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In this paper, we propose three generic models of capacitated coverage and,more generally, submodular maximization to study task-worker assignmentproblems that arise in a wide range of gig economy platforms. Our modelsincorporate the following features: (1) Each task and worker can have anarbitrary matching capacity, which captures the limited number of copies orfinite budget for the task and the working capacity of the worker; (2) Eachtask is associated with a coverage or, more generally, a monotone submodularutility function. Our objective is to design an allocation policy thatmaximizes the sum of all tasks' utilities, subject to capacity constraints ontasks and workers. We consider two settings: offline, where all tasks andworkers are static, and online, where tasks are static while workers arrivedynamically. We present three LP-based rounding algorithms that achieve optimalapproximation ratios of $1-1/\mathsf{e} \sim 0.632$ for offline coveragemaximization, competitive ratios of $(19-67/\mathsf{e}^3)/27\sim 0.580$ and$0.436$ for online coverage and online monotone submodular maximization,respectively.

Two-Sided Capacitated Submodular Maximization in Gig Platforms