Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Industrial Engineering

Degree Program

Industrial Engineering, PhD

Committee Chair

Parikh, Pratik

Committee Co-Chair (if applicable)

Saleem, Jason

Committee Member

Saleem, Jason

Committee Member

Gentili, Monica

Committee Member

Mowrey, Corinne

Author's Keywords

Operations research in health services; hospital layout design; visibility; patient care; bi-objective optimization; heuristics


Patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Healthcare literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. This dissertation fills significant voids in this domain and adds much-needed realism to develop insights that hospital decision-makers can use to design their inpatient unit layout. Our first contribution (Chapter 2) adopts an interdisciplinary approach that combines the human field of regard with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-, I-, and R-shaped; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visibility of a target in a room when viewed by the nurse from the nursing station. This algorithm considers nurses' horizontal visual field and their depth of vision. We also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions to the bi-objective optimization model. Our findings suggest that the R-shaped layout outperforms the other two layouts on these visibility-based objectives. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room. In our second contribution, we extend the model in the first contribution to now include position of the bed in patient rooms as a decision variable and consider various door positions. We consider four distinct layout types, L–shaped, U-shaped, R-shaped, and I-shaped, with eight patient rooms and a nurse-to-patient ratio of 1:4. We propose an ε-constrained approach to convert the corresponding bi-objective optimization model into a single objective optimization model, prioritizing equity as an objective function. We propose a progressive refinement algorithm to solve this optimization model within a reasonable time. Our findings suggest that a significant improvement in the equity score of a layout can be obtained through the joint determination of patient beds and nurse positions. We also perform a comparative analysis of equity offered by various layout types and observed that angular layout types are a promising output. We also observed that higher spatial distance between nurses is beneficial in achieving higher equity measures when obstruction is high in the case of angular layouts. There are several implications of our findings to practice. The insights from our study related to the impact of layout shapes, bed locations, and obstruction levels on patient visibility can help decision-makers in both greenfield and retrofitting of existing inpatient unit layout designs. Our models can quickly identify highly visible layouts, avoiding costly trial and error in layout changes. Improved decision-making in inpatient unit design will facilitate better patient experiences through equitable visibility distribution and enhanced quality of care.