4.7 Article

A novel two-stage framework for reducing ergonomic risks of a mixed-model parallel U-shaped assembly-line

Journal

APPLIED MATHEMATICAL MODELLING
Volume 93, Issue -, Pages 597-617

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2020.12.027

Keywords

Assembly line balancing; Constraint programming; Ergonomic risks; Parallel U-shaped lines

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Taking ergonomic risks into consideration, this study proposes a two-stage framework to balance a mixed-model parallel U-shaped assembly line, aiming to distribute tasks evenly among stations to achieve a balanced task risk. Numerical results demonstrate that the proposed objective function effectively balances ergonomic risks, with the constraint programming model performing better for small and medium-size problems and the heuristic algorithm being more suitable for large-size problems.
Considering ergonomic factors in assembly line balancing problems can significantly improve human ergonomic conditions. In this study, a two-stage framework is developed to balance a mixed-model parallel U-shaped assembly line by considering ergonomic risks. In the first stage, according to various ergonomic standards and using the best-worst method and ELECTRE TRI, ergonomic risks of each task are identified and each task is classified into hard, normal, or easy class. In the second stage, a mathematical model of the problem is developed by considering a new objective function to level the number of tasks of each class between stations. This objective ensures that the tasks of each class are distributed equally among stations, resulting in having a roughly equal number of hard, normal, and easy tasks in each station. To solve the problem, a constraint programming model and a heuristic algorithm are developed. Numerical results show that the proposed objective function can level ergonomic risks well. The performance of the proposed solution approaches is examined against six different metaheuristic algorithms. Numerical results indicate the superiority of the constraint programming model for small and medium-size problems and that of the heuristic algorithm for large-size problems. (c) 2020 Elsevier Inc. All rights reserved.

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