In recent decades, health care costs have dramatically increased, while health care organisations have been under severe pressure to provide improved quality health care for their patients. Several health care administrators have used discrete-event simulation as an effective tool for allocating scarce resources to improve patient flow, while minimising health care delivery costs and increasing patient satisfaction. The rapid growth in simulation software technology has created numerous new application opportunities, including more sophisticated implementations, as well as combining optimisation and simulation for complex integrated facilities. This paper surveys the application of discrete-event simulation modeling to health care clinics and systems of clinics (for example, hospitals, outpatient clinics, emergency departments, and pharmacies). Future directions of research and applications are also discussed.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2600669.pdf

Application of discrete-event simulation in health care clinics: A survey

Production scheduling can be defined as the allocation of available production resources over time to best satisfy some set of criteria. Typically, the scheduling problem involves a set of tasks to be performed, and the criteria may involve both tradeoffs between early and late completion of a task, and between holding inventory for the task and frequent production changeovers. The intent of this paper is to present a broad classification for various scheduling problems, to review important theoretical developments for these problem classes, and to contrast the currently available theory with the practice of production scheduling. This paper will highlight problem areas for which there is both a significant discrepancy between practice and theory, and for which the practice corresponds closely to the theory.

A Review of Production Scheduling

The evolution of the intellectual structure of operations management—1980–2006: A citation/co-citation analysis

The problem of plant layout has generally been treated as a static one. In this paper, we deal with the dynamic nature of this problem. Both optimal and heuristic procedures are developed for this problem and are based on a dynamic programming formulation. The use of one of these approaches depends on the ability to solve the static problem efficiently. Finally, we briefly discuss the issue of extending the planning horizon, and how to resolve system nervousness when previously planned layouts need to be changed.

/pdf/the-dynamics-of-plant-layout-2yv2yiiaoz.pdf

The dynamics of plant layout

Over the past forty years, health care organizations have faced ever-increasing pressures to deliver quality care while facing rising costs, lower reimbursements, and new regulatory demands. Discrete-event simulation has become a popular and effective decision-making tool for the optimal allocation of scarce health care resources to improve patient flow, while minimizing health care delivery costs and increasing patient satisfaction. The proliferation of increasingly sophisticated discrete-event simulation software packages has resulted in a large number of new application opportunities, including more complex implementations. In addition, combined optimization and simulation tools allow decision-makers to quickly determine optimal system configurations, even for complex integrated facilities. This chapter provides an overview of discrete-event simulation modeling applications to health care clinics and integrated health care systems (e.g. hospitals, outpatient clinics, emergency departments, and pharmacies) over the past forty years.

Discrete-event simulation of health care systems

The Wagner-Whitin dynamic economic lot-sizing technique has not been widely applied to real-world production scheduling problems. A frequently quoted reason is the extreme sensitivity of the solution to changes in the estimates of future values of the problem's parameters, especially future demand. This “nervousness” has been of great concern to users of MRP (Material Requirements Planning) systems. A solution procedure which incorporates the cost of changing the current production schedule alleviates this nervousness by considering its economic effect. Thus updated parameter forecasts can be effectively used, and the schedule will be changed only when the joint consideration of setup, inventory holding, and schedule change costs indicates that it is economically beneficial to do so.

Less Nervous MRP Systems: A Dynamic Economic Lot-Sizing Approach

Previous work dealt with the problems of nervousness in Material Requirements Planning (MRP) Systems when production schedules were modified to include setups which previously had not been scheduled. In this work we further examine nervousness and its cost. By modifying the setup costs used in the Wagner-Whitin algorithm, we develop a generalized solution methodology for dealing with situations in which new setups are added to the production schedule and existing setups are cancelled.

A Lot-Sizing Algorithm for Reducing Nervousness in MRP Systems

An important problem facing the manager of an outpatient health care clinic involves determining the best combination of services, facilities, and personnel to maximize profits and simultaneously achieve acceptable measures of the clinic's daily performance. This problem is often attacked using either linear programming or computer simulation. This paper uses a recursive optimization-simulation approach which takes advantage of the best features of both optimization and simulation while minimizing the disadvantages of each method used alone. Results from a hypothetical setting using data from several actual settings demonstrate the value of the recursive method.

Use of optimization and simulation models to analyze outpatient health care settings

For each of several regions a firm has forecasted a probability distribution of future annual demand, and the plan is to build a single production facility to serve this demand Each region is served by a simple leased warehouse with a lease cost which is a linear function of capacity Any regional demand exceeding warehouse capacity is lost The cost of building the plant is a nonlinear function of its capacity, and the unit cost of producing the product and supplying it to a region is constant We develop an efficient algorithm based on the Kuhn-Tucker conditions for simultaneously determining the plant and warehouse capacities which will maximize the firm's expected profit The paper includes a numerical example demonstrating the use of this algorithm

The Simultaneous Determination of Plant and Leased Warehouse Capacities for a Firm Facing Uncertain Demand in Several Regions

Recent work dealing with planning of complex facilities has proposed use of a recursive optimization-simulation approach. This technique takes advantage of the best features of both methods while minimizing the disadvantages of each method used alone. Here a mixed integer program generates staffing and facility plans and a simulation model evaluates their day-to-day acceptability. Then a linear regression uses the simulation results to generate non-cost constraints to be added to the optimization model. Results from a hypothetical health care setting demonstrate the value of the recursive method.

Dean H. Kropp

Papers

Less Nervous MRP Systems: A Dynamic Economic Lot-Sizing Approach

A Lot-Sizing Algorithm for Reducing Nervousness in MRP Systems

Use of optimization and simulation models to analyze outpatient health care settings

The Simultaneous Determination of Plant and Leased Warehouse Capacities for a Firm Facing Uncertain Demand in Several Regions

Use of both optimization and simulation models to analyze complex systems