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Heather Moe
Heather Moe

Heather Moe

Product Engineer
Esri
Bio

After graduating from Rochester Institute of Technology with a BS in Biology, Heather Joined the NOAA Corps. In her seven years serving she held a wide variety of positions from Safety Officer on the NOAA Ship Miller Freeman sailing the Pacific to conducting climate research in remote polar stations. During this time Heather learned of the field of Operations Research and could see its applications everywhere. Heather earned a MS in Operations Research from Kansas State University while still in the NOAA Corps but then transitioned to civilian life working as a Principal Product Engineer at Esri supporting the development of their Vehicle Routing Problem algorithms. Heather has worked at Esri ten years now and is managing projects on top of her algorithm work.


Residential Waste Collection Algorithm Testing

Residential waste collection is stopping at most houses on all residential streets in the municipality to collect the garbage and/or recycling. In the United States that is often done with the automated garbage trucks that use the arms to pickup the bins and empty them into the truck. In Europe that is often done with a crew behind the vehicle grabbing the bins from both sides of the street and emptying them in the back of the truck as it slowly goes down the street. This type of problem could be solved as an Arc Routing Problem (or Chinese Postman Problem) or as a very high-density Vehicle Routing Problem. Our Team’s experience is in VRP, so we choose to tackle the problem that way but in looking for how to benchmark the algorithm we found very little, so we needed to develop our own testing plan.

In this talk we will briefly explain the modeling problem and requirements we gathered from our customer interviews. We will then discuss how we developed metrics to quantify the solution quality according to the business requirements. To make these routes operational and something the drivers would accept we needed to develop routes that were more nuanced than just the shortest cumulative time or distance. Once we had our testing criteria settled there was an iterative approach of algorithm development and testing which we will explore from the testing perspective including visualization, stopping criteria, and the tradeoff between quality and performance.

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