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.
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 α‐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.
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; α‐worst scenario‐based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.
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.
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.
Using robust optimization approaches to balance mixed model assembly line.
