Evaluating the effect of learning rate, batch size and assignment strategies on the production performance

ABSTRACT

Task assignment methods usually rely on the fixed mean processing times of operations with the intent of balancing the workload assigned to operators or workstations in the production line. This assignment usually neglects the variability of operator processing times. In this work, a methodology in which the time in which an operator executes a task is variable, accordingly to a learning model, is proposed. It is exploited in order to assess the real-time task assignment adopted in the actual factory. The results show that, by including a learning model, it is possible to predict more accurately the long-term cycle time of the process. Standard scheduling strategies (first operator available, the operator closest to the machine) were compared with learning-oriented strategies (the most skilled, the least skilled). Through the case study, the paper addresses the problem of using a dynamic task assignment.an illustration.

KEYWORDS:

• Workflow balancing
• computer simulation
• assembly
• value flow improvement

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Giulia Bruno

Giulia Bruno assistant professor in the Department of Management and Production Engineering of the Politecnico di Torino, Italy. Her research activity is focused on knowledge management, machine learning and discrete event simulation applied to production systems. Furthermore, she is working in the fields of human-robot collaboration and product lifecycle management. Her experience includes the participation in several national and European projects in the context of smart factories and industrial innovation.

Dario Antonelli

Dario Antonelli is an associate professor in the Department of Management and Production Engineering at the Politecnico di Torino University, Italy. Previously, he was a researcher at the Fiat Research Center. His research activities include modeling large-scale distributed manufacturing systems and networks, designing production planning and control architectures, developing procedures for organizing human resources in industrial systems and public services. He is member of IFIP Working Group 5.5 COVE (Collaborative Virtual Enterprise).

Dorota Stadnicka

Dorota Stadnicka is an associate professor in the Faculty of Mechanical Engineering and Aeronautics at the Rzeszow University of Technology, Poland. Her research activities include industrial problems identification and elimination with the use of modern technologies supporting lean manufacturing, sustainable development and industrial digitalization. She is a member of Production Engineering Committee of the Polish Academy of Sciences.