Método híbrido para optimizar el Flexible Job Shop Scheduling Problem

Nayeli J. Escamilla-Serna; Juan C. Seck-Tuoh-Mora; Joselito Medina-Marín; Irving Barragan-Vite; José R. Corona-Armenta

doi:10.29057/icbi.v10iEspecial2.8651

Nayeli J. Escamilla-Serna Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-8231-8682
Juan C. Seck-Tuoh-Mora Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-3678-1120
Joselito Medina-Marín Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-0937-8707
Irving Barragan-Vite Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-3625-6943
José R. Corona-Armenta Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0001-7157-1634

DOI: https://doi.org/10.29057/icbi.v10iEspecial2.8651

Keywords: • Flexible Job Shop Scheduling Problem, genetic algorithms, hill climbing, hybrid optimization, makespan

Abstract

This article addresses task scheduling in the Flexible Job Shop Scheduling Problem (FJSSP). In this manufacturing system, it is necessary to intensify the number of jobs to be processed due to the current conditions of the industrial sector where there is an increase in the demand for products, which leads to an increase in production. To find a task schedule close to the optimum. A hybrid optimization method is proposed using a global search based on genetic algorithms (GA) that have good diversification. A restart hill-climbing process (RHC) is used as a local search method in order to improve each solution. These metaheuristics yield the equilibrium necessary to find the best solution that minimizes the makespan as a cost function. The proposed algorithm was implemented in Matlab, and the results were compared with recently published research to review its efficiency.

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