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Utilizing housing resources for homeless youth through the lens of multiple multi-dimensional knapsacks

Utilizing housing resources for homeless youth through the lens of multiple multi-dimensional knapsacks
Utilizing housing resources for homeless youth through the lens of multiple multi-dimensional knapsacks

There are over 1 million homeless youth in the U.S. each year. To reduce homelessness, U.S. Housing and Urban Development (HUD) and housing communities provide housing programs/services to homeless youth with the goal of improving their long-term situation. Housing communities are facing a difficult task of filling their housing programs, with as many youths as possible, subject to resource constraints for meeting the needs of youth. Currently, the assignment is manually done by humans working in the housing communities. In this paper, we consider the problem of assigning homeless youth to housing programs subject to resource constraints. We provide an initial abstract model for this setting and show that the problem of maximizing the total assigned youth to the programs under this model is APX-hard. To solve the problem, we non-trivially formulate it as a multiple multi-dimensional knapsack problem (MMDKP), which is not known to have any approximation algorithm. We provide a first interpretable and easy-to-use greedy algorithm with logarithmic approximation ratio for solving general MMDKP. We conduct experiments on random and realistic instances of the housing assignment settings and show that our algorithm is efficient and effective in solving large instances (up to 1 million youth).

approximation algorithm, greedy algorithm, homeless youth, housing allocation, knapsack, multi-dimensional knapsack, simulation
41-47
ACM
Chan, Hau
4d760146-3e9b-4ba9-8cdb-74203c759421
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Wilder, Bryan
4b626fe0-1d60-4103-817f-32612397f74c
Rice, Eric
2d449527-bc86-4e79-b58a-80b53b7334a9
Vayanos, Phebe
80d104da-8892-4ee6-9232-cdcc561a7445
Tambe, Milind
a620fda8-c4fe-4193-a396-fe6de595fc6f
Chan, Hau
4d760146-3e9b-4ba9-8cdb-74203c759421
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Wilder, Bryan
4b626fe0-1d60-4103-817f-32612397f74c
Rice, Eric
2d449527-bc86-4e79-b58a-80b53b7334a9
Vayanos, Phebe
80d104da-8892-4ee6-9232-cdcc561a7445
Tambe, Milind
a620fda8-c4fe-4193-a396-fe6de595fc6f

Chan, Hau, Tran-Thanh, Long, Wilder, Bryan, Rice, Eric, Vayanos, Phebe and Tambe, Milind (2018) Utilizing housing resources for homeless youth through the lens of multiple multi-dimensional knapsacks. In AIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. ACM. pp. 41-47 . (doi:10.1145/3278721.3278757).

Record type: Conference or Workshop Item (Paper)

Abstract

There are over 1 million homeless youth in the U.S. each year. To reduce homelessness, U.S. Housing and Urban Development (HUD) and housing communities provide housing programs/services to homeless youth with the goal of improving their long-term situation. Housing communities are facing a difficult task of filling their housing programs, with as many youths as possible, subject to resource constraints for meeting the needs of youth. Currently, the assignment is manually done by humans working in the housing communities. In this paper, we consider the problem of assigning homeless youth to housing programs subject to resource constraints. We provide an initial abstract model for this setting and show that the problem of maximizing the total assigned youth to the programs under this model is APX-hard. To solve the problem, we non-trivially formulate it as a multiple multi-dimensional knapsack problem (MMDKP), which is not known to have any approximation algorithm. We provide a first interpretable and easy-to-use greedy algorithm with logarithmic approximation ratio for solving general MMDKP. We conduct experiments on random and realistic instances of the housing assignment settings and show that our algorithm is efficient and effective in solving large instances (up to 1 million youth).

Full text not available from this repository.

More information

Published date: 27 December 2018
Venue - Dates: 1st AAAI/ACM Conference on AI, Ethics, and Society, AIES 2018, New Orleans, United States, 2018-02-02 - 2018-02-03
Keywords: approximation algorithm, greedy algorithm, homeless youth, housing allocation, knapsack, multi-dimensional knapsack, simulation

Identifiers

Local EPrints ID: 428768
URI: https://eprints.soton.ac.uk/id/eprint/428768
PURE UUID: 77e061b1-08db-4288-9442-99be797301e7
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

Catalogue record

Date deposited: 08 Mar 2019 17:30
Last modified: 17 Jul 2019 00:45

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Contributors

Author: Hau Chan
Author: Long Tran-Thanh ORCID iD
Author: Bryan Wilder
Author: Eric Rice
Author: Phebe Vayanos
Author: Milind Tambe

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