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.
U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and plannin…
Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This…
Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two prim…
Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study…
The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a…
Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This…