10 research outputs found

    A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling

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    Identical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.Red de Universidades con Carreras en Informática (RedUNCI

    An ACO approach for the Parallel Machines Scheduling Problem

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    The parallel machines scheduling problem (Pm) comprises the allocation of jobs on the system’s resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve Pm for the minimization Maximum Tardiness (Tmax). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

    A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling

    Get PDF
    Identical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.Red de Universidades con Carreras en Informática (RedUNCI

    An ACO approach for the Parallel Machines Scheduling Problem

    Get PDF
    The parallel machines scheduling problem (Pm) comprises the allocation of jobs on the system’s resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve Pm for the minimization Maximum Tardiness (Tmax). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

    Metaheurísticas basadas en inteligencia computacional aplicadas a la resolución de problemas de optimización numérica con y sin restricciones y optimización combinatoria

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    En esta presentación se describen en forma breve algunas de las direcciones de investigación que en la actualidad se están desarrollando dentro de la línea “Optimización Mono y Multiobjetivo” del Laboratorio de Investigación y Desarrollo en Inteligencia Computacional (LIDIC). Uno de los objetivos de esta línea, es el estudio y desarrollo de metaheurísticas aptas para resolver problemas de optimización numérica y combinatoria. En particular, el énfasis está puesto en las heurísticas de la inteligencia computacional basadas en los paradigmas de inteligencia colectiva y biológicos.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

    A variant of simulated annealing to solve unrestricted identical parallel machine scheduling problems

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    In this paper we propose a modification to the Simulated Annealing (SA) basic algorithm that includes an additional local search cycle after finishing every Metropolis cycle. The added search finishes when it improves the current solution or after a predefined number of tries. We applied the algorithm to minimize the Maximum Tardiness objective for the Unrestricted Parallel Identical Machines Scheduling Problem for which no benchmark have been found in the literature. In previous studies we found, by using Genetic Algorithms, solutions for some adapted instances corresponding to Weighted Tardiness problem taken from the OR-Library. The aim of this work is to find improved solutions (if possible) to be considered as the new benchmark values and make them available to the community interested in scheduling problems. Evidence of the improvement obtained with proposed approach is also provided.XIV Workshop agentes y sistemas inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

    Upgrading evolutionary algorithms through multiplicity for multiobjective optimization in job shop scheduling problems

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    In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization problems (MOOPs) was demonstrated. To test the potential of the novel method when dealing with the Job Shop Scheduling Problem (JSSP), regular and non-regular objectives functions were chosen. They were the makespan and the mean absolute deviation (of job completion times from a common due date, an earliness/tardiness related problem). Diverse representations such as priority list representation (PLR), job-based representation (JBR) and operation-based representation (OBR) among others were implemented and tested. The latter showed to be the best one. As a good parameter setting can enhance the behaviour of an evolutionary algorithm distinct parameters combinations were implemented and their influence studied. Multiple crossovers on multiple parents (MCMP), a powerful multirecombination method showed some enhancement in single objective optimization when compared with MCPC. This paper shows the influence of different recombination schemes when building the Pareto front under OBR and using the best parameter settings determined in previous works on a set of demonstrative Lawrence´s instances. Details of implementation and results are discussed.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

    Metaheurísticas basadas en inteligencia computacional aplicadas a la resolución de problemas de optimización numérica con y sin restricciones y optimización combinatoria

    Get PDF
    En esta presentación se describen en forma breve algunas de las direcciones de investigación que en la actualidad se están desarrollando dentro de la línea “Optimización Mono y Multiobjetivo” del Laboratorio de Investigación y Desarrollo en Inteligencia Computacional (LIDIC). Uno de los objetivos de esta línea, es el estudio y desarrollo de metaheurísticas aptas para resolver problemas de optimización numérica y combinatoria. En particular, el énfasis está puesto en las heurísticas de la inteligencia computacional basadas en los paradigmas de inteligencia colectiva y biológicos.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

    Upgrading evolutionary algorithms through multiplicity for multiobjective optimization in job shop scheduling problems

    Get PDF
    In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization problems (MOOPs) was demonstrated. To test the potential of the novel method when dealing with the Job Shop Scheduling Problem (JSSP), regular and non-regular objectives functions were chosen. They were the makespan and the mean absolute deviation (of job completion times from a common due date, an earliness/tardiness related problem). Diverse representations such as priority list representation (PLR), job-based representation (JBR) and operation-based representation (OBR) among others were implemented and tested. The latter showed to be the best one. As a good parameter setting can enhance the behaviour of an evolutionary algorithm distinct parameters combinations were implemented and their influence studied. Multiple crossovers on multiple parents (MCMP), a powerful multirecombination method showed some enhancement in single objective optimization when compared with MCPC. This paper shows the influence of different recombination schemes when building the Pareto front under OBR and using the best parameter settings determined in previous works on a set of demonstrative Lawrence´s instances. Details of implementation and results are discussed.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

    Comparative study of trajectory metaheuristics for the resolution of scheduling problem of unrestricted parallel identical machines

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    In this paper we present a comparative study of four trajectory metaheuristics or single-solution based metaheuristics (S-meta heuristics): Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP), Variable Neighborhood Search (VNS) and Simulated Annealing (SA). The metaheuristics were used to minimize the Maximum Tardiness (Tmax) for unrestricted parallel identical machine scheduling (Pm) problem, which is considered as NP-Hard problem. The results obtained through experimentation show that SA was the best behaved.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
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