Facility Relocation Search For Good: When Facility Exposure
Meets User Convenience
Hui Luo Zhifeng Bao
∗
J. Shane Culpepper
RMIT University, Australia RMIT University, Australia RMIT University, Australia
hui.luo@rmit.edu.au zhifeng.bao@rmit.edu.au shane.culpepper@rmit.edu.au
Mingzhao Li Yanchang Zhao
RMIT University, Australia CSIRO, Australia
mingzhao.li@rmit.edu.au Yanchang.Zhao@data61.csiro.au
ABSTRACT
In this paper, we propose a novel facility relocation problem where
facilities (and their services) are portable, which is a combinatorial
search problem with many practical applications. Given a set of
users, a set of existing facilities, and a set of potential sites, we
decide which of the existing facilities to relocate to potential sites,
such that two factors are satisfed: (1) facility exposure: facilities
after relocation have balanced exposure, namely serving equivalent
numbers of users; (2) user convenience: it is convenient for users
to access the nearest facility, which provides services with shorter
travel distance. This problem is motivated by applications such as
dynamically redistributing vaccine resources to align supply with
demand for diferent vaccination centers, and relocating the bike
sharing sites daily to improve the transportation efciency. We
frst prove that this problem is NP-hard, and then we propose two
algorithms: a non-learning best response algorithm (BR) and a rein-
forcement learning algorithm (RL). In particular, the best response
algorithm fnds a Nash equilibrium to balance the facility-related
and the user-related goals. To avoid being confned to only one
Nash equilibrium, as found in the BR method, we also propose the
reinforcement learning algorithm for long-term benefts, where
each facility is an agent and we determine whether a facility needs
to be relocated or not. To verify the efectiveness of our methods, we
adopt multiple metrics to evaluate not only our objective, but also
several other facility exposure equity and user convenience metrics
to understand the benefts after facility relocation. Finally, com-
prehensive experiments using real-world datasets provide insights
into the efectiveness of the two algorithms in practice.
CCS CONCEPTS
• Information systems → Information retrieval;• Computing
methodologies → Artifcial intelligence.
KEYWORDS
facility relocation, facility exposure, user convenience
∗
Corresponding author.
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WWW ’23, April 30–May 04, 2023, Austin, TX, USA
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9416-1/23/04. . . $15.00
https://doi.org/10.1145/3543507.3583859
ACM Reference Format:
Hui Luo, Zhifeng Bao, J. Shane Culpepper, Mingzhao Li, and Yanchang
Zhao. 2023. Facility Relocation Search For Good: When Facility Exposure
Meets User Convenience. In Proceedings of the ACM Web Conference 2023
(WWW ’23), April 30–May 04, 2023, Austin, TX, USA. ACM, New York, NY,
USA, 11 pages. https://doi.org/10.1145/3543507.3583859
1 INTRODUCTION
In this paper, we study the problem of facility relocation search,
where facilities (and their services) are portable to provide higher
service quality for social good [33]. The facility relocation prob-
lem can provide support for a lot of domains, such as testing and
vaccination for COVID-19 or similar pandemics that may occur in
the future for medical health [38], and resource redistribution in
vulnerable areas [37]. For example, COVID-19 checkpoints often
need to be dynamically moved since new infection clusters can
occur at diferent exposure sites. Since checkpoints have limited hu-
man resources and cannot test a large number of people in a timely
manner, it is important to let users travel fast to get services and
meantime balance the number of people served by each checkpoint,
which also refers to the concept of fairness among facilities [19]. A
related application is the redistribution of medical resources (e.g.,
vaccines) in the vaccination sites, when the supply cannot meet the
demand [12]. Since virus transmission changes rapidly and is often
unpredictable, it is often urgent to relocate checkpoints or medical
resources quickly.
Another practical application example of this problem is to redis-
tribute bike or scooter sites by bike sharing services, where bikes
or scooters can be rented anywhere [16, 27, 46]. A company must
fnd sites to distribute bikes and scooters daily, often based on de-
mand [28, 45]. Providing a reasonable facility relocation strategy
is important for the wellbeing of both service providers (i.e., facil-
ities) and service recipents (i.e., users) [32]. We refer to this as a
facility relocation problem, that is, a subset of existing facilities can
be relocated to new locations. The benefts provided by the facility
relocation are illustrated in the following example.
Example 1. Fig. 1 illustrates an example before and after facility
relocation. Initially, in Fig. 1a, there are three users (
1
,
2
, and
3
),
two existing facilities (
1
and
2
), and two potential sites (
1
and
2
).
The potential sites are idle locations for existing facilities to relocate to.
Without loss of generality, users follow the closest proximity principle,
which is widely adopted in the literature [8, 25, 36, 49]. It is clear that
1
is the nearest facility for all three users while
2
is unused. In Fig.
1b, when we move
2
to
1
,
1
(i.e., the raw
2
) becomes the nearest
facility of
1
and
2
.
Based on Example 1, we can observe two immediate benefts for
facility relocation: (1) Balanced exposure of each facility. We use
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