Victor Yaurima - Academia.edu (original) (raw)

Papers by Victor Yaurima

Proceedings of the 7th …, 2007

This paper presents a genetic algorithm for a scheduling problem frequent in printed circuit boar... more This paper presents a genetic algorithm for a scheduling problem frequent in printed circuit board manufacturing: a hybrid flowshop with unrelated machines, sequence dependent setup time and machine availability constraints. The proposed genetic ...

"Algorithms for Scheduling Problems, 2018

We address a scheduling problem in an actual environment of the tortilla industry. Since the prob... more We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

We address a scheduling problem in an actual environment of the tortilla industry. Since the prob... more We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Gri... more This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. In this environment, one of the big challenges is to provide a job allocation that allows more efficient use of resources and user satisfaction. In general, the criteria that help achieve these goals are often in conflict. To solve this problem, two-objective genetic algorithm is proposed. We conduct comparative analysis of five crossover and three mutation operators , and determine most influential parameters and operators. To this end multi factorial analysis of variance is applied.

El presente trabajo se enfoca en el problema de calendarización multi-criterio de trabajos parale... more El presente trabajo se enfoca en el problema de calendarización multi-criterio de trabajos paralelos en un Grid computacional jerárquico con dos niveles. En el primer nivel se realiza una asignación de trabajos a recursos. En el segundo nivel, cada recurso aplica una estrategia de calendarización local. Se propone un algoritmo genético como estrategia de asignación en el primer nivel. Se adopta un método de agregación de criterios. Dicho método toma en cuenta las preferencias de los participantes en el proceso de calendarización. Se presenta un análisis experimental con base en cargas de trabajos reales. Los resultados se comparan con resultados de estrategias de calendarización conocidas en la literatura.

This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Grid ... more This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. In this environment, one of the big challenges is to provide a job allocation that allows more efficient use of resources and user satisfaction. In general, the criteria that help achieve these goals are often in conflict. To solve this problem, two-objective genetic algorithm is proposed. We conduct comparative analysis of five crossover and three mutation operators, and determine most influential parameters and operators. To this end multi factorial analysis of variance is applied.

Future Manufacturing Systems, 2010

Production Scheduling, 2012

Computers & Industrial Engineering, 2009

This paper presents a genetic algorithm for an important production scheduling problem. Since the... more This paper presents a genetic algorithm for an important production scheduling problem. Since the problem is NP-hard, we focus on suboptimal scheduling solutions for the hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints, and limited buffers. The production environment of a television assembly line for inserting electronic components is considered. The proposed genetic algorithm is a modified and extended version of the algorithm for a problem without limited buffers. It takes into account additional limited buffer constraints and uses a new crossover operator and stopping criteria. Experimental results carried out on real production settings show an improvement in scheduling when the proposed algorithm is used.

Research in Computing Science, Vol. 63, pp. 89-99. 2013, ISSN 1870-4069

Este artículo aborda la calendarización de trabajos paralelos en un Grid jerárquico con dos etapa... more Este artículo aborda la calendarización de trabajos paralelos en un Grid jerárquico con dos etapas. En esta configuración uno de los grandes retos es asignar las tareas de manera que permita un uso eficiente de los recursos, al mismo tiempo que satisface otros criterios. En general, los criterios de optimización a menudo están en conflicto. Para resolver este problema, proponemos un algoritmo genético bi-objetivo y presentamos un estudio experimental de seis operadores de cruzamientos, y tres operadores de mutación. Se determinan los parámetros más influyentes a través de un análisis estadístico de varianza multifactorial y comparamos nuestra propuesta con cinco estrategias de asignación conocidas en la literatura.

Proceedings of the 7th …, 2007

This paper presents a genetic algorithm for a scheduling problem frequent in printed circuit boar... more This paper presents a genetic algorithm for a scheduling problem frequent in printed circuit board manufacturing: a hybrid flowshop with unrelated machines, sequence dependent setup time and machine availability constraints. The proposed genetic ...

"Algorithms for Scheduling Problems, 2018

We address a scheduling problem in an actual environment of the tortilla industry. Since the prob... more We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

We address a scheduling problem in an actual environment of the tortilla industry. Since the prob... more We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Gri... more This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. In this environment, one of the big challenges is to provide a job allocation that allows more efficient use of resources and user satisfaction. In general, the criteria that help achieve these goals are often in conflict. To solve this problem, two-objective genetic algorithm is proposed. We conduct comparative analysis of five crossover and three mutation operators , and determine most influential parameters and operators. To this end multi factorial analysis of variance is applied.

El presente trabajo se enfoca en el problema de calendarización multi-criterio de trabajos parale... more El presente trabajo se enfoca en el problema de calendarización multi-criterio de trabajos paralelos en un Grid computacional jerárquico con dos niveles. En el primer nivel se realiza una asignación de trabajos a recursos. En el segundo nivel, cada recurso aplica una estrategia de calendarización local. Se propone un algoritmo genético como estrategia de asignación en el primer nivel. Se adopta un método de agregación de criterios. Dicho método toma en cuenta las preferencias de los participantes en el proceso de calendarización. Se presenta un análisis experimental con base en cargas de trabajos reales. Los resultados se comparan con resultados de estrategias de calendarización conocidas en la literatura.

This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Grid ... more This paper addresses non-preemptive offline scheduling parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. In this environment, one of the big challenges is to provide a job allocation that allows more efficient use of resources and user satisfaction. In general, the criteria that help achieve these goals are often in conflict. To solve this problem, two-objective genetic algorithm is proposed. We conduct comparative analysis of five crossover and three mutation operators, and determine most influential parameters and operators. To this end multi factorial analysis of variance is applied.

Future Manufacturing Systems, 2010

Production Scheduling, 2012

Computers & Industrial Engineering, 2009

This paper presents a genetic algorithm for an important production scheduling problem. Since the... more This paper presents a genetic algorithm for an important production scheduling problem. Since the problem is NP-hard, we focus on suboptimal scheduling solutions for the hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints, and limited buffers. The production environment of a television assembly line for inserting electronic components is considered. The proposed genetic algorithm is a modified and extended version of the algorithm for a problem without limited buffers. It takes into account additional limited buffer constraints and uses a new crossover operator and stopping criteria. Experimental results carried out on real production settings show an improvement in scheduling when the proposed algorithm is used.

Research in Computing Science, Vol. 63, pp. 89-99. 2013, ISSN 1870-4069

Este artículo aborda la calendarización de trabajos paralelos en un Grid jerárquico con dos etapa... more Este artículo aborda la calendarización de trabajos paralelos en un Grid jerárquico con dos etapas. En esta configuración uno de los grandes retos es asignar las tareas de manera que permita un uso eficiente de los recursos, al mismo tiempo que satisface otros criterios. En general, los criterios de optimización a menudo están en conflicto. Para resolver este problema, proponemos un algoritmo genético bi-objetivo y presentamos un estudio experimental de seis operadores de cruzamientos, y tres operadores de mutación. Se determinan los parámetros más influyentes a través de un análisis estadístico de varianza multifactorial y comparamos nuestra propuesta con cinco estrategias de asignación conocidas en la literatura.