Open AccessAn improved genetic algorithm for low carbon dynamic scheduling in a discrete manufacturing workshop
Author(s) -
Lei Nie,
Xiaogang Wang,
Yuewei Bai
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1820/1/012111
Subject(s) - crossover , scheduling (production processes) , computer science , dynamic priority scheduling , genetic algorithm , mathematical optimization , population , production (economics) , genetic algorithm scheduling , schedule , flow shop scheduling , mathematics , artificial intelligence , demography , sociology , economics , macroeconomics , operating system
Due to energy consumption activities, manufacturing enterprises produce many carbon dioxide emissions in the production process, which exacerbates global climate deterioration. The production scheduling optimization method is an effective way to reduce carbon emissions and relieve environmental pressure. The paper proposed a low-carbon dynamic scheduling optimization method to solve machine failure interference and to minimize the total cost of production and carbon emissions in the discrete manufacturing workshop. The rolling window mechanism driven by abnormal events and rescheduling strategy are used to update the original schedule in real-time when the machine fails. In the carbon emission measurement method, the machine’s carbon emission parameters in different states are considered. The traditional genetic algorithm is improved in the initial population strategy and crossover operator. The experimental results show that the proposed low-carbon dynamic scheduling method based on the improved genetic algorithm can effectively reduce carbon emissions under the premise of ensuring the completion of production tasks as soon as possible.