Luise Müller, Philipp Wanko, Christian Haubelt, Torsten Schaub
{"title":"Investigating Methods for ASPmT-Based Design Space Exploration in Evolutionary Product Design","authors":"Luise Müller, Philipp Wanko, Christian Haubelt, Torsten Schaub","doi":"10.1007/s10766-024-00763-2","DOIUrl":null,"url":null,"abstract":"<p>Nowadays, product development is challenged by increasing system complexity and stringent time-to-market. To handle the demanding market requirements, knowledge from prior product generations is used to derive new, but partially similar product versions. The concept of product generation engineering, hence, allows manufacturers to release high-quality products within short development times. Therefore, in this paper, we propose a novel approach to evaluate the similarity of two product implementations based on the concept of the Hamming distance. This allows the usage of similarity information in various heuristics as well as in strategies and thus, to improve the product design process. In a wide set of cases, we investigate the quality and similarity of design points. In the experiments, the use of strategies leads to significantly short searching times, but also tends to be too restrictive in certain cases. Simultaneously, the quality of the solutions found in the heuristic design space exploration has been shown to be as good or better than for the search from scratch and considerably closer solutions as part of the non-dominated solution front have been found.</p>","PeriodicalId":14313,"journal":{"name":"International Journal of Parallel Programming","volume":"114 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Parallel Programming","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10766-024-00763-2","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Nowadays, product development is challenged by increasing system complexity and stringent time-to-market. To handle the demanding market requirements, knowledge from prior product generations is used to derive new, but partially similar product versions. The concept of product generation engineering, hence, allows manufacturers to release high-quality products within short development times. Therefore, in this paper, we propose a novel approach to evaluate the similarity of two product implementations based on the concept of the Hamming distance. This allows the usage of similarity information in various heuristics as well as in strategies and thus, to improve the product design process. In a wide set of cases, we investigate the quality and similarity of design points. In the experiments, the use of strategies leads to significantly short searching times, but also tends to be too restrictive in certain cases. Simultaneously, the quality of the solutions found in the heuristic design space exploration has been shown to be as good or better than for the search from scratch and considerably closer solutions as part of the non-dominated solution front have been found.
期刊介绍:
International Journal of Parallel Programming is a forum for the publication of peer-reviewed, high-quality original papers in the computer and information sciences, focusing specifically on programming aspects of parallel computing systems. Such systems are characterized by the coexistence over time of multiple coordinated activities. The journal publishes both original research and survey papers. Fields of interest include: linguistic foundations, conceptual frameworks, high-level languages, evaluation methods, implementation techniques, programming support systems, pragmatic considerations, architectural characteristics, software engineering aspects, advances in parallel algorithms, performance studies, and application studies.