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Dynamic Fleet Management and Household Feedback for Garbage Collection

Dr. Martin Aleksandrov – 2022

We consider a fair division model in which agents have general valuations for bundles of indivisible items. We propose two new approximate properties for envy freeness of allocations in this model: DEFX and DEF1. We compare these with two existing axiomatic properties: EFX and EF1. For example, we give the first result confirming that EFX allocations may not exist with general but identical valuations. However, even when they do exist in such problems, we prove that DEFX (and, therefore DEF1) and PO allocations exist whereas EFX and PO allocations may not exist. Our results assert eloquently that DEFX and DEF1 approximate fairness better than EFX and EF1.

Dynamic Fleet Management and Household Feedback for Garbage Collection
Association for Computing Machinery
DOI: 10.1007/978-3-031-16474-3_59; Print ISBN: 978-3-031-16473-6
Erschienen in
In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES '22), August 1-3, 2022, Oxford, United Kingdom, pages 36-45.
Die Arbeit steht im Zusammenhang mit dem DFG-Projekt "Fairness and Efficiency in Emerging Vehicle Routing Problems' mit der Förderungsnummer 497791398
BibTeX Code
author = {Aleksandrov, Martin Damyanov},
title = {Dynamic Fleet Management and Household Feedback for Garbage Collection},
year = {2022},
isbn = {9781450392471},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3514094.3534152},
doi = {10.1145/3514094.3534152},
abstract = {We propose a solution for intelligent household garbage collection in smart cities. Garbage containers are assumed to be digitalized with Internet-of-Things sensors that are capable of sensing the fill levels of containers and transmitting this data through LoRaWAN networks to a central server. Data is used for dynamic fleet management and household feedback. We give a number of algorithms for these tasks. Fleet management requires scheduling containers for collections and assigning containers to trucks, as well as routing the trucks. Drivers receive such navigations via pervasive computing devices such as tablets, phones, or watches. Household feedback consists of information about the levels of generated garbage and the associated costs. Households receive this information on their home devices. Thus, unlike present solutions, our solution involves households in the intelligent collection of their garbage.},
booktitle = {Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society},
pages = {36--45},
numpages = {10},
keywords = {Ethics, Vehicle Routing Problem, Garbage Management},
location = {Oxford, United Kingdom},
series = {AIES '22}