This thesis aims to fill the gaps in the literature of the theory of constraints (TOC) in publicly funded health systems. While TOC seems to be a natural fit for this resource-constrained environment, there are still no reported application of TOC’s drum-buffer-rope tool and inadequate customizations with regards to defining system-wide goal and performance measures. The “Drum-Buffer-Rope for an Outpatient Cancer Facility” chapter is a real world case study exploring the usefulness of TOC’s drum-buffer-rope scheduling technique in a publicly funded outpatient cancer facility. With the use of a discrete event simulation model populated with historical data, the drum-buffer-rope scheduling policy is compared against “high constraint utilization” and “low wait time” scenarios. Drum-buffer-rope proved to be an effective mechanism in balancing the inherent tradeoff between the two performance measures of instances of delayed treatment and average patient wait time. To find the appropriate level of compromise in one performance measure in favor of the other, the linkage of these measures to system-wide performance measures are proposed. In the “Theory of Constraints’ Performance Measures for Publicly Funded Health Systems” chapter, a system dynamics representation of the classical TOC’s system-wide goal and performance measures for publicly traded for-profit companies is developed, which forms the basis for developing a similar model for publicly funded health systems. The model is then expanded to include some of the factors that affect system performance, providing a framework to apply TOC’s process of ongoing improvement in publicly funded health systems. The “Connecting Low-Level Performance Measures to the Goal” chapter attempts to provide a framework to link the low-level performance measures with system-wide performance measures. It is claimed that until such a linkage is adequately established, TOC has not been fully transferred to publicly funded health systems.

Master of Science / Department of Industrial and Manufacturing Systems Engineering / Todd Easton / Integer Programming is used to solve numerous optimization problems. This class of mathematical models aims to maximize or minimize a cost function restricted to some constraints and the solution must be integer. One class of widely studied Integer Program (IP) is the Multiple Knapsack Problem (MKP). Unfortunately, both IPs and MKPs are NP-hard, potentially requiring an exponential time to solve these problems. Utilization of cutting planes is one common method to improve the solution time of IPs. A cutting plane is a valid inequality that cuts off a portion of the linear relaxation space. This thesis presents a new class of cutting planes referred to as merged knapsack cover inequalities (MKCI). These valid inequalities combine information from a cover inequality with a knapsack constraint to generate stronger inequalities. Merged knapsack cover inequalities are generated by the Merging Knapsack Cover Algorithm (MKCA), which runs in linear time. These inequalities may be improved by the Exact Improvement Through Dynamic Programming Algorithm (EITDPA) in order to make them stronger inequalities. Theoretical results have demonstrated that this new class of cutting planes may cut off some space of the linear relaxation region. A computational study was performed to determine whether implementation of merged knapsack cover inequalities is computationally effective. Results demonstrated that MKCIs decrease solution time an average of 8% and decrease the number of ticks in CPLEX, a commercial IP solver, approximately 4% when implemented in appropriate instances.

We define an order cutoff for a retailer as a time in the day such that orders sent to the depot before this point will be delivered by tomorrow, and orders submitted after will be delivered by the day after tomorrow. The later a retailer’s cutoff, the sooner it receives its orders which helps it to maintain ideal inventory levels. Generally, not all retailers in a supply chain can have the latest cutoff since transportation takes a significant amount of time. This thesis tries to assign optimal order cutoffs to retailers. We call this an order cutoff assignment problem and we solve it using three different mathematical programming approaches. The approaches are exhaustive route generation and selection, a series of mixed integer programs, and branch-and-price. 60 sample problems were solved and results showed that branch-and-price is often the most effective method.

We define an order cutoff for a retailer as a time in the day such that orders sent to the depot before this point will be delivered by tomorrow, and orders submitted after will be delivered by the day after tomorrow. The later a retailer’s cutoff, the sooner it receives its orders which helps it to maintain ideal inventory levels. Generally, not all retailers in a supply chain can have the latest cutoff since transportation takes a significant amount of time. This thesis tries to assign optimal order cutoffs to retailers. We call this an order cutoff assignment problem and we solve it using three different mathematical programming approaches. The approaches are exhaustive route generation and selection, a series of mixed integer programs, and branch-and-price. 60 sample problems were solved and results showed that branch-and-price is often the most effective method.

Doctor of Philosophy / Department of Industrial & Manufacturing Systems Engineering / David H. Ben-Arieh / Chih-Hang Wu / Patients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Clinical trials of treatments for sepsis have been extensively studied in the last 30 years, but there is no general agreement of the effectiveness of the proposed treatments for sepsis. Therefore, it is necessary to find accurate and effective tools that can help physicians predict the progression of disease in a patient-specific way, and then provide physicians recommendation on the treatment of sepsis to lower risk for patients dying from sepsis. The goal of this research is to develop a risk assessment tool and a risk management tool for sepsis. In order to achieve this goal, two system dynamic mathematical models (SDMMs) are initially developed to predict dynamic patterns of sepsis progression in innate immunity and adaptive immunity. The two SDMMs are able to identify key indicators and key processes of inflammatory responses to an infection, and a sepsis progression. Second, an integrated-mathematical-multi-agent-based model (IMMABM) is developed to capture the stochastic nature embedded in the development of inflammatory responses to a sepsis. Unlike existing agent-based models, this agent-based model is enhanced by incorporating developed SDMMs and extensive experimental data. With the risk assessment tools, a Markov decision process (MDP) is proposed, as a risk management tool, to apply to clinical decision-makings on sepsis. With extensive computational studies, the major contributions of this research are to firstly develop risk assessment tools to identify the risk of sepsis development during the immune system responding to an infection, and secondly propose a decision-making framework to manage the risk of infected individuals dying from sepsis. The methodology and modeling framework used in this dissertation can be expanded to other disease situations and treatment applications, and have a broad impact to the research area related to computational modeling, biology, medical decision-making, and industrial engineering.

Preponderant visual interface use for conveying information from machine to human admits failures due to overwhelming the visual channel. This thesis investigates the suitability of auditory feedback and certain related design choices in settings involving background speech. Communicating a tele-operated vehicle’s tilt angle was the focal application. A simulator experiment with pitch feedback on one system variable, tilt angle, and its safety threshold was conducted. Manipulated in a within-subject design were: (1) presence vs. absence of speech, (2) discrete tilt alarm vs. discrete alarm and tilt sonification (continuous feedback), (3) tilt sonification vs. tilt and threshold sonification, and (4) linear vs. quadratic transfer function of variable to pitch. Designs with both variable and reference sonification were found to significantly reduce the time drivers spent exceeding the safety limit compared to the designs with no sonification, though this effect was not detected within the set of conditions with background speech audio.

Preponderant visual interface use for conveying information from machine to human admits failures due to overwhelming the visual channel. This thesis investigates the suitability of auditory feedback and certain related design choices in settings involving background speech. Communicating a tele-operated vehicle’s tilt angle was the focal application. A simulator experiment with pitch feedback on one system variable, tilt angle, and its safety threshold was conducted. Manipulated in a within-subject design were: (1) presence vs. absence of speech, (2) discrete tilt alarm vs. discrete alarm and tilt sonification (continuous feedback), (3) tilt sonification vs. tilt and threshold sonification, and (4) linear vs. quadratic transfer function of variable to pitch. Designs with both variable and reference sonification were found to significantly reduce the time drivers spent exceeding the safety limit compared to the designs with no sonification, though this effect was not detected within the set of conditions with background speech audio.

This thesis investigates the relationships between performance, workload, and situation awareness at varying levels of automation. The relationships observed in this study are compared to a description put forth to formalize the conventional interpretation of the trade-off between the benefits of automation during routine operation and the costs under conditions of automation failure. The original work stipulated that this “routine-failure trade-off” is likely a simplification affected by contextual factors. This work therefore aimed to i) provide empirical evidence to support or refute the trade-off and ii) to identify possible extenuating factors. The results generally supported the routine-failure trade-off, and considered in light of the functional structure of the task suggested that the relationships between goals and individual functions specific to a given task seem to affect the overall costs and benefits of automation through the mechanism of selective reliance. Further work is required to validate the findings of this study.

This thesis aims to fill the gaps in the literature of the theory of constraints (TOC) in publicly funded health systems. While TOC seems to be a natural fit for this resource-constrained environment, there are still no reported application of TOC’s drum-buffer-rope tool and inadequate customizations with regards to defining system-wide goal and performance measures. The “Drum-Buffer-Rope for an Outpatient Cancer Facility” chapter is a real world case study exploring the usefulness of TOC’s drum-buffer-rope scheduling technique in a publicly funded outpatient cancer facility. With the use of a discrete event simulation model populated with historical data, the drum-buffer-rope scheduling policy is compared against “high constraint utilization” and “low wait time” scenarios. Drum-buffer-rope proved to be an effective mechanism in balancing the inherent tradeoff between the two performance measures of instances of delayed treatment and average patient wait time. To find the appropriate level of compromise in one performance measure in favor of the other, the linkage of these measures to system-wide performance measures are proposed. In the “Theory of Constraints’ Performance Measures for Publicly Funded Health Systems” chapter, a system dynamics representation of the classical TOC’s system-wide goal and performance measures for publicly traded for-profit companies is developed, which forms the basis for developing a similar model for publicly funded health systems. The model is then expanded to include some of the factors that affect system performance, providing a framework to apply TOC’s process of ongoing improvement in publicly funded health systems. The “Connecting Low-Level Performance Measures to the Goal” chapter attempts to provide a framework to link the low-level performance measures with system-wide performance measures. It is claimed that until such a linkage is adequately established, TOC has not been fully transferred to publicly funded health systems.

This thesis investigates the relationships between performance, workload, and situation awareness at varying levels of automation. The relationships observed in this study are compared to a description put forth to formalize the conventional interpretation of the trade-off between the benefits of automation during routine operation and the costs under conditions of automation failure. The original work stipulated that this “routine-failure trade-off” is likely a simplification affected by contextual factors. This work therefore aimed to i) provide empirical evidence to support or refute the trade-off and ii) to identify possible extenuating factors. The results generally supported the routine-failure trade-off, and considered in light of the functional structure of the task suggested that the relationships between goals and individual functions specific to a given task seem to affect the overall costs and benefits of automation through the mechanism of selective reliance. Further work is required to validate the findings of this study.