An Improved NSGA-II Method for Solving Multi-objective Flexible Job-shop Scheduling Problems

An Improved NSGA-II Method for Solving Multi-objective Flexible Job-shop Scheduling Problems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2025 V1 Latest version Share on An Improved NSGA-II Method for Solving Multi-objective Flexible Job-shop Scheduling Problems Authors : MingJiang , Hanxi Wei [email protected] , and Dongpeng Peng Authors Info & Affiliations https://doi.org/10.22541/au.173866340.01094017/v1 193 views 73 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper addresses the multi-objective optimization problem in flexible job-shop scheduling, es-tablishing a model that optimizes completion time, total machine load, and energy consumption. An improved version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed. This algorithm is designed with multi-objective optimization in mind, featuring a population initializa-tion method for multiple objective functions that enhance the quality and diversity of the population. It also introduces an adaptive crossover and mutation operator, incorporating evaluation during the crossover and mutation process to raise the quality of the offspring. An experience-based im-proved elite preservation strategy has been designed to prevent the reduction of population diver-sity in the later stages of evolution while protecting high-quality individuals from degradation during the genetic process. The results demonstrate that the advantages of this algorithm can more effectively solve the multi-objective flexible job-shop scheduling problem. Supplementary Material File (an improved nsga-ii method for solving multi-objective flexible job-shop scheduling problems.docx) Download 659.76 KB Information & Authors Information Version history V1 Version 1 04 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords improved elite preservation strategy multi-objective function population initialization nsga-ii algorithm Authors Affiliations MingJiang Fujian University of Technology View all articles by this author Hanxi Wei [email protected] Fujian University of Technology View all articles by this author Dongpeng Peng Fujian University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 193 views 73 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation MingJiang, Hanxi Wei, Dongpeng Peng. An Improved NSGA-II Method for Solving Multi-objective Flexible Job-shop Scheduling Problems. Authorea . 04 February 2025. DOI: https://doi.org/10.22541/au.173866340.01094017/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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