Mustafa Y. Sir

Supply chain design considering correlated failures and inspection in pharmaceutical and food supply chains

Abstract This paper studies the impact of correlated supplier failures as well as inspection to detect these failures in the context of a supplier selection problem. A two-stage stochastic programming model is developed to explore the tradeoffs between costs and risk when designing a supply network. The first-stage decisions include the strategic decisions of determining which suppliers should be selected considering suppliers location and capacity while in the second stage, operational decisions related to transportation and inspection are determined. Several computational results are presented examining the effect of supplier correlation and inspection on supplier selection, transportation, and inspection strategies. A sensitivity analysis is also performed to explain the effect of key parameters on expected total cost and expected cost of shipped tainted materials.

Application of constrained optimization methods in health services research : report 2 of the ISPOR optimization methods emerging good practices task force

BACKGROUND Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity. OBJECTIVES In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available. CONCLUSIONS Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Forces first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods.

Functional regression-based monitoring of quality of service in hospital emergency departments

Abstract This article focuses on building a statistical monitoring scheme for service systems that experience time-varying arrivals of customers and have time-varying service rates. There is lack of research in the systematic statistical monitoring of large-scale service systems, which is critical for maintaining a high quality of service. Motivated by the emergency department at a major academic medical center, this article intends to fill this research gap and provide a practical statistical monitoring scheme capable of detecting changes in service using readily available time stamp data. The proposed method is focused on building a functional regression model based on customer arrival and departure time instances from an in-control system. The model finds the expected departure intensity function for an observed arrival intensity on any given day of operation. The mean squared difference between the expected departure intensity function and the observed departure intensity functions is used to generate an alarm indicating a significant change in service. This methodology is validated using simulation and real data case studies. The proposed method can identify patterns of inefficiency or delay in service that are hard to detect using traditional statistical monitoring algorithms. The method offers a practical approach for monitoring service systems and determining when staffing levels need to be re-optimized.

A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality

Abstract We develop a multi-objective stochastic programming model to explore tradeoffs between costs and risk in the supply chain in the event a disruption occurs. We explicitly consider network configuration and operating cost under normal conditions, cost of unsatisfied demand, cost of shipping tainted products to a customer, and quality inspection cost as conflicting goals to be minimized simultaneously. We analyze different disruption scenarios to determine the best supplier selection and inspection strategies to mitigate the effect of disruptions on supply availability and quality. Even the single-objective version of this problem is NP-hard; thus, we propose a genetic algorithm-based search method to identify Pareto-optimal supply chain configurations. We use data envelopment analysis for calculating the fitness value of various supply chain configurations. The proposed approach efficiently yields high-quality supply chain designs, allowing the decision maker to determine an appropriate tradeoff between various costs.

Capacitated inspection scheduling of multi-unit systems

Abstract We consider the inspection scheduling problem of multi-unit systems where the inspections of individual units are coupled via a capacity constraint. Although the optimal inspection policy of the majority of single-unit systems can be characterized by a threshold policy, finding an optimal policy for multi-unit systems is significantly harder. Therefore, the current state-of-the-practice uses a periodical inspection policy for all units. Instead, we propose using a dynamic programming (DP) approach to solve small-scale problem instances to optimality and use solutions optimized for a single-unit system in an approximation scheme to obtain near-optimal solutions for large-scale problems. Our results show that taking individual properties of the units to be inspected into account and incorporating the single-unit solutions within an approximate DP framework significantly decrease the inspection cost compared to a periodical inspection policy. The proposed methods can help resource-constrained regulatory agencies such as US Food and Drug Administration (FDA) to optimize their inspection activities.

Identifying factors influencing patient alone time at the emergency department using RFID data: What is next?

Emergency Department (ED) represents a highly chaotic environment with a big responsibility to provide critical care to patients on a rapid fashion for life saving service. The operating capacity of ED is often challenged with overcrowding especially with patients at non-emergency situation using it as point of access for primary care. Application of Radio Frequency Identification Device (RFID) technology in ED is gaining significant attention with the potential to improve ED care service and subsequently reduce cost. Our previous pilot study demonstrated the feasibility of quantifying patient alone time and provider time in establishing relationship to the ED length of stay (LOS). In this work, RFID data within the ED on a larger patient group was used to quantify and understand the various factors influencing ‘patient alone’ time in ED. Results indicate that many factors such as patients per physician or nurse and order volumes are controllable to improve the operating efficiency. These findings motivates further investigation to explore the relationship of patient alone time to overall hospital LOS, readmission, patient leaving without being seen, mortality, patient satisfactions and other complications for a particular cohort of disease group to improve quality of care at ED.

A coordinated scheduling policy to improve patient access to surgical services

This paper presents a scheduling policy that aims to reduce patient wait time for surgical treatment by coordinating clinical and surgical appointments. This study is of interest since the lack of coordination of these resources could lead to an inefficient utilization of available capacity, and most importantly, could cause delays in patient access to surgical treatment. A simulation model is used to analyze the impact of the policy on patient access and surgical throughput.