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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/4139

Title: Neighborhood structures for scheduling problems with additional resource types
Author(s): Mencía Cascallana, Raúl
Advisor: Varela Arias, José Ramiro
Rodríguez Vela, María del Camino
Keywords: Metaheuristics
Job Shop Scheduling Problem With Operators
Total Flow Time
Genetic Algorithms
Memetic Algorithms
Local Search
Issue date: 2012
Series/Report no.: Máster Universitario en Soft Computing y Análisis Inteligente de Datos
Abstract: The job shop scheduling is a challenging problem that has interested to researchers in the fields of Artificial Intelligence and Metaheuristics over the last decades. In this project, we face the job shop scheduling problem with an additional resource type (operators). This is a variant of the problem, which has been proposed recently in the literature. We start from a genetic algorithm that has been proposed previously to solve this problem and improve it in two different ways. Firstly, we introduce a modification in the schedule generation scheme in order to control the time of inactivity of the machines. Secondly we define a number of neighbourhood structures that are then incorporated in a memetic algorithm. In order to evaluate the proposed strategies, we have conducted an experimental study across a benchmark derived from a set of hard instances of the classic job shop problem.
URI: http://hdl.handle.net/10651/4139
Appears in Collections:Trabajos Fin de Máster

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MasterProject.pdfMemoria del proyecto735,06 kBAdobe PDFView/Open
TFM_Raúl Mencía Cascallana.pdf733,3 kBAdobe PDFView/Open

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