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Virtual Factories with Knowledge-Driven Optimization as a New Research Profile
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
2020 (English)In: SPS2020: Proceedings of the Swedish Production Symposium, October 7–8, 2020 / [ed] Kristina Säfsten; Fredrik Elgh, IOS Press, 2020, p. 179-189Conference paper, Published paper (Refereed)
Abstract [en]

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skovde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word 'driven' means 'motivated' or 'manipulated', so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making. 

Place, publisher, year, edition, pages
IOS Press, 2020. p. 179-189
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 13
Keywords [en]
Data Analytics, Decision Support, Digital Manufacturing, Digital Twins, Industry 4.0, Knowledge Discovery, Many-Objective Optimization, Simulations, Virtual Manufacturing, Decision making, Decision support systems, Manufacture, Optimal systems, Decision supports, Design and operations, Intelligent decision support systems, Manufacturing industries, Manufacturing research, Many-objective optimizations, Radical innovation, Sustainable decision makings, Industrial research
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-19398DOI: 10.3233/ATDE200155ISI: 001180173900016Scopus ID: 2-s2.0-85098627699ISBN: 978-1-64368-146-7 (print)ISBN: 978-1-64368-147-4 (electronic)OAI: oai:DiVA.org:his-19398DiVA, id: diva2:1517775
Conference
Swedish Production Symposium, online, Sweden, October 7–8, 2020
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Note

CC BY-NC 4.0

Available from: 2021-01-14 Created: 2021-01-14 Last updated: 2024-05-16Bibliographically approved

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Ng, Amos H. C.Bandaru, Sunith

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