Gig-work Management System with Chance-Constraints Verification Algorithm

TL;DR

This paper introduces a gig-work management system utilizing chance-constrained model predictive control to optimize task hours and wages, supported by data-driven gig-worker models and an efficient solution algorithm.

Contribution

It presents a novel framework combining CC-MPC with gig-worker decision models and an approximate solution algorithm for effective gig-work management.

Findings

Developed a new gig-work management framework.

Formulated the problem as a CC-MPC model.

Created an efficient approximate solution algorithm.

Abstract

This paper proposes the framework of an efficient gig-work management system. A gig-work management system recommends one-off tasks with information about task hours and wages to gig-workers. To enable effective management, this paper develops a model of gig-workers' decision-making. Then, based on the model, we formulate an optimization problem to determine the optimal task hours and wages. The formulated problem belongs to the class of chance-constrained model predictive control (CC-MPC) problems. To efficiently solve the CC-MPC problem, we develop an approximate solution algorithm with guaranteed confidence levels. Finally, we develop gig-worker models based on data collected through crowdsourcing.