Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements. ARTICLE HISTORY

Purpose The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions. Design/methodology/approach It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO. Findings This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment. Originality/value The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.

SEMINELLI, MICHAEL DAVID. Implementing Discrete Event Simulation to Improve Optometry Clinic Operations. (Under the direction of Dr. Thom Hodgson). As the tempo of military operations slows and Soldiers experience more time at their home installations between overseas deployments, service agencies effectively have a larger population to support. Army Medical Facilities are faced with a need to improve the efficiency and throughput of their clinics to provide timely service to this growing population all while maintaining or reducing the size of their staff. Optometry clinics provide both routine and acute care to Soldiers ensuring medical readiness and also treat Soldiers' family members and retirees from the surrounding community. A discrete event simulation was used to examine six scheduling and staffing policies for the Womack Army Medical Center's Optometry Clinic with a goal of increasing the daily throughput of the clinic with consideration to patient waiting times. The Womack Optometry Clinic serves six different patient types ranging from eye wellness exams to vision screening evaluations. All patients experience up to three basic processes in the clinic: an evaluation by an Optometry Technician, examination by an Optometrist, and if required an eye dilation exam. A discrete event simulation was constructed to model optometry clinic processes using probability distributions, derived from observed and historical data, which represent the variation in service and arrival times at each of the clinic stations. The output metrics from the baseline simulation were validated against the actual clinic performance and served as a benchmark for testing the six scheduling policies. The discrete event simulation determined the best policy increased clinic throughput by eight patients a day, generating an additional $314,000 annually, while only increasing patient wait times by 26%. As a minimum, increasing the walk-in provider's scheduled patient load from seven to nine enables the provider to optimally treat both scheduled and walk-in patients, with a $94,000 annual revenue increase. This research also identified an optimal optometrist-to-technician ratio of 2:1 was found to increase the staff utilization while having no impact on clinic performance measures. Implementation of these results will improve clinic performance, revenue, and increase Soldier's access to care.

Purpose: The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions. Design/methodology/approach: It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO. Findings: This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment. Originality/value: The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.