{"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&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.
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.