Exploring a circular economy solution space A comparative study to develop automated optimisation workflows supported by machine learning for circular design problems

Embedding circular economy (CE) principles in early design requires iterative evaluation across multiple lifecycle phases, with trade-offs between objectives complicating the identification of best solutions. This paper puts forward methods to automatically discover diverse, yet well-performing solution types within complex multi-objective CE design optimisation models. Working with a parametric model derived from a furniture design for CE case study, a comparison is made between weighted-sum single objective optimisation and multi-objective optimisation augmented with clustered solution types targeted by the reference point-based NSGA-II optimisation algorithm. Efficiency of optimisation, quality of results and distinctiveness of solution types presented by each method is compared in an effort to understand which will best assist designers to manage complexity in CE design. The generalisability of the presented methods to larger scale CE design problems is discussed and future areas of work on computational design for CE are extrapolated from the presented results.