Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study

Abstract

Purpose

The purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.

Design/methodology/approach

This paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.

Findings

Computational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.

Originality/value

The developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.

Citation

Kim, D., Moon, D.H. and Moon, I. (2018), "Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study", Assembly Automation, Vol. 38 No. 4, pp. 511-523. https://doi.org/10.1108/AA-06-2017-070