using agent based simulation to understand queue management post covid-19

How Agent-Based Simulation Can Help Reduce Impact of Queue Management Post COVID-19

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by Joni Newkirk, CEO, and Ben Dubiel, VP,  Integrated Insight

Published April 28, 2020

Summertime is often synonymous with long lines as Americans begin to travel. Long wait times increase at the airport, amusement parks, and even entrances to beaches and state and national parks. This summer, those queue lines might also include job applicant or unemployment lines, and even food bank lines for people or cars. Lines for access to testing for COVID-19 will also pop up as more capability becomes available. And in November, add voting lines to the mix of queue lines that need to be restructured for social distancing.

Psychology dictates how consumers perceive waiting time in lines. As Lavi Industries points out in the “The Art and Science of Queuing”, consumers want to feel as though they are in control. They want to start right away, or at least be instantly acknowledged. They need to know how long they’ll be waiting before deciding to get in line. Their radar goes up if they feel someone else is cutting in. And using distractors to make the perceived wait feel like less time is a great stress reducer.

Setting psychology aside, effectively managing queue lines is the most definitive step you can take to enhance the customer experience. However, it is not always intuitive. And it is not something that is easy to optimize just by sight.

Cutting wait times significantly is possible with the right process. At a minimum, a queuing process revolves around two forces:

  1. The arrival rate of patrons.
  2. The amount of time it takes to serve one customer.

Both of these factors can vary. The added complication is how the service is delivered; primarily, how the servers are arranged and how guests in the waiting lines approach the servers. Through agent-based simulation, it is easier to both see and record the impact of different queue processes.

Here is an example of using agent-based simulation to optimize queue lines. When Florida schools shut down due to COVID-19, a free lunch pickup program was initiated. This required schools to quickly determine a distribution plan.

We took a look at the distribution process for a local Orlando high school and used agent-based modeling to identify bottlenecks. The high school currently uses a single line for lunch pickup. At the first stop, guests provide their name and the number of meals being picked up. Next, a monitor directs the driver to one of two stations directly ahead for pickup. These two stations distribute the same meal.

In this queue line, valuable time is lost if the pickup occurring in the second station is slower than the first as the next car to pick up is blocked by the car ahead. Under this scenario, the distribution process can take hours to complete and parents are consuming time sitting in their car.

We built the distribution environment in agent-based simulation software to see the impacts that single lane queuing had on the time parents had to wait in their cars. In the video below, you can see wait times reach higher than 30 minutes as the cars stack up.

Before – Single Lane Fulfillment with 35 minute wait time by 12 p.m.

Using agent-based simulation, we created a model with parallel fulfillment (using two lanes), to see if lunches could be distributed more efficiently.

The alternative process, two distinct lines, cuts distribution time significantly. Rather than cars waiting behind one another, the approaching line splits into two. Each driver is free to leave once done. This process still uses just four staff members given there isn’t a need for a monitor to direct traffic. In total, average wait time is reduced from 35 minutes to 11, and over 100 more cars are serviced in a single hour.

Recommended – Parallel Lane Fulfillment with 11 minute wait time by 12 p.m.

Here you can watch the metrics change as the two side by side processes continue.

Queue management has a significant impact on the bottom line. Efficient queues increase throughput, brand loyalty, and customer satisfaction.

This is one small example of how simulation can bring to light what customers experience and help justify process changes. Relatively minor fixes can give minutes or hours back to time-starved, task-loaded consumers. For more complex processes, the intrinsic value is even greater. And with social distancing, being able to iterate potential solutions is made far easier with agent-based simulation.

For more information on agent-based simulation and how we partner with brands across the globe, please contact us at info@integratedinsight.com

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