TY - GEN
T1 - Daycare Matching in Japan
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
AU - Sun, Zhaohong
AU - Takenami, Yoshihiro
AU - Moriwaki, Daisuke
AU - Tomita, Yoji
AU - Yokoo, Makoto
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - In this paper, we study a daycare matching problem in Japan and report the design and implementation of a new centralized algorithm. There are two features that make this market different from the classical hospital-doctor matching problem: i) some children are initially enrolled and prefer to be transferred to other daycare centers; ii) one family may be associated with two or more children and is allowed to submit preferences over combinations of daycare centers. We revisit some well-studied properties including individual rationality, non-wastefulness, as well as stability, and generalize them to this new setting. We design an algorithm based on integer programming (IP) that captures these properties and conduct experiments on five real-life data sets provided by three municipalities. Experimental results show that i) our algorithm performs at least as well as currently used methods in terms of numbers of matched children and blocking coalition; ii) we can find a stable outcome for all instances, although the existence of such an outcome is not guaranteed in theory.
AB - In this paper, we study a daycare matching problem in Japan and report the design and implementation of a new centralized algorithm. There are two features that make this market different from the classical hospital-doctor matching problem: i) some children are initially enrolled and prefer to be transferred to other daycare centers; ii) one family may be associated with two or more children and is allowed to submit preferences over combinations of daycare centers. We revisit some well-studied properties including individual rationality, non-wastefulness, as well as stability, and generalize them to this new setting. We design an algorithm based on integer programming (IP) that captures these properties and conduct experiments on five real-life data sets provided by three municipalities. Experimental results show that i) our algorithm performs at least as well as currently used methods in terms of numbers of matched children and blocking coalition; ii) we can find a stable outcome for all instances, although the existence of such an outcome is not guaranteed in theory.
UR - http://www.scopus.com/inward/record.url?scp=85167979807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85167979807&partnerID=8YFLogxK
U2 - 10.1609/aaai.v37i12.26694
DO - 10.1609/aaai.v37i12.26694
M3 - Conference contribution
AN - SCOPUS:85167979807
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 14487
EP - 14495
BT - AAAI-23 Special Tracks
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
PB - AAAI Press
Y2 - 7 February 2023 through 14 February 2023
ER -