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Projeto de investigação
Collaborative Platform based on Data Normalization Techniques and Dynamic Fuzzy Multi-criteria approaches for Integrated Manufacturing and Maintenance Information Processing
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Bio-inspired multi-objective algorithms applied on production scheduling problems
Publication . Azevedo, Beatriz Flamia; Vega, Rubén; Varela, Maria Leonilde Rocha; Pereira, Ana I.
Production scheduling is a crucial task in the manufacturing process. In this way, the managers must decide
the job's production schedule. However, this task is not simple, often requiring complex software tools and
specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was
developed to explore production scheduling performance measures to help managers in decision-making
related to job attribution under three simulations of parallel machine scenarios. Five important production
scheduling performance measures were considered (makespan, tardiness and earliness times, number of
tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three
multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization,
Multi-objective Grey Wolf Algorithm, and, Non-dominated Sorting Genetic Algorithm II), and the set of
optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating
a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analysed through
an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated
that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed
methodology provided more robust solutions by combining different bio-inspired multi-objective
techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results
and support for choosing the final optimum solution.
Contributions to accelerating a numerical simulation of free flow parallel to a porous plane
Publication . Schepke, Claudio; Spigolon, Roberta A.; Rufino, José; Cristaldo, Cesar F. Da C.; Pizzolato, Glener L.
Flow models over flat p orous surfaces have applications in natural processes, such as material, food, chemical processing, or mountain mudflow simulations. The development
of simplified a nalytical or numerical models can predict characteristics such as velocity, pressure, deviation length, and even temperature of such flows for geophysical and engineering purposes. In this context, there is considerable interest in theoretical and experimental models. Mathematical models to represent such phenomena for fluid mechanics have continuously been developed and implemented. Given this, we propose a mathematical and simulation model to describe a free-flowing flow pa rallel toa
porous material and its transition zone. The objective of the application is to analyze the influence o f t he p orous matrix on the flow u nder d ifferent m atrix p roperties. W e i mplement a Computational Fluid Dynamics scheme using the Finite Volume Method to simulate and calculate the numerical solutions for case studies. However, computational applications of this type demand high performance, requiring parallel execution techniques. Due to this, it is necessary to modify the sequential version of the code. So, we propose a methodology describing the steps required to adapt and improve the code. This approach decreases 5.3% the execution time of the sequential version of the code. Next,
we adopt OpenMP for parallel versions and instantiate parallel code flows and executions on multi-core. We get a speedup of 10.4 by using 12 threads. The paper provides simulations that offer the correct understanding, modeling, and construction of abrupt transitions between free flow a nd porous media. The process presented here could expand to the simulations of other porous media problems. Furthermore, customized simulations require little processing time, thanks to parallel processing.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
EXPL/EME-SIS/1224/2021