Automated experience-based learning for plug and produce assembly systems

YesThis paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.European Union [grant number 314762]

An environment to support negotiation and contracting in collaborative networks

During the last years, manufacturing and service industries faced a global change in the production paradigm. They have to continuously adapt their operating principles in reaction to new business or collaboration opportunities, where a natural reaction is a shift to a new business paradigm with the creation of strategic alliances for product or services development, but also for innovative and emergent business services design. On one hand, the process of creating such alliances can be rather simple if organizations share the same geographical and cultural context. But on the other hand, considering different conditions, there might be a low success rate in the creation of successful consortia. One known reason for such low rate are the delays resulting from negotiations in the establishment of collaboration commitments, represented by contracts or agreements, which are crucial in the creation of such alliances. The collaborative networks discipline covers the study of networks of organizations specially when supported by computer networks. This thesis contributes with research in this field describing the creation process of virtual organizations, and proposing a negotiation support environment to help participants in the negotiation of the consortia creation process and in the co-design of new business services. A negotiation support environment is therefore proposed and described with its main requirements, adopted negotiation protocol, conceptual architecture, models, and software environment. To demonstrate the feasibility of the implementation of the proposed systems, a proof-ofconcept software prototype was implemented and tested using some specific scenarios. This thesis work has been validated adopting a methodology that includes: (i) validation in the research community; (ii) validation in a solar industry network; and (iii) validation by comparison analysis

C-EMO: A Modeling Framework for Collaborative Network Emotions

Recent research in the area of collaborative networks is focusing on the social and organizational complexity of collaboration environments as a way to prevent technological failures and consequently contribute for the collaborative network’s sustainability. One direction is moving towards the need to provide “human-tech” friendly systems with cognitive models of human factors such as stress, emotion, trust, leadership, expertise or decision-making ability. In this context, an emotion-based system is being proposed with this thesis in order to bring another approach to avoid collaboration network’s failures and help in the management of conflicts. This approach, which is expected to improve the performance of existing CNs, adopts some of the models developed in the human psychology, sociology and affective computing areas. The underlying idea is to “borrow” the concept of human-emotion and apply it into the context of CNs, giving the CN players the ability to “feel emotions”. Therefore, this thesis contributes with a modeling framework that conceptualizes the notion of “emotion” in CNs and a methodology approach based on system dynamics and agent-based techniques that estimates the CN player’s “emotional states” giving support to decision-making processes. Aiming at demonstrating the appropriateness of the proposed framework a simulation prototype was implemented and a validation approach was proposed consisting of simulation of scenarios, qualitative assessment and validation by research community peers.Recentemente a área de investigação das redes colaborativas tem vindo a debruçar-se na complexidade social e organizacional em ambientes colaborativos e como pode ser usada para prevenir falhas tecnológicas e consequentemente contribuir para redes colaborativas sustentáveis. Uma das direcções de estudo assenta na necessidade de fornecer sistemas amigáveis “humano-tecnológicos” com modelos cognitivos de factores humanos como o stress, emoção, confiança, liderança ou capacidade de tomada de decisão. É neste contexto que esta tese propõe um sistema baseado em emoções com o objectivo de oferecer outra aproximação para a gestão de conflitos e falhas da rede de colaboração. Esta abordagem, que pressupõe melhorar o desempenho das redes existentes, adopta alguns dos modelos desenvolvidos nas áreas da psicologia humana, sociologia e affective computing. A ideia que está subjacente é a de “pedir emprestado” o conceito de emoção humana e aplicá-lo no contexto das redes colaborativas, dando aos seus intervenientes a capacidade de “sentir emoções”. Assim, esta tese contribui com uma framework de modelação que conceptualiza a noção de “emoção” em redes colaborativas e com uma aproximação de metodologia sustentada em sistemas dinâmicos e baseada em agentes que estimam os “estados emocionais” dos participantes e da própria rede colaborativa. De forma a demonstrar o nível de adequabilidade da framework de modelação proposta, foi implementado um protótipo de simulação e foi proposta uma abordagem de validação consistindo em simulação de cenários, avaliação qualitativa e validação pelos pares da comunidade científica

Evolutionary Service Composition and Personalization Ecosystem for Elderly Care

Current demographic trends suggest that people are living longer, while the ageing process entails many necessities, calling for care services tailored to the individual senior’s needs and life style. Personalized provision of care services usually involves a number of stakeholders, including relatives, friends, caregivers, professional assistance organizations, enterprises, and other support entities. Traditional Information and Communication Technology based care and assistance services for the elderly have been mainly focused on the development of isolated and generic services, considering a single service provider, and excessively featuring a techno-centric approach. In contrast, advances on collaborative networks for elderly care suggest the integration of services from multiple providers, encouraging collaboration as a way to provide better personalized services. This approach requires a support system to manage the personalization process and allow ranking the {service, provider} pairs. An additional issue is the problem of service evolution, as individual’s care needs are not static over time. Consequently, the care services need to evolve accordingly to keep the elderly’s requirements satisfied. In accordance with these requirements, an Elderly Care Ecosystem (ECE) framework, a Service Composition and Personalization Environment (SCoPE), and a Service Evolution Environment (SEvol) are proposed. The ECE framework provides the context for the personalization and evolution methods. The SCoPE method is based on the match between the customer´s profile and the available {service, provider} pairs to identify suitable services and corresponding providers to attend the needs. SEvol is a method to build an adaptive and evolutionary system based on the MAPE-K methodology supporting the solution evolution to cope with the elderly's new life stages. To demonstrate the feasibility, utility and applicability of SCoPE and SEvol, a number of methods and algorithms are presented, and illustrative scenarios are introduced in which {service, provider} pairs are ranked based on a multidimensional assessment method. Composition strategies are based on customer’s profile and requirements, and the evolutionary solution is determined considering customer’s inputs and evolution plans. For the ECE evaluation process the following steps are adopted: (i) feature selection and software prototype development; (ii) detailing the ECE framework validation based on applicability and utility parameters; (iii) development of a case study illustrating a typical scenario involving an elderly and her care needs; and (iv) performing a survey based on a modified version of the technology acceptance model (TAM), considering three contexts: Technological, Organizational and Collaborative environment

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms