A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Graphical Networks for Optimizing Hospital Layouts at Macro- and Micro-Scales
Graphical networks have been used for generative design to create maximally planar graphs to create initial layouts. In this work, we propose the use of graphical networks to (1) implement sparsity network constraints, (2) analyze the relationships between nodes at multiple scales, and (3) create a graphical network of nodes based on the flow of resources (e.g., nurses). In this approach, we present a solution procedure for macro and micro scales in a hospital layout problem optimized for the flow of nurses (i.e., between the department and within-department scales). The theoretical formulation and solution procedures are based on user requirement data. Sparsity constraints are introduced to reduce the solution complexity of a layout that includes many nodes while also addressing a common limitation of graphical networks, feasibility. The resulting generated design conceptually aligns to hierarchical user input, bridging a gap between prior work on facility layout optimization. The approach creates a framework for future algorithms that can scale between macro and micro (in this case, Hospital-level and Department-level) to determine a generated and optimized form for conceptual planning. The implications of this work will enhance quantitative analysis in healthcare facility planning and general layout planning procedures for civil and construction engineering layout planning problems at multiple scales.
Graphical Networks for Optimizing Hospital Layouts at Macro- and Micro-Scales
Graphical networks have been used for generative design to create maximally planar graphs to create initial layouts. In this work, we propose the use of graphical networks to (1) implement sparsity network constraints, (2) analyze the relationships between nodes at multiple scales, and (3) create a graphical network of nodes based on the flow of resources (e.g., nurses). In this approach, we present a solution procedure for macro and micro scales in a hospital layout problem optimized for the flow of nurses (i.e., between the department and within-department scales). The theoretical formulation and solution procedures are based on user requirement data. Sparsity constraints are introduced to reduce the solution complexity of a layout that includes many nodes while also addressing a common limitation of graphical networks, feasibility. The resulting generated design conceptually aligns to hierarchical user input, bridging a gap between prior work on facility layout optimization. The approach creates a framework for future algorithms that can scale between macro and micro (in this case, Hospital-level and Department-level) to determine a generated and optimized form for conceptual planning. The implications of this work will enhance quantitative analysis in healthcare facility planning and general layout planning procedures for civil and construction engineering layout planning problems at multiple scales.
Graphical Networks for Optimizing Hospital Layouts at Macro- and Micro-Scales
Lather, Jennifer I. (author) / Harms, Andrew (author)
Construction Research Congress 2022 ; 2022 ; Arlington, Virginia
Construction Research Congress 2022 ; 747-756
2022-03-07
Conference paper
Electronic Resource
English
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