COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

Purpose – The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.

Design/methodology/approach – Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min-max related; and the other is a-worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm-based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches.

Findings – With min-max related robust criteria, the solutions can provide an optimal worst-case hedge against uncertainties without a significant sacrifice in the long-run performance; a-worst scenario-based criteria can generate flexible robust solutions: through rationally tuning the value of a, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.

Research limitations/implications – This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.

Practical implications – In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.

Originality/value – Using robust optimization approaches to balance mixed model assembly line.