Silvis, Mark
(2018)
PittGrub: A Frustration-Free System To Reduce Food Waste By Notifying Hungry College Students.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
The amount of food waste generated by the U.S. is staggering, both expensive in economic cost and environmental side effects. Surplus food, which could be used to feed people facing food insecurity, is instead discarded and sent to landfills. Institutions, universities, and non-profits have noticed this issue and are beginning to take action to reduce surplus food waste, typically by redirecting it to food banks and other organizations or having students transport or eat the food. These approaches present challenges such as transportation, volunteer availability, and lack of prioritization of those in need. In this thesis, we introduce PittGrub, a notification system to intelligently select users to invite to events that have leftover food. PittGrub was invented to help reduce food waste at the University of Pittsburgh. We use reinforcement learning to determine how many notifications to send out and whom to prioritize in the notifications. Our goal is to produce a system that prioritizes feeding students in need while simultaneously eliminating food waste and maintaining a fair distribution of notifications. As far as we are aware, PittGrub is unique in its approach to eliminating surplus food waste while striving for social good. We compare our reinforcement learning approach to multiple baselines on simulated datasets to demonstrate effectiveness. Experimental results comparing various algorithms show promise in eliminating food waste while helping those facing food insecurity and treating users fairly. At the time of writing, our prototype is in beta; we plan to have it deployed during the Spring semester of 2018.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
14 June 2018 |
| Date Type: |
Publication |
| Defense Date: |
19 March 2018 |
| Approval Date: |
14 June 2018 |
| Submission Date: |
12 April 2018 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
60 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Computer Science |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
reinforcement learning, machine learning, food insecurity, sustainability |
| Related URLs: |
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| Date Deposited: |
14 Jun 2018 13:29 |
| Last Modified: |
14 Jun 2019 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/34276 |
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