Human-Oriented Assembly Line Balancing and Sequencing Model in the Industry 4.0 Era
Daria Battini (),
Serena Finco () and
Fabio Sgarbossa ()
Additional contact information
Daria Battini: University of Padova
Serena Finco: University of Padova
Fabio Sgarbossa: NTNU—Norwegian University of Science and Technology
Chapter Chapter 8 in Scheduling in Industry 4.0 and Cloud Manufacturing, 2020, pp 141-165 from Springer
Abstract:
Abstract Ergonomics plays a crucial role in the design process of manual assembly systems, since a poorly ergonomic workplace leads to injuries, accidents, and musculoskeletal disorders. Using Industry 4.0 solutions, smart technologies, and cloud platforms, the well-being of workers can be improved more easily than in the past. In this context, smartwatches can be used to monitor workers’ health and to collect data about the physical efforts of each worker during the working day, in relation to energy expenditure or heart rate monitoring. Managers can use data collected via these smart solutions to improve sequencing and scheduling processes in terms of both ergonomics and time, achieving a trade-off between ergonomics and productivity. Using real-time monitoring, a dynamic scheduling and sequencing approach can be implemented to guarantee the right safety level for each worker. In this chapter, we give a general overview of smart tools for measuring and quantifying the ergonomics level. Based on the data from smartwatches, we propose a multi-objective assembly line balancing model and an ergo-sequencing model, and demonstrate the benefits of using smart solutions and Industry 4.0 tools. The limitations are discussed using a real case application. Our conclusions can guide managers and practitioners during the design phase.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-43177-8_8
Ordering information: This item can be ordered from
http://www.springer.com/9783030431778
DOI: 10.1007/978-3-030-43177-8_8
Access Statistics for this chapter
More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().