An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing

Several conflicting criteria must be optimized simultaneously during the service composition and optimal selection (SCOS) in cloud manufacturing, among which tradeoff optimization regarding the quality of the composite services is a key issue in successful implementation of manufacturing tasks. This study improves the artificial bee colony (ABC) algorithm by introducing a synergetic mechanism for food source perturbation, a new diversity maintenance strategy, and a novel computing resources allocation scheme to handle complicated many-objective SCOS problems. Specifically, differential evolution (DE) operators with distinct search behaviors are integrated into the ABC updating equation to enhance the level of information exchange between the foraging bees, and the control parameters for reproduction operators are adapted independently. Meanwhile, a scalarization based approach with active diversity promotion is used to enhance the selection pressure. In this proposal, multiple size adjustable subpopulations evolve with distinct reproduction operators according to the utility of the generating offspring so that more computational resources will be allocated to the better performing reproduction operators. Experiments for addressing benchmark test instances and SCOS problems indicate that the proposed algorithm has a competitive performance and scalability behavior compared with contesting algorithms

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

Propostas para o Futuro do Setor Vitivinícola Português

Para se manter competitiva, a indústria portuguesa necessita de aumentar a sua economia de escala, através do aumento da sua capacidade de produção e de comercialização, com eficácia e eficiência, de forma a favorecer a redução dos custos diretos e indiretos. Para tal, a Indústria 4.0 reveste-se de especial importância para a indústria portuguesa, visto que se trata de uma oportunidade notória para conseguir colmatar as principais barreiras competitivas. Por outro lado, o crescimento das organizações pode acarretar, muitas vezes, o aumento do número de acidentes de trabalho, proporcionando consequentemente um aumento ao nível dos encargos financeiros desnecessariamente. Deste modo, torna-se pertinente que as organizações desenvolvam um planeamento que favoreça um crescimento mais sustentável em todas as linhas, incluindo uma cultura para a prevenção de riscos ocupacionais. Deste modo, esta dissertação visa propor três modelos de otimização, que assentam em três vetores de atuação essenciais: a organização de layouts produtivos, cuja metodologia se baseia na aplicação de um algoritmo genético; o planeamento de tarefas, fundamentado na utilização de um algoritmo de otimização por colónias de formigas (Ant Colony Optimization - ACO); e a implementação de ferramentas da Indústria 4.0 adequadas à minimização dos riscos ergonómicos, cuja abordagem assenta no desenvolvimento de um método de lógica difusa (fuzzy logic). A metodologia foi iniciada com a realização de uma avaliação de riscos gerais associados a cada uma das áreas de laboração, as quais normalmente fazem parte de uma empresa vitivinícola, de acordo com os cenários estabelecidos. A avaliação foi efetuada com base o método William T. Fine, que permitiu obter uma estimativa sobre os graus de perigosidade dos potenciais riscos relacionados com cada uma das zonas de laboração, sendo seguida de uma avaliação de riscos ergonómicos baseada no gasto de energia metabólica no decurso da execução das tarefas, a qual permitiu a quantificação dos riscos inerentes às tarefas dos processos produtivos. Com base nos resultados das avaliações de riscos efetuadas, foram aplicados os modelos de otimização de layouts de produção e de planeamento de tarefas, tendo sido possível obter soluções viáveis para os cenários que integram todas as operações que decorrem na época alta (época de vindima), tanto em termos de operacionalidade dos processos produtivos, como em relação a minimização de riscos gerais e ergonómicos. Relativamente aos cenários estabelecidos para a época baixa, foram obtidos resultados viáveis relativamente à otimização do planeamento de tarefas, tendo-se verificado que os resultados obtidos a partir da aplicação do modelo de otimização de layouts de produção, podem apenas ser considerados como resultados complementares, os quais sugerem uma implantação de infraestruturas em dois níveis ou pisos diferenciados. Ainda com base nos resultados das avaliações de riscos, a aplicação do método de seleção de ferramentas da Indústria 4.0 para minimização de riscos ergonómicos também proporcionou soluções viáveis em termos operacionais, verificando-se uma forte tendência para a recomendação de sistemas mais autónomos, nomeadamente os sistemas integrados automatizados e os robôs autónomos. A aplicação do método HTA (Hierarchical Task Analysis) proporcionou a descrição detalhada de um cenário contemplando a implementação das ferramentas da Indústria 4.0, o qual permitiu constatar uma redução significativa da ação humana nas tarefas, com a exceção dos trabalhos específicos que requerem mão de obra humana. Com base nos resultados obtidos através do método HTA, relativamente ao cenário pósimplementação das ferramentas da Indústria 4.0, foi efetuada uma simulação de avaliação de riscos ergonómicos, mediante a aplicação do método NASA-TLX, que permitiu evidenciar que os conjuntos de tarefas que apresentam menores cargas de trabalho são aqueles que beneficiam do apoio das ferramentas da Indústria 4.0.In order to remain competitive, the Portuguese industry must improve its economy of scale, by increasing its capacity of production and commercialization with effectiveness and efficiency, in order to provide the reduction of direct and indirect costs. To achieve this goal, the Industry 4.0 reveals a special importance to the Portuguese industry, being a notable opportunity to overcome the main competitive barriers. On the other hand, the growth of organizations often may lead to an increase of workrelated accidents, providing consequently the increase of financial charges unnecessarily. Thus, it is pertinent that organizations develop a plan that could promote a sustainable growth in all lines, including a culture for the prevention of occupational risks. Thus, this dissertation aims to propose three optimization models, which are based on three essential action vectors: the organization of productive layouts, whose methodology is based on the application of a genetic algorithm; the planning of tasks, settled on the use of an ant colony optimization algorithm (Ant Colony Optimization - ACO); and the implementation of Industry 4.0 tools suitable for minimizing ergonomic risks, whose approach is based on the development of a fuzzy logic method. The methodology began with an assessment of the general risks related to each work zone characteristic of a wine company, according to the established scenarios. The assessment was based on the William T. Fine method, which allowed to obtain an estimated degree of danger regarding the potential risks in each work zones, followed by an ergonomic risk assessment based on the expenditure of metabolic energy during the execution of tasks, which allowed the quantification of risks inherent to the productive processes’ tasks. Based on the results of the risk assessments performed, the optimization models of production layouts and tasks planning were applied, providing feasible solutions to the scenarios that integrate all operations that takes place during the high season (harvest season), both regarding the production processes operations, and the minimization of general and ergonomic risks. Regarding the scenarios established for the low season, feasible results were obtained regarding the optimization of task planning, having been verified that the results obtained from the optimization model applied to the production layouts, can only be considered as supplementary results, which suggest an infrastructure built on two different levels or floors. Also based on the results of the risk assessments, the application of Industry 4.0 tool selection method to minimize ergonomic risks provided operationally viable solutions, with a strong tendency towards the recommendation of autonomous systems, namely automated integrated systems and autonomous robots. The employ of the HTA (Hierarchical Task Analysis) method provided a detailed description of a scenario that include the implementation of Industry 4.0 tools, which allowed to verify a significant reduction of human action on the tasks, with the exception of specific tasks that require human work. Based on the results obtained through the HTA method, regarding the scenario after the implementation of the Industry 4.0 tools, an ergonomic risk assessment simulation was performed, using the NASA-TLX method, which allowed to demonstrate that the sets of tasks that have the lowest workloads corresponds to those that benefit from the support of Industry 4.0 tools