OTIMIZAÇÃO COM ALGORITMO BIO-INSPIRADO DE CONTROLE DE TRÁFEGO EM SISTEMAS DE GRUPOS DE ELEVADORES

Resumo. Este artigo tem como objetivo apresentar a implementação de uma técnica de otimização bioinspirada como solução ao problema de controle de tráfego em sistemas de grupos de elevadores (EGCS). A técnica de controle usada é o algoritmo de otimização por inteligência de enxame (PSO - swarm optimization particle) de tipo binário. A ideia é que o algoritmo escolha o melhor elevador para um usuário que faz uma chamada de serviço em umsistema de controle destino (DCS ”“ destination control system). Para a escolha do elevador o algoritmo tem uma função custo que considera as variáveis: (1) tempo de espera; (2) tempo de voo; (3) capacidade do elevador; (4) número de paradas alocadas; (5) número de paradas (baseado nas chamadas que são asignadas) para cada elevador. Estes parâmetros são ponderados de acordo com sua importância e inferência na seleção do melhor elevador. Assim, o sistema seleciona de todas as possíveis soluções encontradas a solução que apresenteo melhor valor de aptidão (a solução representa o elevador ou os elevadores selecionado para atender a atual chamada)

Querying histories of organisation simulations

Industrial Dynamics involves system modelling, simulation and evaluation leading to policy making. Traditional approaches to industrial dynamics use expert knowledge to build top-down models that have been criticised as not taking into account the adaptability and sociotechnical features of modern organisations. Furthermore, such models require a-priori knowledge of policy-making theorems. This paper advances recent research on bottom-up agent-based organisational modelling for Industrial Dynamics by presenting a framework where simulations produce histories that can be used to establish a range of policy-based theorems. The framework is presented and evaluated using a case study that has been implemented using a toolset called ES

Querying histories of organisation simulations

Industrial Dynamics involves system modelling, simulation and evaluation leading to policy making. Traditional approaches to industrial dynamics use expert knowledge to build top-down models that have been criticised as not taking into account the adaptability and sociotechnical features of modern organisations. Furthermore, such models require a-priori knowledge of policy-making theorems. This paper advances recent research on bottom-up agent-based organisational modelling for Industrial Dynamics by presenting a framework where simulations produce histories that can be used to establish a range of policy-based theorems. The framework is presented and evaluated using a case study that has been implemented using a toolset called ES

Design Simulation and Analysis of Manual Block-Making Machine

This paper reports the synopsis of design simulation and analysis of manual block-making machine; it is a small fraction of a bigger research-study. Analysis of the various components that make up the final design was done in order to establish the forces, stresses and dimensions. The studies included dynamic simulation, frame analysis and stress examination. Finite Element Analysis was conducted on the components that could have failed during the normal operation of the machine; as such two analyses were done, one to investigate the effect of member components weight due to gravity and the second to investigate the effect of the return load on the frame members. All drawings, calculations, design, assembly, simulations, and Finite Element Analysis (FEA) were done by the Autodesk Inventor, 2016 Engineering Design Software. The results are: Flat lever analysis: The maximum displacement achieved was 0.0000008642mm, while the safety factor was 15ul and so the design of the flat component was acceptable if subjected to loads as exposed. However, it seems that the part is overdesigned. Compactor frame analysis: The maximum displacement was 1.605mm and considering the fixed-end would not actually be fixed in the actual machine, this displacement was found to be acceptable. The minimum safety factor achieved was 4.35ul which is acceptable and the maximum 15 ul. Complete assembly analysis: Maximum contact pressure achieved was 36.72 MPa while some components received no contact pressure from the load. The safety factor for the whole machine was 15ul. The value may lead to an assumption that the machine has been “overdesigned”, but considering that some of the sections of the machine are actually unaffected by the load, or the loading conditions is short, and allowing for that it is an equipment that intends to operate on a daily-basis, and it is made of ductile-material, operated in repeated and impact mode of loading, environmental considerations and also to account for all the unpredictable-factors, then, this safety factor for the machine is acceptable. For single components however there would be a need to reduce the “overdesigning”. The study accomplished design simulation and analysis of manual block-making machine, resulting in 3D-view of the final assembly of the machine (made of mild steel) with all standard notations. Overall, the results of this concise study are rather positive, providing a good starting point for further and much- deeper exploration on the same. The major recommendation was made vis-à-vis design factor of safety, in order to eliminate/reduce “overdesigning” and to obtain sequential solutions that exhibit asymptotic convergence to values representing the exact solution, it is recommended to conduct h-refinement of the mesh in FEA. Keywords: design, simulation, assembly, analysis, block-making-machin

Transporting Robotic Swarms via Mean-Field Feedback Control

With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for deploying a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations