Bi-objective optimization for equipment system-of-systems development planning using a novel co-evolutionary algorithm based on NSGA-II and HypE

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2024-10-29 DOI:10.1016/j.cie.2024.110677
{"title":"Bi-objective optimization for equipment system-of-systems development planning using a novel co-evolutionary algorithm based on NSGA-II and HypE","authors":"","doi":"10.1016/j.cie.2024.110677","DOIUrl":null,"url":null,"abstract":"<div><div>Previous research on the equipment system-of-systems development planning (ESoSDP) problem has predominantly focused on the portfolio selection of developed equipment (DE) or equipment pending development (EPD), often neglecting the real-world confrontation scenarios and the practical implementation of DE and EPD. Motivated by this gap, we conduct a novel investigation into the ESoSDP problem, integrating the characteristics of systematization, confrontation, and implementation. To address this, we formulate an integer linear programming model aimed at minimizing total expenditures while maximizing operational effectiveness. Specifically, a two-sided equipment system-of-systems (ESoS) confrontation network, encompassing both EPD and DE, is designed to evaluate the operational effectiveness of the ESoS. The associated costs are determined by integrating expenses related to research and development (R&amp;D), procurement, maintenance, and decommissioning activities of both DE and EPD. To address this problem, a co-evolutionary algorithm, named MCEANH, which integrates the NSGA-II and HypE algorithm, is proposed. Within the MCEANH framework, an adaptive crossover-mutation strategy and a knowledge transfer mechanism between NSGA-II and HypE are introduced to enhance its performance. Through a series of comprehensive experiments conducted across nine different solution scales, MCEANH demonstrates superior performance in terms of distribution and convergence when compared to three widely-used multi-objective optimization algorithms, as well as their modified versions incorporating the adaptive crossover-mutation strategy. This study provides essential insights and practical tools for managers of ESOSDP, particularly in light of current trends in systemic confrontation. The research not only contributes to academic discourse but also proposes pragmatic planning schemes for real-world ESoSDP challenges, emphasizing the necessity of integrating real-world confrontation and equipment implementation into ESoSDP.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Industrial Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S036083522400799X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Previous research on the equipment system-of-systems development planning (ESoSDP) problem has predominantly focused on the portfolio selection of developed equipment (DE) or equipment pending development (EPD), often neglecting the real-world confrontation scenarios and the practical implementation of DE and EPD. Motivated by this gap, we conduct a novel investigation into the ESoSDP problem, integrating the characteristics of systematization, confrontation, and implementation. To address this, we formulate an integer linear programming model aimed at minimizing total expenditures while maximizing operational effectiveness. Specifically, a two-sided equipment system-of-systems (ESoS) confrontation network, encompassing both EPD and DE, is designed to evaluate the operational effectiveness of the ESoS. The associated costs are determined by integrating expenses related to research and development (R&D), procurement, maintenance, and decommissioning activities of both DE and EPD. To address this problem, a co-evolutionary algorithm, named MCEANH, which integrates the NSGA-II and HypE algorithm, is proposed. Within the MCEANH framework, an adaptive crossover-mutation strategy and a knowledge transfer mechanism between NSGA-II and HypE are introduced to enhance its performance. Through a series of comprehensive experiments conducted across nine different solution scales, MCEANH demonstrates superior performance in terms of distribution and convergence when compared to three widely-used multi-objective optimization algorithms, as well as their modified versions incorporating the adaptive crossover-mutation strategy. This study provides essential insights and practical tools for managers of ESOSDP, particularly in light of current trends in systemic confrontation. The research not only contributes to academic discourse but also proposes pragmatic planning schemes for real-world ESoSDP challenges, emphasizing the necessity of integrating real-world confrontation and equipment implementation into ESoSDP.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用基于 NSGA-II 和 HypE 的新型协同进化算法进行设备系统开发规划的双目标优化
以往关于装备系统开发规划(ESoSDP)问题的研究主要集中在已开发装备(DE)或待开发装备(EPD)的组合选择上,往往忽视了现实世界中的对抗场景以及 DE 和 EPD 的实际实施。在这一空白的激励下,我们对 ESoSDP 问题进行了新颖的研究,将系统性、对抗性和实施性等特点融为一体。为此,我们制定了一个整数线性规划模型,旨在最大限度地提高运营效率的同时,最大限度地减少总支出。具体来说,我们设计了一个包含 EPD 和 DE 的双面设备系统(ESoS)对抗网络,以评估 ESoS 的运行效果。相关成本是通过整合 DE 和 EPD 的研发(R&D)、采购、维护和退役活动的相关费用确定的。为解决这一问题,我们提出了一种名为 MCEANH 的协同进化算法,它集成了 NSGA-II 和 HypE 算法。在 MCEANH 框架内,引入了自适应交叉突变策略和 NSGA-II 与 HypE 之间的知识转移机制,以提高其性能。通过对九种不同求解尺度进行的一系列综合实验,与三种广泛使用的多目标优化算法及其包含自适应交叉突变策略的改进版本相比,MCEANH 在分布和收敛性方面表现出更优越的性能。这项研究为 ESOSDP 的管理者提供了重要的见解和实用工具,尤其是在当前系统对抗的趋势下。该研究不仅为学术讨论做出了贡献,还针对现实世界中的 ESoSDP 挑战提出了务实的规划方案,强调了将现实世界的对抗和设备实施融入 ESoSDP 的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
期刊最新文献
Joint optimization of opportunistic maintenance and speed control for continuous process manufacturing systems considering stochastic imperfect maintenance Production line location strategy for foreign manufacturer when selling in a market lag behind in manufacturing Bi-objective optimization for equipment system-of-systems development planning using a novel co-evolutionary algorithm based on NSGA-II and HypE Artificial intelligence abnormal driving behavior detection for mitigating traffic accidents Design and strategy selection for quality incentive mechanisms in the public cloud manufacturing model
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1