Discrete-Event Simulation: An Improvement Process for Any System Type

What is discrete-event simulation?

Systems are in a constant state of change. Raw material arrives at a factory, gets transformed into something useful and leaves for the customer. Customers arrive at a bank, withdraw or deposit money and exit. Patients arrive at a clinic, receive care and depart. Whether it’s a clinic, a bank, or a factory, the process of moving entities (patients, staff or material) through a system can either run efficiently or become congested.

A discrete event simulation model is a virtual representation of such a system that takes into consideration resource constraints like the number of available rooms, staff and equipment, and other operational constraints like resource scheduling and arrival patterns that often limit system performance. When the rate of arrivals out-paces the available processing capacity, bottlenecks occur.

The DES model will simulate a system over time to study its behavior and uncover those bottlenecks. Changes are then made to the virtual system and tested for their impacts on system performance, without implementing costly changes to the real, physical system. The DES models the variability and uncertainty of specific inputs.  So rather than dealing purely with averages and what typically happens, the simulation provides statistics on the range of what could possibly happen.

From these attributes, DES models are a uniquely objective and data-driven tool for supporting the decision making process within highly-complex design efforts across many industries.

What are the common uses of DES?

DES has been in existence for several decades, since the early days of operations research and computing. As a result, DES has been applied to countless industries, including construction, logistics, manufacturing, service systems (food service, banking, amusement parks, etc.) and healthcare. Anywhere we find complex operational decisions to be optimized under uncertainty, DES can be a useful decision support tool.

And this use of DES impacts our everyday lives in many ways.

Have you been to a major airline lobby in any big cities in the U.S. since 2016? If yes, then you might have experienced self-checking and self-tagging process (tagging your checked-in luggage with a bar code) using a kiosk. Well, the number of kiosks was determined using DES!

These well-known airline companies have strict passenger experience standards: for example, 20 minute maximum waiting time for economy passengers and five minutes for their most frequent fliers. Using DES modeling, they were able to identify the correct number of kiosks needed to handle their passenger population, given the flight schedules throughout the days, the different passenger needs, and the seasonality effect throughout the year and without compromising their passenger satisfaction standards. This DES application is just one example among thousands.

How do we use DES at HDR?            

Our teams at HDR have been consistently using simulation over the past several years to assist in the planning and design phases of healthcare architecture projects. We’ve found that DES is an effective method for improving the quality and efficiency of healthcare settings through the optimization of resource allocation, patient flow and care processes. 

During the planning phases of healthcare projects, a large number of DES efforts focus on determining the necessary resource requirements of the space, such as the number of patient care rooms (or stations), and further examining the relationship between room count and operational performance measures of the system, such as patient waiting times, patient time with caregivers, patient throughput and resource utilization.

DES provides clear advantages to traditional programming and planning methods, particularly in healthcare environments where patient needs are complex and demand for care is highly variable over time. During the design phases, DES studies have also examined the relationship between layout design of healthcare facilities and these performance measures.


HDR developed a DES model reflecting the future design layout a brand new Ambulatory Surgery Center (ASC). Fed by the future patient volume, among other key inputs, the simulation model helped the client understand the impact of different operating scenarios on the space need, and the patient and staff experience. Our client leadership were able to make data-driven decisions and ensure the operational efficiency of the future ASC.  


This study highlighted the implications of DES and climate-based daylight modeling in developing a framework for predicting the duration of daylight exposure for caregivers, considering their work processes and space occupancy patterns in healthcare settings. This approach can be replicated for different environments (manufacturers, airports, and more) to help inform the noise and cold or heat exposure of the workers given specific operational scenarios.

While healthcare has been the focus of much of our team’s recent work, we’re also adding value through simulation modeling with clients across HDR. Outside healthcare, our team has other interesting experiences with simulation, such as the modeling of material transport in manufacturing settings.  For example, a past engagement with a large agricultural equipment manufacturer employed simulation to determine the most cost-effective material handling strategies and inventory policies to support Just-in-Time while avoiding production delays due to part shortages. 

What is the typical DES project timeline?

Short answer: there is no typical project timeline for a DES simulation engagement. The timeline for building and using a DES model depends on a large number of factors that vary from project to project.

It may be helpful to think of a simulation engagement in other ways to begin to focus in on the right timeline for an engagement, such as, “What is the complexity of the problem being solved?” Or, “What is the timeline available for making a decision?”

Logistically, there are other factors that affect a project timeline, such as how easily the data inputs for a model can be obtained and whether there are champions who will help keep the project moving forward both internally and externally.

In general, DES models are highly data driven and, therefore, require in-depth data collection and process understanding. Model development also takes time for programming and development, including verification and validation. At the end of the process, the DES model will be used to conduct scenario testing and “what-if” testing. In total, the time and effort to complete a study will be highly variable depending on the client’s needs and expectations, and can be measured in months.

What is the future of DES?

There are a large number of system types that can be improved through simulation modeling. Overall, our team sees an opportunity for expanding the application of DES within HDR to improve our own processes and products, and externally to serve a broader range of clients.

For example:

  • Expanding DES into Engineering: interfacing with our transportation, industrial, urban planning, and other markets throughout the company.
  • Using DES to support process improvement efforts for our clients, independent from a larger design effort (moving towards the later end of the project lifecycle).
  • Combining DES with other design tools to seamlessly design spaces and test operational performance.
  • Combining DES with other artificial intelligence and analytics tools to create high-fidelity predictive models.
Advanced Simulation Specialist
Industrial Engineer