A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems

Ghiasi, Hossein; Pasini, Damiano; Lessard, Larry

doi:10.1080/03052151003739598

Cited by 50 publications

(17 citation statements)

References 38 publications

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“…A pesar de que en la literatura evaluada (Wang et al, 2010;Ghiasi et al, 2011;Xiong et al, 2012;Nicoara, 2012) y sus antecedentes, los procesos de investigación de los sistemas Job shop que utilizan técnicas fundamentadas en inteligencia artificial para la solución de problemas multiobjetivo se han extendido durante los últimos años; se resalta que las variables analizadas en el tipo de problema multipropósito, objeto de éste artículo, no han sido tratadas previamente por ningún autor. Asimismo, el problema identificado es de interés relevante debido a que se orienta a consolidar la competitividad de las operaciones de una Organización y consecuentemente a garantizar su permanencia y crecimiento en el mercado global.…”

Section: Discusión De Resultadosunclassified

Programación Multiobjetivo en un Sistema de Fabricación Tipo "Contra Existencia" (Job Shop)

2013

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ResumenEn el presente artículo se analiza el desempeño de las variables tiempo de procesamiento, fracción defectuosa causada por la fatiga del operario y costo de mano de obra directa, en un sistema tipo "contra existencia" (job shop). Con el anterior propósito se comparan dos métodos, el primero toma elementos de los algoritmos VEGA y MOGA (VyM), mientras que el segundo corresponde a un método de sumas ponderadas (MSP). Al aplicar las metodologías se encontró que el método VyM presenta mejor desempeño al compararlo con el MSP. Respecto al tiempo de proceso (en horas) el primero supera al segundo en 7.0 %; respecto al tiempo de proceso (en semanas) el primero supera al segundo en 6.7 % y respecto a la fracción defectuosa, el primero supera al segundo en 1,4 %. En cuanto al costo de mano de obra, ambos métodos presentan igual desempeño. Palabras clave: multiobjetivo, algoritmos genéticos, moga, job shop, tiempo de procesamiento Multiobjective Programming in a Manufacturing System of the Job Shop Type AbstractIn 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.

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Section: Discusión De Resultadosunclassified

Programación Multiobjetivo en un Sistema de Fabricación Tipo "Contra Existencia" (Job Shop)

2013

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“…These parameters have been selected based on preliminary investigation and parameter tuning. The NSGAII algorithm was the selected Evolutionary Algorithm because this MOEA is acknowledged as one of the most efficient and most commonly, incorporating several prominent characteristics to speed up the search and the solution space exploration (Ghiasia, Pasinia, & Lessard, 2010;Sayyad & Ammar, 2013).…”

Section: Multi-objective Optimization Kernelmentioning

confidence: 99%

Boosting Trading Strategies performance using VIX indicator together with a dual-objective Evolutionary Computation optimizer

Pinto

Neves

Horta

2015

Expert Systems with Applications

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“…Examples of this approach include ε-MOEA, a proper domination, fuzzy domination (Deb 2008) and quick sort (Zheng et al 2004). The aims of these improvements are to reduce the time to converge to the Pareto front and to reduce the computational complexity, but the subsequent penalty is that part of the real Pareto front may be excluded [31].…”

Section: Nsga-ii With Modified Non-dominated Sortingmentioning

confidence: 99%

“…However, no activity can be tested before its list-predecessor has not been scheduled [30]. ) is a multiobjective evolutionary algorithm that uses non-dominated sorting and a crowded-comparison approach to find a set of evenly distributed solutions to a multiobjective optimization problem [31]. NSGA-II is an improved version of the nondominated sorting genetic algorithm NSGA.…”

Section: Decoding Based On Serial Schedule Schemementioning

confidence: 99%

Time-cost Tradeoff For Optimizing Contractor Npv By Cost Payment And Resource Constraints Using Nsga-ii Algorithm (case Studybandar Abbas Gas Condensate Refinery Project)

Zareei¹,

Hassan-pour²,

Mosadegh-khah³

2014

J. Math. Computer Sci.

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In this paper, a mathematical model is presented for project scheduling with multiple purposes based on considering cost payment and resource constrains and since this this problem is considered as complex optimization in NP-Hard context, in order to solve proposed method from NSGA-II algorithm and the results are compared with GAMS software in some problems. The proposed method is a Converge to the optimum and efficient solution algorithm. Besides this algorithm is used in some parts of refinery project.

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A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems

Cited by 50 publications

References 38 publications

Programación Multiobjetivo en un Sistema de Fabricación Tipo "Contra Existencia" (Job Shop)

Programación Multiobjetivo en un Sistema de Fabricación Tipo "Contra Existencia" (Job Shop)

Boosting Trading Strategies performance using VIX indicator together with a dual-objective Evolutionary Computation optimizer

Time-cost Tradeoff For Optimizing Contractor Npv By Cost Payment And Resource Constraints Using Nsga-ii Algorithm (case Studybandar Abbas Gas Condensate Refinery Project)

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