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

Taejong Joo, Hyunyoung Jun and Dongmin Shin
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Taejong Joo: Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL 60208, USA
Hyunyoung Jun: Industrial and Management Engineering Department, Hanyang University, Ansan 15588, Korea
Dongmin Shin: Industrial and Management Engineering Department, Hanyang University, Ansan 15588, Korea

Sustainability, 2022, vol. 14, issue 4, 1-18

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.

Keywords: deep learning; dynamic task allocation; human factors; intelligent manufacturing systems; manufacturing scheduling; reinforcement learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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