Two-Sided Capacitated Submodular Maximization in Gig Platforms

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

In this paper, we propose three generic models of capacitated coverage and, more generally, submodular maximization to study task-worker assignment problems that arise in a wide range of gig economy platforms. Our models incorporate the following features: (1) Each task and worker can have an arbitrary matching capacity, which captures the limited number of copies or finite budget for the task and the working capacity of the worker; (2) Each task is associated with a coverage or, more generally, a monotone submodular utility function. Our objective is to design an allocation policy that maximizes the sum of all tasks’ utilities, subject to capacity constraints on tasks and workers. We consider two settings: offline, where all tasks and workers are static, and online, where tasks are static while workers arrive dynamically. We present three LP-based rounding algorithms that achieve optimal approximation ratios of 1 - 1 / e∼ 0.632 for offline coverage maximization, competitive ratios of (19 - 67 / e3) / 27 ∼ 0.580 and 0.436 for online coverage and online monotone submodular maximization, respectively.

Identifier

85181982193 (Scopus)

ISBN

[9783031489730]

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

External Full Text Location

https://doi.org/10.1007/978-3-031-48974-7_34

e-ISSN

16113349

ISSN

03029743

First Page

600

Last Page

617

Volume

14413 LNCS

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