Optimizing Time Utilization of FMS

The aim of the research is to solve the problem of simultaneous production on the flexible manufacturing system with different combination of product types and quantities that will give maximal utilization of production system. The presumption for good utilization of FMS (Flexible Manufacturing System) is in forming of working order with such product type structure that will make possible of production processing with minimal time load of complete production system. Working order structure from the point of product types and quantities is dictated by market demands that are known earlier. Because the structure of particular working order is not harmonized with the exploitation characteristics of FMS, we are faced with problem how to realize working order in such conditions as well as how to achieve main goal: shorter machining cycle with less time occupation of production system. The method based on two phases for solving problem of control working order realization is presented in the work. In the first phase the selection of optimal combination of process plans which gives minimal time load of production system through simultaneous production of different products and their quantities is given. In the second phase the order of part production and the order of particular operations processing is optimized. The optimization problem in both phases of control is solved by application of genetic algorithm approach. The software for computing and optimizing of processing order on FMS is developed

Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement

7 páginas, 7 figuras, 3 tablas.-- El Pdf es la versión post-print de autor.The prediction of the shear capacity of reinforced concrete beams retrofitted in shear by means of externally bonded FRP is very complex as demonstrate the studies carried out up to date. As alternative to the conventional methods two approaches based on artificial intelligence are proposed for the first time. Firstly, the use of neural networks as a means of predicting shear capacity without the need of using complex models and, secondly, the use of genetic algorithms as a means of determining suitably how the shear mechanism works. Predictions obtained with both approaches are compared to experimental values.The writers acknowledge support for the work reported in this paper from the Spanish Ministry of Education and Science (project BIA2007-67790).Peer reviewe

Design equations for reinforced concrete members strengthened in shear with external FRP reinforcement formulated in an evolutionary multi-objective framework

Methods for predicting the shear capacity of FRP shear strengthened RC beams assume the traditional approach of superimposing the contribution of the FRP reinforcing to the contributions from the reinforcing steel and the concrete. These methods become the basis for most guides for the design of externally bonded FRP systems for strengthening concrete structures. The variations among them come from the way they account for the effect of basic shear design parameters on shear capacity. This paper presents a simple method for defining improved equations to calculate the shear capacity of reinforced concrete beams externally shear strengthened with FRP. For the first time, the equations are obtained in a multiobjective optimization framework solved by using genetic algorithms, resulting from considering simultaneously the experimental results of beams with and without FRP external reinforcement. The performance of the new proposed equations is compared to the predictions with some of the current shear design guidelines for strengthening concrete structures using FRPs. The proposed procedure is also reformulated as a constrained optimization problem to provide more conservative shear predictions

Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naïve PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO).The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Çarpımsal Ceza Modeli ile Tamsayılı Programlama

Doğrusal olmayan optimizasyon problemlerinin çözüm yöntemlerinden birisi, kısıtların sağlanmama durumlarında amaç fonksiyonunu olumsuz yönde değiştirecek bir ceza uygulanmasıdır. Çarpımsal ceza modeli, son dönem çalışmalarında henüz yer almakla birlikte, literatürde farklı yaklaşımlara sahip ceza teknikleri de yer almaktadır. Tamsayılı programlama problemlerinin çözümünde kullanılan yöntemler arasında kesme düzlemi yaklaşımları, dal sınır yöntemleri ve evrimsel optimizasyon uygulamaları sayılabilir. Bu çalışma kapsamında ilk kez tamsayılı optimizasyon problemlerinin çözümünde çarpımsal ceza temelli kısıt sağlama yöntemi uygulanmıştır. Yöntem doğrusal olmayan ortak test problemlerinden Himmelblau üzerinde test edilmiş. Yöntemin, problemin kompleksliği ve büyüklüğü karşısındaki davranışı gözlemlenmiştir ve performansı da, diğer yaklaşımlarla karşılaştırmalı biçimde analiz edilmiştir. One of the solution methods for the nonlinear optimization problems is to apply penalty that changes the value of objective function as contrary to optimization direction. Multiplicative penalty method, has been already taken place in the recent studies whereas there can be found other approaches in the literature. Cutting plane approaches, branch and bound methods and evolutionary optimization applications can be regarded as essential solutions to integer programming problems. In scope of this study it is first time that the multiplicative penalty approach is used for integer programming as a new constraint handling method. The method is tested on the Himmelblau's problem which is one of the common test problems in the nonlinear optimization area. Method was also tested against complexity and size of the problem in terms of its performance as compared to existing methods' results

Application of an Evolutionary Algorithm to Reduce the Cost of Strengthening of Timber Beams

The present paper describes the application of an evolutionary algorithm to the optimum design of the reinforcement of timber beams using FRP laminates and sheets. The objective function is the material cost of the strengthening and is subjected to ten constraints derived from the ultimate limit states for flexural and shear behaviour as well as the serviceability limit states. A genetic algorithm is used and the optimization problem is transformed into an unconstrained one by means of an adaptive penalty function. The design variables are the CFRP and GFRP mechanical properties and dimensions and they are encoded in a binary chromosome: type of composite material (CFRP or GFRP), reinforcement mechanical properties and geometric configuration. The search space for the minimum cost consists of 65 billion possible solutions. The crossover operator switches randomly between a fenotype crossover and flat crossover. An adaptive mutation scheme has been as well as an elitism criterion. The algorithm has been used for obtaining optimum designs in several specific load and geometry cases of glued laminated timber beams. The objective is finding whether there are specific reinforcement configurations more feasible for a certain loading situations: short or long beams and lower or higher loading increments. Five cases have been analysed. In the first three cases the length of the beams has constant values of 2, 2.5 and 3 m, whereas the value of loading was variable. In the latter case, the value of the load was fixed and the length of the beam was variable. The analysis of the results shows that the GFRP reinforcement is more efficient than CFRP for designs governed by shear failure, whereas CFRP is more effective in the case of flexural failure and deflection controlled strengthening of timber beams.This work was partially financed by the University of Alicante by means of the GRE12-04 Research Project and Generalitat Valenciana, grant GV/2014/079

Application of Genetic Algorithm in Multi-objective Optimization of an Indeterminate Structure with Discontinuous Space for Support Locations

In this thesis, an indeterminate structure was developed with multiple competing objectives including the equalization of the load distribution among the supports while maximizing the stability of the structure. Two different coding algorithms named “Continuous Method” and “Discretized Method” were used to solve the optimal support locations using Genetic Algorithms (GAs). In continuous method, a continuous solution space was considered to find optimal support locations. The failure of this method to stick to the acceptable optimal solution led towards the development of the second method. The latter approach divided the solution space into rectangular grids, and GAs acted on the index number of the nodal points to converge to the optimality. The average value of the objective function in the discretized method was found to be 0.147 which was almost onethird of that obtained by the continuous method. The comparison based on individual components of the objective function also proved that the proposed method outperformed the continuous method. The discretized method also showed faster convergence to the optima. Three circular discontinuities were added to the structure to make it more realistic and three different penalty functions named flat, linear and non-linear penalty were used to handle the constraints. The performance of the two methods was observed with the penalty functions while increasing the radius of the circles by 25% and 50% which showed no significant difference. Later, the discretized method was coded to eliminate the discontinuous area from the solution space which made the application of the penalty functions redundant. A paired t-test (α=5%) showed no statistical difference between these two methods. Finally, to make the proposed method compatible with irregular shaped discontinuous areas, “FEA Integrated Coded Discretized Method (FEAICDM)” was developed. The manual elimination of the infeasible areas from the candidate surface was replaced by the nodal points of the mesh generated by Solid Works. A paired t-test (α=5%) showed no statistical difference between these two methods. Though FEAICDM was applied only to a class of problem, it can be concluded that FEAICDM is more robust and efficient than the continuous method for a class of constrained optimization problem