Assembly line worker assignment and rebalancing problem: A mathematical model and an artificial bee colony algorithm

In real life, some situations such as demand fluctuations, changes in product structure, and workstation failures affect the existing balance of assembly lines, resulting in the need for these lines to rebalance. In the assembly line rebalancing problem (ALRBP), it is assumed that the processing time of a task does not depend on the worker performing it. Nevertheless, in practice, the time each worker requires to execute a task varies owing to several reasons, including the experience, skill, and disability of some individuals. In this study, the assembly line worker assignment and rebalancing problem (ALWARBP), which considers task times diversify in terms of workers, is introduced to fill this gap. The ALWARBP consists of the reassignment of tasks and workers to non-disrupted workstations after disruptions occur due to breakdowns or shutdowns of workstations to minimize variability in terms of cycle time and workstation assignments of tasks relative to the initial line balance. The aims of this paper are to i) describe the ALWARBP, ii) develop a mixed-integer linear programming (MILP) model, and iii) propose an artificial bee colony (ABC) algorithm to tackle the considered problem. The numerical experiments have been designed and conducted using 120 problem instances. The experimental results indicate that both methods managed to obtain optimal solutions in small-sized instances. In large-sized test instances, the proposed ABC algorithm showed higher performance in terms of solution value and computation time.

Automated summary

This study introduces the Assembly Line Worker Assignment and Rebalancing Problem (ALWARBP), aiming to minimize variability in cycle time and workstation assignments by reallocating tasks and workers after disruptions occur. Both methods successfully obtained optimal solutions in small instances, while the proposed ABC algorithm showed better performance in terms of solution value and computation time in large test instances.