解决双目标灵活作业车间调度问题的混合模拟算法

Saman Nessari , Reza Tavakkoli-Moghaddam , Hessam Bakhshi-Khaniki , Ali Bozorgi-Amiri
{"title":"解决双目标灵活作业车间调度问题的混合模拟算法","authors":"Saman Nessari ,&nbsp;Reza Tavakkoli-Moghaddam ,&nbsp;Hessam Bakhshi-Khaniki ,&nbsp;Ali Bozorgi-Amiri","doi":"10.1016/j.dajour.2024.100485","DOIUrl":null,"url":null,"abstract":"<div><p>The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm’s effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm’s consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm’s faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm’s potential in providing flexible, robust solutions. The proposed algorithm’s unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100485"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000894/pdfft?md5=7212b786690a27bfbd5ce4f691eeda54&pid=1-s2.0-S2772662224000894-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems\",\"authors\":\"Saman Nessari ,&nbsp;Reza Tavakkoli-Moghaddam ,&nbsp;Hessam Bakhshi-Khaniki ,&nbsp;Ali Bozorgi-Amiri\",\"doi\":\"10.1016/j.dajour.2024.100485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm’s effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm’s consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm’s faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm’s potential in providing flexible, robust solutions. The proposed algorithm’s unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments.</p></div>\",\"PeriodicalId\":100357,\"journal\":{\"name\":\"Decision Analytics Journal\",\"volume\":\"11 \",\"pages\":\"Article 100485\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772662224000894/pdfft?md5=7212b786690a27bfbd5ce4f691eeda54&pid=1-s2.0-S2772662224000894-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Analytics Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772662224000894\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Analytics Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772662224000894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

柔性作业车间调度问题(FJSSP)是一个复杂的优化难题,对提高现代制造系统的生产率和效率起着至关重要的作用,其目的是优化将作业分配给一组可变机器的过程。本文介绍了一种解决 FJSSP 的算法,即在不确定条件下最大限度地缩短工期,并最小化总加权提前期和延迟期。这种混合算法将均衡优化器(EO)作为初始解生成器、非支配排序遗传算法 II(NSGA-II)和模拟技术相结合,有效地解决了随机多目标优化的复杂性问题。通过展示具体实例和提供不同不确定性水平下优化调度的决策结果,验证了该算法的有效性。结果表明,与现有方法相比,该算法在管理各种规模的随机参数的复杂性、实现更低的生产周期和提高帕累托前沿质量方面始终保持优势。尤其值得注意的是,该算法收敛速度更快,性能更稳定,这一点通过统计 Wilcoxon 检验得到了验证,证实了该算法在处理动态调度情况时的可靠性和有效性。这些发现凸显了该算法在提供灵活、稳健的解决方案方面的潜力。所提出的算法在仿真框架内兼顾了探索和利用能力,能有效处理 FJSSP 中的不确定性,具有适应各种调度环境的灵活性和定制性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems

A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems

The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm’s effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm’s consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm’s faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm’s potential in providing flexible, robust solutions. The proposed algorithm’s unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.90
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信