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Applying genetic algorithm for hybrid job shop scheduling in a cosmetic industry


Macias, Edgar
Niebles, Fabricio
Jimenez, Genett
Neira Rodado, Dionicio

Artículo de revista

2017

Proceedings of 2017 4th International Conference on Control, Decision and Information Technologies

https://doi.org/DOI: 10.1109/CoDIT.2017.8102732

978-150906465-6

SchedulingBuscar en Repositorio UMECIT
Genetic AlgorithmsBuscar en Repositorio UMECIT
Cosmetic IndustryBuscar en Repositorio UMECIT
Hybrid JobshopBuscar en Repositorio UMECIT

This work considers the problem of scheduling a given set of jobs in a Flexible Job Shop in a cosmetic industry, located in Colombia, taking into account the natural complexity of the process and a lot of amount of variables involved, this problem is considered as NP-hard in the strong sense. Therefore, it is possible to find and optimal solution in a reasonable computational time for only small instances, which in general, does not reflect the industrial reality. For that reason, it is proposed the use of metaheuristics as an alternative approach in order to determine, with a low computational effort, the best assignment of jobs in order to minimize the number of tardy jobs. This optimization objective will allow to company to improve their customer service. A Genetic Algorithm (GA) is proposed. Computational experiments are carried out comparing the proposed approach versus instances of literature by Chiang and Fu. Results show the efficiency of our GA Algorithm.

http://hdl.handle.net/11323/1167

  • Artículos científicos [2641]

Descripción: Applying genetic algorithm for hybrid job shop scheduling.pdf
Título: Applying genetic algorithm for hybrid job shop scheduling.pdf
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