A Methodology of Task Allocation to Design a Human-Robot Assembly Line: Integration of DFA Ergonomics and Time-Cost Effectiveness Optimization

Anh Vo Ngoc Tram and Morrakot Raweewan
Additional contact information
Anh Vo Ngoc Tram: School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University, Thailand
Morrakot Raweewan: School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University, Thailand

International Journal of Knowledge and Systems Science (IJKSS), 2021, vol. 12, issue 3, 21-52

Abstract: There are successful cases in lean manual assembly lines; however, in some cases, such as the ease of assembly in quicker cycle time, the designs are not satisfactory and must be transformed to semi-automation. This research studies human-robot task allocation when designing for semi-automation considering not only time-cost effectiveness as in the existing research but also assembly difficulty and ergonomic issues. A proposed methodology optimally determines what tasks should be performed by humans or robots, at which station, and in what sequence. A multi-objective linear programming (MOLP) model is proposed to simultaneously minimize total operating cost, cycle time, and ergonomic difficulty. Solving the model has two approaches: with and without optimal weights. The methodology is applied to a Lego-car assembly line. To illustrate the benefits of the proposed MOLP, a comparison between it and three single-objective models is made. Results show that the optimal-weight MOLP yields a better performance (a shorter cycle time, a lower cost, and especially, a significant ergonomic improvement) when compared to the other MOLP and single-objective models.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJKSS.2021070102 (application/pdf)

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:igg:jkss00:v:12:y:2021:i:3:p:21-52

Access Statistics for this article

International Journal of Knowledge and Systems Science (IJKSS) is currently edited by Van Nam Huynh

More articles in International Journal of Knowledge and Systems Science (IJKSS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().