basel Exhibition Centre logistics support system, and the Berlin laboratory area test of the Bentobox. This paper ends with a transversal analysis of the solutions observed, and with methodological conclusions.

Best practice factory for freight transport in Europe: demonstrating how ‘good’ urban freight cases are improving business profit and public sectors benefits

The objective of this study is to better understand why selected urban freight solutions represent innovations that are technically feasible, economically profitable in different contexts, sustainable, transferable, and with tangible beneficial impacts. A total of 15 solutions are evaluated in the fields of Urban Consolidation Centre, clean and electric vehicles, IT solutions, use of urban waterways, and others. Three solutions are analysed more thoroughly, the Cityporto Padova, the Basel Exhibition Centre logistics support system, and the Berlin laboratory area test of the Bentobox. This paper ends with a transversal analysis of the solutions observed, and with methodological conclusions.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Enhancing Agent Mediated Electronic Markets with Ontology Matching Services and Social Network Support

Os Mercados Eletrónicos atingiram uma complexidade e nível de sofisticação tão elevados, que tornaram inadequados os modelos de software convencionais. Estes mercados são caracterizados por serem abertos, dinâmicos e competitivos, e constituídos por várias entidades independentes e heterogéneas. Tais entidades desempenham os seus papéis de forma autónoma, seguindo os seus objetivos, reagindo às ocorrências do ambiente em que se inserem e interagindo umas com as outras. Esta realidade levou a que existisse por parte da comunidade científica um especial interesse no estudo da negociação automática executada por agentes de software [Zhang et al., 2011]. No entanto, a diversidade dos atores envolvidos pode levar à existência de diferentes conceptualizações das suas necessidades e capacidades dando origem a incompatibilidades semânticas, que podem prejudicar a negociação e impedir a ocorrência de transações que satisfaçam as partes envolvidas. Os novos mercados devem, assim, possuir mecanismos que lhes permitam exibir novas capacidades, nomeadamente a capacidade de auxiliar na comunicação entre os diferentes agentes. Pelo que, é defendido neste trabalho que os mercados devem oferecer serviços de ontologias que permitam facilitar a interoperabilidade entre os agentes. No entanto, os humanos tendem a ser relutantes em aceitar a conceptualização de outros, a não ser que sejam convencidos de que poderão conseguir um bom negócio. Neste contexto, a aplicação e exploração de relações capturadas em redes sociais pode resultar no estabelecimento de relações de confiança entre vendedores e consumidores, e ao mesmo tempo, conduzir a um aumento da eficiência da negociação e consequentemente na satisfação das partes envolvidas. O sistema AEMOS é uma plataforma de comércio eletrónico baseada em agentes que inclui serviços de ontologias, mais especificamente, serviços de alinhamento de ontologias, incluindo a recomendação de possíveis alinhamentos entre as ontologias dos parceiros de negociação. Este sistema inclui também uma componente baseada numa rede social, que é construída aplicando técnicas de análise de redes socias sobre informação recolhida pelo mercado, e que permite melhorar a recomendação de alinhamentos e auxiliar os agentes na sua escolha. Neste trabalho são apresentados o desenvolvimento e implementação do sistema AEMOS, mais concretamente: • É proposto um novo modelo para comércio eletrónico baseado em agentes que disponibiliza serviços de ontologias; • Adicionalmente propõem-se o uso de redes sociais emergentes para captar e explorar informação sobre relações entre os diferentes parceiros de negócio; • É definida e implementada uma componente de serviços de ontologias que é capaz de: • o Sugerir alinhamentos entre ontologias para pares de agentes; • o Traduzir mensagens escritas de acordo com uma ontologia em mensagens escritas de acordo com outra, utilizando alinhamentos previamente aprovados; • o Melhorar os seus próprios serviços recorrendo às funcionalidades disponibilizadas pela componente de redes sociais; • É definida e implementada uma componente de redes sociais que: • o É capaz de construir e gerir um grafo de relações de proximidade entre agentes, e de relações de adequação de alinhamentos a agentes, tendo em conta os perfis, comportamento e interação dos agentes, bem como a cobertura e utilização dos alinhamentos; • o Explora e adapta técnicas e algoritmos de análise de redes sociais às várias fases dos processos do mercado eletrónico. A implementação e experimentação do modelo proposto demonstra como a colaboração entre os diferentes agentes pode ser vantajosa na melhoria do desempenho do sistema e como a inclusão e combinação de serviços de ontologias e redes sociais se reflete na eficiência da negociação de transações e na dinâmica do mercado como um todo.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Sistemas de gestión para el control de la cadena de abastecimiento que apropie características de los cubos OLAP como marco de aplicación en una empresa colombiana del sector de alimentos.

Se basa en un análisis teórico de los sistemas de información como lo es el almacenaje de datos, cubos OLAP e inteligencia de negocios. Seguidamente, se hace un análisis de los sectores económicos de Colombia con un especial interés sobre el sector de alimentos, de esta manera conceptualizar la empresa sobre la cual este trabajo se enfocara. Se encontrará un análisis del caso de éxito Summerwood Corporation, el cual brindará una justificación para la propuesta final presentada a la empresa Dipsa Food, Pyme dedicada a la producción de alimentos no perecederos ubicada en la ciudad de Bogotá D.C –Colombia, la cual tiene gran interés en cuanto al desarrollo de nuevas tecnologías que brinden información fidedigna para la toma de decisiones

El Outsourcing en la dinámica de sistemas hacia mercados extranjeros

Este trabajo recopila literatura académica relevante sobre estrategias de entrada y metodologías para la toma de decisión sobre la contratación de servicios de Outsourcing para el caso de empresas que planean expandirse hacia mercados extranjeros. La manera en que una empresa planifica su entrada a un mercado extranjero, y realiza la consideración y evaluación de información relevante y el diseño de la estrategia, determina el éxito o no de la misma. De otro lado, las metodologías consideradas se concentran en el nivel estratégico de la pirámide organizacional. Se parte de métodos simples para llegar a aquellos basados en la Teoría de Decisión Multicriterio, tanto individuales como híbridos. Finalmente, se presenta la Dinámica de Sistemas como herramienta valiosa en el proceso, por cuanto puede combinarse con métodos multicriterio.

Modelo de gestión del conocimiento para una empresa del sector de insumos químicos para la industria farmacéutica, cosmética y de alimentos: caso Disaromas S.A.

La creación de conocimiento al interior de las organizaciones es visible mediante la dirección adecuada del conocimiento de los individuos, sin embargo, cada individuo debe interactuar de tal manera que forme una red o sistema de conocimiento organizacional que consolide a largo plazo las empresas en el entorno en el que se desenvuelven. Este documento revisa elementos centrales acerca de la gestión de conocimiento visto desde varios autores y perspectivas e identifica puntos clave para diseñar un modelo de gestión de conocimiento para una empresa del sector de insumos químicos para la industria farmacéutica, cosmética y de alimentos de la ciudad de Bogotá.

Emociones y toma de decisiones éticas: desarrollo e implicaciones para la empresa

Los líderes organizacionales se deben enfrentar a retos ambientales del mundo de los negocios y diversas presiones que los ponen día a día en un alto riesgo ético. Sortear dichos riesgos ha demandado cambios sustanciales en las dinámicas de las organizaciones contemporáneas, por lo que las exigencias a los directivos de tomar decisiones acertadas en situaciones de alta complejidad moral son cada vez mayores. Estas decisiones involucran un comportamiento ético de quien las toma, lo cual a su vez está mediado por sus emociones.

Gestão e trabalhadores do conhecimento em Tecnologias da informação (UML)

RESUMO: O conhecimento existe desde sempre, mesmo num estado latente condicionado algures e apenas à espera de um meio (de uma oportunidade) de se poder manifestar. O conhecimento é duplamente um fenómeno da consciência: porque dela procede num dado momento da sua vida e da sua história e porque só nela termina, aperfeiçoando-a e enriquecendo-a. O conhecimento está assim em constante mudança. À relativamente pouco tempo começou-se a falar de Gestão do Conhecimento e na altura foi muito associada às Tecnologias da Informação, como meio de colectar, processar e armazenar cada vez mais, maiores quantidades de informação. As Tecnologias da Informação têm tido, desde alguns anos para cá, um papel extremamente importante nas organizações, inicialmente foram adoptadas com o propósito de automatizar os processos operacionais das organizações, que suportam as suas actividades quotidianas e nestes últimos tempos as Tecnologias da Informação dentro das organizações têm evoluído rapidamente. Todo o conhecimento, mesmo até o menos relevante de uma determinada área de negócio, é fundamental para apoiar o processo de tomada de decisão. As organizações para atingirem melhores «performances» e conseguirem transcender as metas a que se propuseram inicialmente, tendem a munir-se de mais e melhores Sistemas de Informação, assim como, à utilização de várias metodologias e tecnologias hoje em dia disponíveis. Por conseguinte, nestes últimos anos, muitas organizações têm vindo a demonstrar uma necessidade crucial de integração de toda a sua informação, a qual está dispersa pelos diversos departamentos constituintes. Para que os gestores de topo (mas também para outros funcionários) possam ter disponível em tempo útil, informação pertinente, verdadeira e fiável dos negócios da organização que eles representam, precisam de ter acesso a bons Sistemas de Tecnologias de Informação. Numa acção de poderem agir mais eficazmente e eficientemente nas tomadas de decisão, por terem conseguido tirar por esses meios o máximo de proveito possível da informação, e assim, apresentarem melhores níveis de sucesso organizacionais. Também, os Sistemas de «Business Intelligence» e as Tecnologias da Informação a ele associadas, utilizam os dados existentes nas organizações para disponibilizar informação relevante para as tomadas de decisão. Mas, para poderem alcançar esses níveis tão satisfatórios, as organizações necessitam de recursos humanos, pois como podem elas serem competitivas sem Luís Miguel Borges – Gestão e Trabalhadores do Conhecimento em Tecnologias da Informação (UML) ULHT – ECATI 6 trabalhadores qualificados. Assim, surge a necessidade das organizações em recrutar os chamados hoje em dia “Trabalhadores do Conhecimento”, que são os indivíduos habilitados para interpretar as informações dentro de um domínio específico. Eles detectam problemas e identificam alternativas, com os seus conhecimentos e discernimento, eles trabalham para solucionar esses problemas, ajudando consideravelmente as organizações que representam. E, usando metodologias e tecnologias da Engenharia do Conhecimento como a modelação, criarem e gerirem um histórico de conhecimento, incluindo conhecimento tácito, sobre várias áreas de negócios da organização, que podem estar explícitos em modelos abstractos, que possam ser compreendidos e interpretados facilmente, por outros trabalhadores com níveis de competência equivalentes. ABSTRACT: Knowledge has always existed, even in a latent state conditioning somewhere and just waiting for a half (an opportunity) to be able to manifest. Knowledge is doubly a phenomenon of consciousness: because proceeds itself at one point in its life and its history and because solely itself ends, perfecting it and enriching it. The knowledge is so in constant change. In the relatively short time that it began to speak of Knowledge Management and at that time was very associated with Information Technologies, as a means to collect, process and store more and more, larger amounts of information. Information Technologies has had, from a few years back, an extremely important role in organizations, were initially adopted in order to automate the operational processes of organizations, that support their daily activities and in recent times Information Technologies within organizations has evolved rapidly. All the knowledge, even to the least relevant to a particular business area, is fundamental to support the process of decision making. The organizations to achieve better performances and to transcend the goals that were initially propose, tend to provide itself with more and better Information Systems, as well as, the use of various methodologies and technologies available today. Consequently, in recent years, many organizations have demonstrated a crucial need for integrating all their information, which is dispersed by the diver constituents departments. For top managers (but also for other employees) may have ready in time, pertinent, truthful and reliable information of the organization they represent, need access to good Information Technology Systems. In an action that they can act more effectively and efficiently in decision making, for having managed to get through these means the maximum possible advantage of the information, and so, present better levels of organizational success. Also, the systems of Business Intelligence and Information Technologies its associated, use existing data on organizations to provide relevant information for decision making. But, in order to achieve these levels as satisfactory, organizations need human resources, because how can they be competitive without skilled workers. Thus, arises the need for organizations to recruit called today “Knowledge Workers”, they are the individuals enable to interpret the information within a specific domain. They detect problems and identify alternatives, with their knowledge and discernment they work to solve these problems, helping considerably the organizations that represent. And, using Luís Miguel Borges – Gestão e Trabalhadores do Conhecimento em Tecnologias da Informação (UML) ULHT – ECATI 8 methodologies and technologies of Knowledge Engineering as modeling, create and manage a history of knowledge, including tacit knowledge, on various business areas of the organization, that can be explicit in the abstract models, that can be understood and interpreted easily, by other workers with equivalent levels of competence.