Mitigating risk and ambiguity in service systems

Ph.DDOCTOR OF PHILOSOPH

Robust Appointment Scheduling with Waiting Time Guarantees

Appointment scheduling problems under uncertainty encounter a fundamental trade-off between cost minimization and customer waiting times. Most existing studies address this trade-off using a weighted sum approach, which puts little emphasis on individual waiting times and, thus, customer satisfaction. In contrast, we study how to minimize total cost while providing waiting time guarantees to all customers. Given box uncertainty sets for service times and no-shows, we introduce the Robust Appointment Scheduling Problem with Waiting Time Guarantees. We show that the problem is NP-hard in general and introduce a mixed-integer linear program that can be solved in reasonable computation time. For special cases, we prove that polynomial-time variants of the well-known Smallest-Variance-First sequencing rule and the Bailey-Welch scheduling rule are optimal. Furthermore, a case study with data from the radiology department of a large university hospital demonstrates that the approach not only guarantees acceptable waiting times but, compared to existing robust approaches, may simultaneously reduce costs incurred by idle time and overtime. This work suggests that limiting instead of minimizing customer waiting times is a win-win solution in the trade-off between customer satisfaction and cost minimization. Additionally, it provides an easy-to-implement and customizable appointment scheduling framework with waiting time guarantees

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA

Healthcare Operations Management: A Snapshot of Emerging Research

A new generation of healthcare operations management (HOM) scholars is studying timely healthcare topics (e.g., organization design, design of delivery, and organ transplantation) using contemporary methodolog- ical tools (e.g., econometrics, information economics, and queuing games). A distinguishing feature of this stream of work is that it explicitly incorporates behavior, incentive, and policy considerations arising from the entanglements across multiple entities that make up the complex healthcare ecosystem. This focus is a departure from an earlier generation of research that primarily centered on optimizing given operations of a single entity. This paper provides an introduction to this burgeoning field and maps out research opportunities. We start with identifying key entities of healthcare delivery, financing, innovation, and policymaking, illustrating them on a healthcare ecosystem map (HEM). Next, we explore the HOM literature examining the interactions among various entities in the HEM. We then develop a taxonomy for the recent HOM literature (published in Manufacturing & Service Operations Management, Management Science, and Operations Research between 2013 and 2017), provide a tool-thrust graph mapping methodological tools with research thrusts, and situate the HOM literature in context by connecting it with perspectives from medical journals and mass media. We close with a reference to technological innovations that have the potential to transform the healthcare ecosystem in future decades

Managing Operational Efficiency And Health Outcomes At Outpatient Clinics Through Effective Scheduling

A variety of studies have documented the substantial deficiencies in the quality of health care delivered across the United States. Attempts to reform the United States health care system in the 1980s and 1990s were inspired by the system\u27s inability to adequately provide access, ensure quality, and restrain costs, but these efforts had limited success. In the era of managed care, access, quality, and costs are still challenges, and medical professionals are increasingly dissatisfied. In recent years, appointment scheduling in outpatient clinics has attracted much attention in health care delivery systems. Increase in demand for health care services as well as health care costs are the most important reasons and motivations for health care decision makers to improve health care systems. The goals of health care systems include patient satisfaction as well as system utilization. Historically, less attention was given to patient satisfaction compared to system utilization and conveniences of care providers. Recently, health care systems have started setting goals regarding patient satisfaction and improving the performance of the health system by providing timely and appropriate health care delivery. In this study we discuss methods for improving patient flow through outpatient clinics considering effective appointment scheduling policies by applying two-stage Stochastic Mixed-Integer Linear Program Model (two-stage SMILP) approaches. Goal is to improve the following patient flow metrics: direct wait time (clinic wait time) and indirect wait time considering patient’s no-show behavior, stochastic server, follow-up surgery appointments, and overbooking. The research seeks to develop two models: 1) a method to optimize the (weekly) scheduling pattern for individual providers that would be updated at regular intervals (e.g., quarterly or annually) based on the type and mix of services rendered and 2) a method for dynamically scheduling patients using the weekly scheduling pattern. Scheduling templates will entertain the possibility of arranging multiple appointments at once. The aim is to increase throughput per session while providing timely care, continuity of care, and overall patient satisfaction as well as equity of resource utilization. First, we use risk-neutral two-stage stochastic programming model where the objective function considers the expected value as a performance criterion in the selection of random variables like total waiting times and next, we expand the model formulation to mean-risk two-stage stochastic programming in which we investigate the effect of considering a risk measure in the model. We apply Conditional-Value-at-Risk (CVaR) as a risk measure for the two-stage stochastic programming model. Results from testing our models using data inspired by real-world OBGYN clinics suggest that the proposed formulations can improve patient satisfaction through reduced direct and indirect waiting times without compromising provider utilization

Distributionally robust views on queues and related stochastic models

This dissertation explores distribution-free methods for stochastic models. Traditional approaches operate on the premise of complete knowledge about the probability distributions of the underlying random variables that govern these models. In contrast, this work adopts a distribution-free perspective, assuming only partial knowledge of these distributions, often limited to generalized moment information. Distributionally robust analysis seeks to determine the worst-case model performance. It involves optimization over a set of probability distributions that comply with this partial information, a task tantamount to solving a semiinfinite linear program. To address such an optimization problem, a solution approach based on the concept of weak duality is used. Through the proposed weak-duality argument, distribution-free bounds are derived for a wide range of stochastic models. Further, these bounds are applied to various distributionally robust stochastic programs and used to analyze extremal queueing models—central themes in applied probability and mathematical optimization

Distributionally robust views on queues and related stochastic models

This dissertation explores distribution-free methods for stochastic models. Traditional approaches operate on the premise of complete knowledge about the probability distributions of the underlying random variables that govern these models. In contrast, this work adopts a distribution-free perspective, assuming only partial knowledge of these distributions, often limited to generalized moment information. Distributionally robust analysis seeks to determine the worst-case model performance. It involves optimization over a set of probability distributions that comply with this partial information, a task tantamount to solving a semiinfinite linear program. To address such an optimization problem, a solution approach based on the concept of weak duality is used. Through the proposed weak-duality argument, distribution-free bounds are derived for a wide range of stochastic models. Further, these bounds are applied to various distributionally robust stochastic programs and used to analyze extremal queueing models—central themes in applied probability and mathematical optimization