Search

Copyright of Inventum. Ingenieria, Tecnologia e Investigacion is the property of Corporacion Universitaria Minuto de Dios and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main objective of this research study is to propose an optimal oil well intervention program by reassigning the routes of SWAB teams in companies dedicated to these operations. The first phase makes use of the grouping method using k-means to obtain a number of groupings. The second phase uses genetic algorithms (GA) to find the optimal path in each cluster of the first phase and optimize the number of clusters. The intervention time data, the geographical coordinates of each well, and the travel distances were obtained by visiting each of the wells. The results show that the algorithm proposes an optimal program for the case under study, obtaining a saving of S/664 994.06 per year in these operations. In conclusion, algorithm implementation in two phases (clustering and GA) is a viable solution for solving problems in swablike transport planification route processes. [ABSTRACT FROM AUTHOR]

Introduction: The Unequal Area Facility Layout Problem (UA-FLP), is a problem of combinatory optimization no lineal, well known for looking for the best ordination of stations work that possess areas and/or distinct dimensions; recent studios show approximate methods, like metaheuristics, to resolve this type of problems, or in his defect show innovation in the modelacion mathematical of the same, fits to highlight that the effect of the decoders like variable of the problem had not been analyzed until this moment. Objective: Determine if it existed significant difference in the quality of the solution offered by each one of the combinations Metaheuristic-Decoder. Method: They proposed two metaheuristics, a Basic genetic algorithm and an algorithm called N2, to the equal that two decoders, the Decoder in spiral and the Decoder in blower, later realized a simple experiment whose experimental factor was the combination Metaheuristic-Decoder and the dependent variable was the objective function of the problem analyzed. Results: The experimental design showed that the combination, metaheuristic N2 and Decoder in spiral offer better quality results. Conclusions: It exists significant difference in the combination Metaheuristic- Decoder; in specific can affirm that for the problem in question, the metaheuristic N2 is more efficient than the Basic Genetic Algorithm, added to this, also can conclude that the decoders have big influence on the hour to resolve an UA-FLP. [ABSTRACT FROM AUTHOR]

This article presents a new algorithm based on non-dominated sorting genetic algorithm II (NSGA II) for the distribution of perishable products, which minimizes the number of damaged products and the total distance traveled in the delivery of these. Both algorithms were tested in instances of the literature of 50 and 100 clients, to evaluate the quality and efficiency of the same. From the analysis, it was obtained that the proposed algorithm has the capacity to generate better results comparing it with the NSGA II for instances of 50 clients. However, their solutions lose quality when the instances are 100 clients and the complexity of the problem increases. Therefore, the decision makers have now, as a tool, this comparison for the planning of routes of perishable products according to the number of clients. [ABSTRACT FROM AUTHOR]

This article presents a new methodology for the optimal placement of reclosers on electric power distribution systems. The methodology simultaneously considers the installation of reclosers normally closed and open, in order to fault isolation and service restoration, respectively. The problem is described by a multi- objective mathematical model, where the first objective function minimizes the unserved energy level of the system (NENS) and the second one minimizes the investment costs of the project. The set of constraints considers operational criteria of the system. As a solution strategy the concept of operational areas is introduced, which reduce computational effort considering that the NENS by area remains constant. In the problem solution, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed. The results show the validity of the proposed methodology and its applicability to real distribution systems. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the variables: makespan time, defective fraction due to operator fatigue and direct labor cost, in a job shop manufacturing system is presented. Two methods are compared; the first one blends elements from VEGA and MOGA algorithms (V&M), while the second one corresponds to a weighted sums method (WSM). Applying these methodologies, it was found that the method V&M has a better performance than the WSM. Comparing he variable makespan time (in hours) the first is better by 7.0 %; comparing the variable makespan time (in weeks) by 6.7 %; and comparing the variable defective fraction by 1.4 %. Regarding the variable labor costs, both methods have the same performance. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Energía y Computación is the property of Universidad del Valle and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the application of an evolutionary algorithm using Matlab to obtain the optimum main angles of the cutting geometry of a machining tool. The optimization method considered the non linear model of Kaldor and Venuvinod. Different algorithm configurations were tested using data obtained from the literature. Several simulations were carried out to validate the performance of the algorithm and to show the usefulness of the method employed. It is concluded that that the proposed approach shows to be stable and coherent in the results for the cases studied. [ABSTRACT FROM AUTHOR]

This paper described the problem of path-planning in mobile robots in a partially structured environment. The work was divided in three parts: design of a mechatronic system, denominated Robot IO, study of advantages and disadvantages of three path-planning algorithms: neural nets, genetic algorithms and fuzzy logic, and implementation of algorithms that allow to identify the workspace where IO moves. [ABSTRACT FROM AUTHOR]

This work presents a multiobjective optimization algorithm based on a variant of the evolutionary programming field called memetic algorithms (MA). This algorithm was proposed by the authors, combines genetic evolution with local search, in the same way as traditional MA, but with the use of independent populations for each objective, as well as a mechanism to find compromise solutions (tradeoff), where local search is performed restricted by a compromise parameter. The algorithm was applied to a scheduling problem of a molding production process and compared against two multiobjective techniques available in the literature , Multiobjective Genetic Algorithm (MOGA) and Multiobjective Simulated Annealing (MOSA). The results of the proposed approach, based on the experiments performed, outperformed the benchmark techniques based on two objectives of industrial interest, such as, the total completion time (Cmax) and the total tardiness. Both objectives are minimization. This objectives have a direct impact on the process productivity as well as the capability of delivering the goods on time. [ABSTRACT FROM AUTHOR]

This study analyzes the ability of the recursive dynamics multivaried models constructed through genetic algorithms and the recursive neuronal networks to predict the sign of the IPSA's weekly changes. The data correspond to the period between July 14, 1997 and December 9, 2002. The analyzed models were evaluated in 60 series generated by a block-bootstrap process. The results indicate that the network ward would have better forecast ability than the genetic algorithms model and the naive model to predict the sign of the IPSA's changes. The network ward and the genetic algorithms models surpassed, in average, the buy and hold strategy, even when a transaction cost of 0.1% is considered. [ABSTRACT FROM AUTHOR]

The placement of convex objects in the plane, specially rectangular objects has been studied extensively in the literature and its applications to VLSI designs, the steel industry (stock cutting). The placement of non convex objects and its application in many design and textile industries, specially the textile industry has been less studied. Mathematical Approaches determining and adequate solution of such problems include linear programming and the newest genetic algorithms. In this paper we consider a particular industrial problem, the design of printing sheets for decorative porcelain. These objects permit inclusions and other geometric situations which do not appear in the case of convex objects. In this paper we proof results related with this problem for different algorithms: a heuristic algorithm, simulated annealing algorithm and a genetic algorithm. [ABSTRACT FROM AUTHOR]