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research-article

Multi-Objective Optimal Design of Variably Constrained 2D Path Network Layouts with Application to Ascent Assembly Engineering

[+] Author and Article Information
Alexandru-Ciprian Zavoianu

Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Altenbergerstraße 69, 4040 Linz, Austria
ciprian.zavoianu@jku.at

Susanne Saminger-Platz

Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Altenbergerstraße 69, 4040 Linz, Austria
susanne.saminger-platz@jku.at

Doris Entner

Design Automation, V-Research GmbH − Industrial Research and Development, Stadtstraße 33, 6850 Dornbirn, Austria
doris.entner@v-research.at

Thorsten Prante

Design Automation, V-Research GmbH − Industrial Research and Development, Stadtstraße 33, 6850 Dornbirn, Austria
thorsten.prante@v-research.at

Michael Hellwig

Research Centre Process and Product Engineering, Vorarlberg University of Applied Sciences, Hochschulstraße 1, 6850 Dornbirn, Austria
michael.hellwig@fhv.at

Martin Schwarz

Technology Management, Liebherr-Werk Nenzing GmbH, Dr.-Hans-Liebherr-Str. 1, 6710 Nenzing, Austria
martin.schwarz@liebherr.com

Klara Fink

Technology Management, Liebherr-Werk Nenzing GmbH, Dr.-Hans-Liebherr-Str. 1, 6710 Nenzing, Austria
klara.fink@liebherr.com

1Corresponding author.

ASME doi:10.1115/1.4039009 History: Received September 09, 2017; Revised January 08, 2018

Abstract

We present an effective optimization strategy that is capable of discovering high-quality cost-optimal solution for 2D path network layouts that, among other applications, can serve as templates for complete ascent assembly structures. The main innovative aspect of our approach is that our aim is not restricted to simply synthesizing optimal assembly designs with regard to a given goal, but we also strive to discover the best trade-offs between geometric and domain-dependent optimal designs. As such, the proposed approach is centered on a variably constrained multi-objective formulation of the optimal design task and on an efficient coevolutionary solver. The results we obtained on both artificial problems and realistic design scenarios based on an industrial test case empirically support the value of our contribution to the field of design automation.

Copyright (c) 2018 by ASME
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