Thomas R. Rohleder

Using Simulation Modeling to Improve Patient Flow at an Outpatient Orthopedic Clinic

We report on the use of discrete event simulation modeling to support process improvements at an orthopedic outpatient clinic. The clinic was effective in treating patients, but waiting time and congestion in the clinic created patient dissatisfaction and staff morale issues. The modeling helped to identify improvement alternatives including optimized staffing levels, better patient scheduling, and an emphasis on staff arriving promptly. Quantitative results from the modeling provided motivation to implement the improvements. Statistical analysis of data taken before and after the implementation indicate that waiting time measures were significantly improved and overall patient time in the clinic was reduced.

Rolling Horizon Appointment Scheduling: A Simulation Study

Health-care consumers continue to be frustrated with long waits, especially when an appointment has been made. However, providers who book appointments are under increasing pressure to maximize utilization so that revenues will be increased and costs reduced. Thus, scheduling appointments involves opposing forces that are difficult to manage. This challenge is addressed in a rolling-horizon environment with fluctuating demand loads. These two issues have not been explored previously in the appointment-scheduling research. Two management policies are considered: overload rules (OLR) and rule delay (RD). The former considers different scheduling methods (overtime, double booking) when demand loads are high, and the rule delay policy considers when to implement the overload rules. These methods are explored for six different demand patterns/loads and evaluated with a variety of client and server-oriented measures. The results show that managers of appointment scheduling systems must carefully consider which measures are most important to them since the best choices of OLR and RD vary substantially by measure. Good choices also depend on the general type of client demand pattern. Thus, to consider the various tradeoffs between client and server measures a matrix is developed that outlines good choices for each scenario.

Outpatient appointment scheduling with urgent clients in a dynamic, multi‐period environment

Time waiting for service is a major concern for consumers, and excessive waiting for a pre‐scheduled appointment is especially annoying. This is an on‐going problem because appointment scheduling is a challenging task, mainly due to the uncertainties associated with service times. Prior studies have focused mainly on a single scheduling period (i.e. either a morning or afternoon); this paper uses a more realistic model that represents an on‐going, multi‐period scheduling environment where clients can be scheduled days or even weeks into the future. Two main objectives will be considered; the best scheduling rule to use in a multi‐period environment, and the best placement of appointment slots that are left open for urgent clients. Both of these have been studied in a single period environment, and results here will be compared to those. It will be shown that in some cases earlier findings from the one‐period environment are robust and perform well in a multi‐period environment, while in other cases the one‐period findings do not apply.

A tutorial on business process improvement

Abstract Considering the strategic importance of business process improvement, it is imperative that educators, students, and practitioners be familiar with this topic. Thus, the intention of this tutorial is to provide a guiding framework for carrying out improvements of business processes. We present numerous illustrative examples, taken from our personal experiences and those of our students, as well as from the literature. An extensive reference list is provided, thus pointing the interested reader to sources of further detail on specific topics.

Optimal booking and scheduling in outpatient procedure centers

Patient appointment booking, sequencing, and scheduling decisions are challenging for outpatient procedure centers due to uncertainty in procedure times and patient attendance. We extend a previously developed appointment scheduling model to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty. The objective is to maximize expected profit. Analytical insights are reported for special cases and experimental results show that they provide useful rules of thumb for more general problems. Three solution methods are described which take advantage of the underlying structure of the stochastic program, and a series of experiments are performed to determine the best method. A case study based on an endoscopy suite at a large medical center is used to draw a number of useful managerial insights for procedure center managers.

Finding effective schedules for the economic lot scheduling problem: A simple mixed integer programming approach

We extend past research on the economic lot scheduling problem to address some of the limitations of the earlier work. In particular we develop mixed integer programming formulations with the assumption of a production precedence sequence. When evaluated over a variety of problems from the literature, it is clear that finding a schedule that minimizes costs is not trivial. However, with the use of an effective sequencing heuristic and appropriate formulations, optimal schedules can often be found.

Demand and capacity management decisions in services: How they impact on one another

Service managers are continually challenged with balancing customer demand and service capacity. Recent studies have raised awareness of various demand and capacity management practices available to services, but little numerical work has been done to identify how these decisions work together and how they relate to one another. For instance, reducing prices may attract customers during a slow period, but the extent of impact this should have on cross‐training staff is not clear. A simulation based on theoretical and empirical insights explores the impact of various decisions on profitability and operations. The decisions modelled include the impact of: automation, customer participation, cross training employees, informing customers about the operation, and others. It is shown that demand and capacity decisions do indeed impact on each other – sometimes in ways that are not initially obvious. Results provide useful thought‐starters for service managers striving to improve their operations.

Use of the net present value criterion in a random job shop where early shipments are forbidden

Abstract Both practitioners and researchers in the field of Operations Management have suggested that shop scheduling should be an integral component in both the strategic and tactical plans for an organizations assets. This paper examines the use of an accepted measure of return on assets, net present value (NPV), in a simulated shop scheduling environment where early shipment of jobs before their due dates is forbidden. In addition, early shipment of raw materials to the shop is also forbidden. This shop environment is consistent with the prevalent practice in industry of accepting orders only on a just-in-time basis to reduce purchased parts inventories. The NPV measure provides a means of balancing a variety of performance criteria that have been treated as separate objectives previously, including work-in-process inventory, finished goods inventory, mean flow time and mean tardiness, while also providing a means of measuring monetarily the value of various shop scheduling approaches. The NPV performance of priority scheduling rules and order release policies is measured in this research through the simulation of a random job shop under a variety of environmental conditions. It is found in a comparison of priority rules that use time-based information with those that use job value information that the Critical Ratio rule provides higher average performance than the three other rules used in the study. However, in some situations that are consistent with JIT practice, value-based priority rules also perform well. The use of a mechanism for delaying the release of jobs to each work center in the shop provided higher average NPV when shop utilization was set at a low level of 80%, while immediate release of work upon its arrival to the shop provided superior performance at a higher shop utilization level of 94%. While JIT materials delivery and costing yields higher NPV, it did not alter the relative ranking of priority rule/release policy combinations. In addition, it was found that environmental factors, including average job length, average number of tasks per job and level of tardiness penalty, resulted in greater variations in NPV performance than the institution of a JIT raw materials delivery policy.

Incorporating human resources in group technology/cellular manufacturing

In todays fiercely competitive marketplace, firms are looking for ways to improve their profitability. Computer simulation studies comparing group technology/cellular manufacturing (GT/CM) to job-shop layouts addressed this topic. However, each study ignored some of the most basic operating conditions said to give GT/ CM its advantages: this simulation study incorporates some of those operating conditions like human resource issues, such as learning and labour constraints, in a comprehensive setting. The results indicate that GT/CM significantly outperforms the job shop layout in almost every environmental setting.

Scheduling rule selection for the forbidden early shipment environment: a comparison of economic objectives

In a recent paper, Christy and Kanet (1990) examined the performance of several dispatching rules in a forbidden early shipment environment. Their primary performance measure was time-weighted inventory value. This study re-examines their results using a new model, with a net present value (NPV) performance measure. The results show that using NPV and inventory objectives lead to different scheduling decisions. Also examined are the reasons for relatively poor performance of operation-based due date rules.