Examinando por Materia "GRASP"
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Ítem GRASP Algorithm to Minimize Makespan in a No-Wait Flow Shop with Batch Processing Machines(Inst of Industrial Engineers; Cdr edition, 2012-05-19) Muthuswamy; Shanthi; Mario C. Vélez-Gallego; Maya; Jairo; Rojas; Miguel; Universidad EAFIT. Departamento de Ingeniería de Producción; Gestión de Producción y LogísticaGiven a set of jobs and two Batch Processing Machines (BPMs) in a flow shop environmentÍtem A Metaheuristic Approach for the Cumulative Capacitated Arc Routing Problem(Springer Verlag, 2018-09-03) Rivera, Juan Carlos; Lenis, Sergio Andres; Rivera, Juan Carlos; Lenis, Sergio Andres; Universidad EAFIT. Departamento de Ciencias; Modelado MatemáticoIn this paper we propose a new variant of the capacitated arc routing problem (CARP). In this new problem the objective function becomes a cumulative objective computed as the traveled distance multiplied by the vehicle load. A metaheuristic approach is proposed which is based on the hybridization of three known procedures: GRASP, VND and Set covering model. The metaheuristic is tested with some benchmark instances from CARP. The results allow to evaluate the performance with the different metaheuristic components and to compare the solutions with the classical objective function. © 2018, Springer Nature Switzerland AG.Ítem A Metaheuristic Approach for the Cumulative Capacitated Arc Routing Problem(Springer Verlag, 2018-09-03) Rivera, Juan Carlos; Lenis, Sergio Andres; Rivera, Juan Carlos; Lenis, Sergio Andres; Universidad EAFIT. Departamento de Ciencias; Matemáticas y AplicacionesIn this paper we propose a new variant of the capacitated arc routing problem (CARP). In this new problem the objective function becomes a cumulative objective computed as the traveled distance multiplied by the vehicle load. A metaheuristic approach is proposed which is based on the hybridization of three known procedures: GRASP, VND and Set covering model. The metaheuristic is tested with some benchmark instances from CARP. The results allow to evaluate the performance with the different metaheuristic components and to compare the solutions with the classical objective function. © 2018, Springer Nature Switzerland AG.