Abstract
The Kentucky Humane Society (KHS) is a private, nonprofit organization dedicated to saving every healthy, behaviorally sound animal that they take in. They are also the largest no-kill animal shelter in Kentucky, USA. Due to limited resources, the KHS has a great need for an optimized methodology for allocating available animals to their adoption facilities. Workers at the KHS believe that a capacity constraint is their most limiting factor. The purpose of this work is to develop an allocation model that will improve the throughput rate of the animals to be adopted, thereby freeing up extra capacity more quickly than current practices. The allocation model presented here assigns animals to adoption locations based on their expected length of stay at all available locations. The resulting allocation minimizes the expected length of stay for these animals. In order to make the model easy to use, we have implemented a software with a user-friendly interface.
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Acknowledgements
The authors would like to thank Robin Vincent from the Kentucky Humane Society for all her support and extensive knowledge throughout this research project. The authors are also grateful to the two students who worked on the project: Stefanie Olga who helped with the activities of Stage 1 of the project, and Mark Cummins who implemented the software.
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Gentili, M., Gerber, E., Gue, K. (2019). From Pallets to Puppies: Using Insights from Logistics to Save Animals. In: Dell'Amico, M., Gaudioso, M., Stecca, G. (eds) A View of Operations Research Applications in Italy, 2018. AIRO Springer Series, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-25842-9_10
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DOI: https://doi.org/10.1007/978-3-030-25842-9_10
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