Fair Online Allocation of Perishable Goods and its Application to Electric Vehicle Charging

Gerding, Enrico H.; Perez‐Diaz, Alvaro; Aziz, Haris; Gaspers, Serge; Marcu, Antonia; Mattei, Nicholas; Walsh, Toby

doi:10.24963/ijcai.2019/773

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…fied many important features of such problems, there are many research directions still to be considered. For example, how do we extend recent results such as (Aziz et al 2015b;2015a;Aziz, Schlotter, and Walsh 2016;Aziz et al 2017;Gerding et al 2019;Aziz et al 2019;Aleksandrov and Walsh 2018) about the (offline) fair division of goods and bads to the online setting? As a second example, the preferences of agents are often highly correlated.…”

Section: Discussionmentioning

confidence: 85%

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Online Fair Division: A Survey

Aleksandrov

Walsh

2020

AAAI

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We survey a burgeoning and promising new research area that considers the online nature of many practical fair division problems. We identify wide variety of such online fair division problems, as well as discuss new mechanisms and normative properties that apply to this online setting. The online nature of such fair division problems provides both opportunities and challenges such as the possibility to develop new online mechanisms as well as the difficulty of dealing with an uncertain future.

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Section: Discussionmentioning

confidence: 85%

“…Or do we allocate multiple items to each agent (e.g. multiple food products in (Aleksandrov et al 2015), multiple charging slots in (Gerding et al 2019))?…”

Section: Resource Typesmentioning

confidence: 99%

Online Fair Division: A Survey

Aleksandrov

Walsh

2020

AAAI

Self Cite

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“…In [29] they consider this setting where the resources are indivisible with the goal of maximizing utilitarian welfare (or the sum of utilities) which provides no guarantees on individual fairness. Another approach in [25] considers a scheduling setting where agents arrive and depart online. Each agent has a fixed and known arrival time, departure time, and demand.…”

Section: Related Workmentioning

confidence: 99%

Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve

Sinclair¹,

Banerjee²,

Yu³

2021

Preprint

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We consider the problem of dividing limited resources to individuals arriving over T rounds. Each round has a random number of individuals arrive, and individuals can be characterized by their type (i.e. preferences over the different resources). A standard notion of 'fairness' in this setting is that an allocation simultaneously satisfy envy-freeness and efficiency. The former is an individual guarantee, requiring that each agent prefers her own allocation over the allocation of any other; in contrast, efficiency is a global property, requiring that the allocations clear the available resources. For divisible resources, when the number of individuals of each type are known upfront, the above desiderata are simultaneously achievable for a large class of utility functions. However, in an online setting when the number of individuals of each type are only revealed round by round, no policy can guarantee these desiderata simultaneously, and hence the best one can do is to try and allocate so as to approximately satisfy the two properties.We show that in the online setting, the two desired properties (envy-freeness and efficiency) are in direct contention, in that any algorithm achieving additive envy-freeness up to a factor of L T necessarily suffers an efficiency loss of at least 1/L T . We complement this uncertainty principle with a simple algorithm, Guarded-Hope, which allocates resources based on an adaptive threshold policy. We show that our algorithm is able to achieve any fairness-efficiency point on this frontier, and moreover, in simulation results, provides allocations close to the optimal fair solution in hindsight. This motivates its use in practical applications, as the algorithm is able to adapt to any desired fairness efficiency trade-off.

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“…In our application of allocating mentors to students we can observe a dynamic nature, so the literature on online matching is also related. Natural applications for online matching with preferences are deceased organ allocation [25], [3], allocation in social housing [23], [14], electric vehicle charging [20], or lending decisions [28].…”

Section: Related Literaturementioning

confidence: 99%

Online voluntary mentoring: Optimising the assignment of students and mentors

Bíró¹,

Gyetvai²

2021

Preprint

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After the closure of the schools in Hungary from March 2020 due to the pandemic, many students were left at home with no or not enough parental help for studying, and in the meantime some people had more free time and willingness to help others in need during the lockdown. In this paper we describe the optimisation aspects of a joint NGO project for allocating voluntary mentors to students using a web-based coordination mechanism. The goal of the project has been to form optimal pairs and study groups by taking into the preferences and the constraints of the participants. In this paper we present the optimisation concept, and the integer programming techniques used for solving the allocation problems. Furthermore, we conducted computation simulations on real and generated data for evaluate the performance of this dynamic matching scheme under different parameter settings.

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Fair Online Allocation of Perishable Goods and its Application to Electric Vehicle Charging

Cited by 7 publications

References 16 publications

Online Fair Division: A Survey

Online Fair Division: A Survey

Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve

Online voluntary mentoring: Optimising the assignment of students and mentors

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