Transgenic Genetic Algorithm to Minimize the Makespan in the Job Shop Scheduling Problem

Monique Viana, Orides Morandin Junior, Rodrigo Contreras

Abstract

In recent years, several research studies have been conducted that use metaheuristics to calculate approximations of solutions for solving NP-Hard problems, within this class of problems there is the Job Shop Scheduling Problem (JSSP), which is discussed in this study. Improved solutions to problems of this type have been created for metaheuristics in the form of additional operators. For the Genetic Algorithm (GA) the transgenic operator has recently been created, whose operation is based on the idea of "genetically modified organisms", with the proposal to direct some population of individuals to a more favorable solution to the problem without removing the diversity of the population with a competitive cost of time. In this study, our main contribution is an adaptation of the GA with transgenic operator to the JSSP. The results obtained by the proposed method were compared with three papers in the literature that work on the same benchmark: one using GA, one using Adaptive GA and another using Ant Colony Optimization. The results confirm that the GA used with the transgenic operator obtains better results in a competitive processing time in comparison to the other techniques, due to its better targeting in the search space.

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Paper Citation


in Harvard Style

Viana M., Morandin Junior O. and Contreras R. (2020). Transgenic Genetic Algorithm to Minimize the Makespan in the Job Shop Scheduling Problem.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 463-474. DOI: 10.5220/0008937004630474


in Bibtex Style

@conference{icaart20,
author={Monique Viana and Orides Morandin Junior and Rodrigo Contreras},
title={Transgenic Genetic Algorithm to Minimize the Makespan in the Job Shop Scheduling Problem},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={463-474},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008937004630474},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Transgenic Genetic Algorithm to Minimize the Makespan in the Job Shop Scheduling Problem
SN - 978-989-758-395-7
AU - Viana M.
AU - Morandin Junior O.
AU - Contreras R.
PY - 2020
SP - 463
EP - 474
DO - 10.5220/0008937004630474