A bi-objective ergonomic assembly line balancing model with conic scalarization method


Human Factors and Ergonomics In Manufacturing, vol.32, no.6, pp.494-507, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.1002/hfm.20967
  • Journal Name: Human Factors and Ergonomics In Manufacturing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Compendex, Environment Index, INSPEC, Psycinfo
  • Page Numbers: pp.494-507
  • Keywords: assembly line balancing, conic scalarization, ergonomic risk evaluation, multiobjective programming, REBA, HEURISTIC-PROCEDURE, RISK, DESIGN, POSTURES, WMSDS
  • Eskisehir Osmangazi University Affiliated: Yes


© 2022 Wiley Periodicals LLC.The most important factor affecting efficiency and ergonomic risk levels in an assembly line design is the problem of assigning certain tasks to certain stations, namely the assembly line balancing problem. In the literature, assembly line balancing problem has often been studied, but studies that consider ergonomic risks are deficient. Recently, it has been one of the issues that have started to attract great attention with the realization of health problems caused by assembly lines. To this end, in this study, a bi-objective mathematical model is developed that considers balancing assembly line station time and ergonomic risk levels, simultaneously. It is aimed to minimize both station time and the total deviations of ergonomic risk scores for the stations. Weighted sum and conic scalarization methods are applied to solve the bi-objective model. To analyze the outcomes of the developed model, an application is proposed and tested on a real industrial case, at a home appliance assembly line. The deployment of the OMAX method is a contribution to the literature since it shows an analysis tool which evaluates the results of assembly line balancing. This method evaluates the performance of the stations based on different criteria such as station time and ergonomic risk. The number of high-risk stations is obtained as 13 in the single-objective model aiming to minimize the station time, while it is found to be nine in the bi-objective model solved with CSM, without an increase in the total number of stations.