Stephen R. Lawrence

Clinic Overbooking to Improve Patient Access and Increase Provider Productivity

The problem of patient no-shows (patients who do not arrive for scheduled appointments) is significant in many health care settings, where no-show rates can vary widely. No-shows reduce provider productivity and clinic efficiency, increase health care costs, and limit the ability of a clinic to serve its client population by reducing its effective capacity. In this article, we examine the problem of no-shows and propose appointment overbooking as one means of reducing the negative impact of no-shows. We find that patient access and provider productivity are significantly improved with overbooking, but that overbooking causes increases in both patient wait times and provider overtime. We develop a new clinic utility function to capture the trade-offs between these benefits and costs, and we show that the relative values that a clinic assigns to serving additional patients, minimizing patient waiting times, and minimizing clinic overtime will determine whether overbooking is warranted. From the results of a series of simulation experiments, we determine that overbooking provides greater utility when clinics serve larger numbers of patients, no-show rates are higher, and service variability is lower. Even with highly variable service times, many clinics will achieve positive net results with overbooking. Our analysis provides valuable guidance to clinic administrators about the use of appointment overbooking to improve patient access, provider productivity, and overall clinic performance.

Resource-constrained multi-project scheduling with tardy costs: Comparing myopic, bottleneck, and resource pricing heuristics

Abstract This paper addresses the problem of scheduling multiple resource-constrained projects with the objective of minimizing weighted tardiness costs. Extending our earlier heuristic scheduling work for production shops, we develop an efficient and effective means of generating low cost schedules for multiple projects requiring multiple resources. A ‘cost-benefit’ scheduling policy with resource pricing is developed which balances the marginal cost of delaying the start of an eligible activity with the marginal benefit of such a delay. A central part of this policy is the heuristic estimation of implicit resource prices, which form the basis for calculating marginal delay costs. The resulting policies are tested against a number of dispatch scheduling rules taken from the project scheduling literature, and against several new scheduling rules, with encouraging results for both the weighted tardiness problem and for the special case of weighted project delay.

Estimating flowtimes and setting due-dates in complex production systems

This paper presents a methodology for estimating flowtimes and setting due-dates in complex production systems. This is accomplished by modeling flowtime estimation as a forecasting problem, and using the empirical distribution of forecast errors to set job due-dates in production settings with multiple workcenters, multiple servers, feedback queues, and machine breakdowns. Several due-date performance objectives are considered, including cost minimization, attainment of service level targets, and minimization of mean absolute lateness and mean squared lateness. Simulation experiments demonstrate the effectiveness of the method in comparison with both theoretical and empirical methods previously introduced in the literature.

Economic Analysis of Production Bottlenecks

The management of bottlenecks has become a central topic in the planning and control of production systems. In this paper, we critically analyze bottlenecks from an economic perspective. Using a queueing network model, we demonstrate that bottlenecks are inevitable when there are differences in job arrival rates, processing rates, or costs of productive resources. These differences naturally lead to the creation of bottlenecks both for facilities design and demand planning problems. To evaluate bottlenecks from an economic perspective, we develop the notion of an “economic bottleneck,” which defines resources as bottlenecks based on economic, rather than physical, characteristics.

Negotiating due-dates between customers and producers

Abstract An important managerial issue in the coordination of the manufacturing-sales interface is the joint determination of order due-dates between customer and manufacturer, mediated by sales personnel. This paper presents a methodology for negotiating due-dates between customers and producers in complex manufacturing environments. This is accomplished by modeling the setting of due-dates as a leadtime forecasting problem, and using the empirical distribution of forecast errors as the basis for negotiating and setting due-dates with customers. These distributions provide the basis for accepting or rejecting customer due-date proposals, and allows the construction of managerially useful trade-off curves between customer due-dates and several alternative performance measures including cost and service-level measures.

VOLUME AND CAPACITY INTERACTION IN FACILITY DESIGN

Abstract This paper addresses the joint facilities design problem of determining both demand and capacity with stochastic demand arrivals and stochastic processing throughput. Using a simple M/M/1 queueing model of a profit maximizing firm, we link marketing and production decision variables by recognizing appropriate congestion costs, and show that coordinated decision-making provides results superior to making demand and capacity decisions sequentially. Sensitivity analysis indicates that the model is robust with respect to its assumptions and parameters. An example illustrates the approach and demonstrates the application of the model.

Scheduling a single machine to maximize net present value

This paper investigates scheduling a single productive resource (machine) such that the net present value of the resulting cash flow stream is maximized. The static single machine setting provides an ideal context in which to develop high value scheduling policies. A job release rule (RDE) and a job dispatch rule (MTP) are developed through a marginal cost analysis of the NPV objective, and the composite MTP/RDE scheduling rule is extensively tested against several other benchmark heuristics obtained from the literature. Results indicate that the MTP/RDE rule outperforms the benchmark heuristics by a substantial margin in providing high value schedules. Additionally, sensitivity analysis shows that the MTP/RDE rule is robust in relation to parameter settings and misspecified cost data, suggesting its applicability to real-world production settings.

Exploiting Block Structure to Improve Resource-Constrained Project Schedules

We identify a block structure, typically found in resource-constrained project schedules, that yields a natural decomposition of the original problem into a series of smaller subproblems. Reducing the makespan of any subproblem has the effect of reducing the total makespan by an equivalent amount. Since these subproblems can be independently reoptimized, this block structure provides a way of improving schedules generated by heuristic methods. We report computational results of applying this technique to problems containing up to five resources and on the order of 200 activities, and speculate as to its applicability to more powerful local search based heuristics.

Emergency Department Staff Planning to Improve Patient Care and Reduce Costs

In the face of high staffing costs, uncertain patient arrivals, and patients unsatisfied with long wait times, staffing of medical emergency departments (EDs) is a vexing problem. Using empirical data collected from three active EDs, we develop an analytic model to provide an effective staffing plan for EDs. Patient demand is aggregated into discrete time buckets and used to model the stochastic distribution of patient demand within these buckets, which considerably improves model tractability. This model is capable of scheduling providers with different skill profiles who work either individually or in teams, and with patients of varying acuity levels. We show how our model helps to balance staffing costs and patient service levels, and how it facilitates examination of important ED staffing policies.

Mean-variance interactions in process improvement and capacity design

We investigate mean-variance interactions of processing time as applied to process improvement and capacity design. For general capacity cost and flowcost functions, we demonstrate that production processes fall into one of six regions on the mean-variance interaction plane, each with its own policy implications. The general model is specialized to the case of an M/G/1 queue with linear and separable mean and variance costs, and with flowcosts proportional to mean queue length. Optimal solutions for processing-time mean and variance are derived, and easily obtained operating parameters are used to identify appropriate process improvement policies. A simulation example of a production network taken from industry verifies the efficacy of the linear M/G/1 model in a more general setting. We conclude that intelligent management of both processing capacity (i.e. mean processing time) and processing-time variances can be powerful tools for both capacity design and process improvement.