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  • LOD_2021_paper_167

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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Publication date2/02/2022
Host publicationMachine Learning, Optimization, and Data Science: 7th 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
ISBN (Electronic)9783030954703
ISBN (Print)9783030954697
<mark>Original language</mark>English

Publication series

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


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.