A Hybrid Search Using Genetic Algorithms and Random-Restart Hill-Climbing for Flexible Job Shop Scheduling Instances with High Flexibility

A Hybrid Search Using Genetic Algorithms and Random-Restart Hill-Climbing for Flexible Job Shop Scheduling Instances with High Flexibility

This work presents a novel hybrid algorithm called GA-RRHC based on genetic algorithms (GAs) and a random-restart hill-climbing (RRHC) algorithm for the optimization of the flexible job shop scheduling problem (FJSSP) with high flexibility (where every operation can be completed by a high number of...

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Bibliographic Details
Main Authors: Nayeli Jazmin Escamilla-Serna, Juan Carlos Seck-Tuoh-Mora, Joselito Medina-Marin, Irving Barragan-Vite, José Ramón Corona-Armenta
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Applied Sciences
Subjects:
flexible job shop scheduling instances
genetic operators
local search methods
cellular automata
Online Access:https://www.mdpi.com/2076-3417/12/16/8050

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https://www.mdpi.com/2076-3417/12/16/8050

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