Robust balancing of mixed model assembly line

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.