Assisting decision-makers select multi-dimensionally efficient infrastructure designs - Application to urban drainage systems

Omid Seyedashraf; Andrea Bottacin-Busolin; Julien J Harou

doi:10.1016/j.jenvman.2023.117689

Assisting decision-makers select multi-dimensionally efficient infrastructure designs - Application to urban drainage systems

J Environ Manage. 2023 Jun 15:336:117689. doi: 10.1016/j.jenvman.2023.117689. Epub 2023 Mar 14.

Authors

Omid Seyedashraf¹, Andrea Bottacin-Busolin², Julien J Harou³

Affiliations

¹ Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M13 9PL, Manchester, UK; Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran.
² Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M13 9PL, Manchester, UK; Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121, Padova, Italy. Electronic address: andrea.bottacinbusolin@unipd.it.
³ Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M13 9PL, Manchester, UK; Department of Civil, Environmental & Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK.

PMID: 36924710
DOI: 10.1016/j.jenvman.2023.117689

Abstract

Multi-objective design approaches can help identify future infrastructure system designs that appropriately balance different engineering, environmental, and other societal goals. Planners benefit from assessing the trade-offs implied by the best-performing infrastructure system solutions. However, a large number of possible efficient system designs, obtained when using multi-objective optimization, can be overwhelming to interpret. This study attempts to aid decision-making in multi-criteria infrastructure system design by reducing the complexity of the identified set of efficient infrastructure designs, i.e., the Pareto-front. A soft clustering algorithm is applied, which identifies similarities between solutions, partitions the front accordingly, and selects a set of representative solutions while preserving the multi-dimensional structure of the solutions on the efficiency frontier. Three post-optimization decision-making metrics are introduced to help quantify the overall performance of the Pareto-optimal designs to further summarize design process outputs for decision-makers. We apply the method to an illustrious urban drainage network case study. Results show how the approach can simplify Pareto-fronts with thousands of solutions into sets of highlighted designs that aid interpreting the trade-offs implied by the best-performing simulated systems.

Keywords: Fuzzy data clustering; Multi-objective design; Pareto-front representation; Sustainable urban drainage systems.

MeSH terms

Algorithms*
Decision Making
Engineering*