jueves, 19 de julio de 2018

Ficha del recurso:

Fuente:

Vínculo original en ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH, 29 (2):1417-1430; JUL 2012
Akgobek, O

Última actualización:

jueves, 28 de junio de 2012

Entrada en el observatorio:

jueves, 28 de junio de 2012

Idioma:

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A comparative study of genetic algorithm and simulated annealing for solving the operational fixed job scheduling problems

In the operational fixed job scheduling problems (OFJS) all jobs have fixed ready times and deadlines on identical parallel machines. There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems. We developed two meta heuristic methods like Genetic Algorithm (GA) and Simulated Annealing (SA) to solve these problems. The objective is to select a set of jobs for processing so as to maximize the total weight. The efficiency and effectiveness of a GA and SA depends on their control parameters. Firstly we searched the best values for control parameters and solved the OFJS problems on identical parallel machines with these parameters by GA and SA. Our computational results for the OFJS problems on identical parallel machines clearly indicate that the GA methods significantly outperform the SA.