Optimizing Design Circularity: Managing Complexity In Design For Circular Economy Through Single And Multi-Objective Optimisation.

F. Peter Ortner Singapore University of Technology and Design
Jing Zhi Tay Singapore University of Technology and Design

Design for circular economy is an increasingly urgent concern for built environment and product design, with recent calls to action from the United Nations Environment Programme among other international agencies.(UNEP, 2019) Currently many frameworks, strategies and metrics exist for circular economy design, without a single comprehensive or universally accepted method.(Saidani, 2019b; Saidani, 2020) Furthermore, design evaluation for circularity requires quantitative evaluation of many criteria, making precise evaluation for many design variants a time-consuming process. To support designers in identifying better design options for circular economy, computational tools are needed to automate quantitative evaluation of circularity metrics, and support search among many options to find designs which demonstrate the highest circularity performance while meeting subjective concerns of the designer. Responding to this need is circular design optimization, an area of research that asks how computational tools may support search for optimal circular designs and management of complexity of circular design process. No definitive methodology for optimizing a design for circularity currently exists, with the choice between multi-objective (MOO) and scalarized single-objective optimization (SOO) representing a prominent open question. While the many criteria required to assess a design’s circularity suggest that MOO may be most appropriate, the computational cost of MOO in comparison SOO provides a counter-argument against this default position. (Wortman, 2020) Furthermore, prominent circular design frameworks rank their circularity criteria according to hierarchies of impact, suggesting that scalarized SOO with weights ranked according to these hierarchies may offer a more nuanced and complete assessment of the objective space. (Reike et al., 2018, Potting et. al, 2017) In this paper we demonstrate two methods for optimizing circularity of a parametric model based on multiple circularity objectives, using both MOO and scalarized SOO. We apply these methods to a case study of on an actually-existing furniture line designed for circularity. We compare results from each method based on computational cost (speed of convergence), and ability to support understanding of the complexity of circular design problems, specifically through the generation of well-defined pareto sets for pairs of conflicting objectives. Building from our results we reflect on future work for computational optimization of circular design with reference to a wider variety of problem types in product design and design of the built environment. References: Chiandussi G, Codegone M, Ferrero S, Varesio FE. Comparison of multi-objective optimization methodologies for engineering applications. 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Keywords: Circular Economy, Computational Design, Design Optimization, Design Process,

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