The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing

Des Fagan*, Ruth Conroy Dalton

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


The research considers buildings as a test case for the development and implementation of multi-objective optimized social distance layout redesign. This research aims to develop and test a unique methodology using software Wallacei and the NSGA-II algorithm to automate the redesign of an interior layout to automatically provide compliant social distancing using fitness functions of social distance, net useable space and total number of users. The process is evaluated in a live lab scenario, with results demonstrating that the methodology provides an agile, accurate, efficient and visually clear outcome for automating a compliant layout for social distancing.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science
Subtitle of host publication7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton
Place of PublicationCham, Switzerland
Number of pages6
ISBN (Electronic)9783030954703
ISBN (Print)9783030954697
Publication statusPublished - 2 Feb 2022
Event7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online
Duration: 4 Oct 20218 Oct 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
CityVirtual, Online

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