Task Allocation in Human-Machine Manufacturing Systems Using Deep Reinforcement Learning

Abstract

Catering for human operators is a critical aspect in the sustainability of a manufacturing sector. This paper presents a task allocation problem in human-machine manufacturing systems. A key aspect of this problem is to carefully consider the characteristics of human operators having different task preferences and capabilities. However, the characteristics of human operators are usually implicit, which makes the mathematical formulation of the problem difficult. In addition, variability in manufacturing systems such as job completion and machine breakdowns are prevalent. To address these challenges, this paper proposes a deep reinforcement learning-based approach to accommodate the unobservable characteristics of human operators and the stochastic nature of manufacturing systems. Historical data accumulated in the process of job assignment are exploited to allocate tasks to either humans or machines. We demonstrate that the proposed model accommodates task competence and fatigue levels of individual human operators into job assignments, thereby improving scheduling-related performance measures compared to classical dispatching rules.