In this research, we address U-line balancing under uncertainty and propose a robust optimization model to generate assembly lines that are protected against disruptions in operation times. U-shaped layouts have been widely investigated in literature because they are more efficient and flexible than traditional straight assembly lines. They offer more choices for grouping tasks since the same worker could work in different stations which are located at entrance and exit sides of the lines. Majority of the existing research on assembly line balancing makes a simplifying assumption and models deterministic environments. However, in real life we are subject to various sources of uncertainty, like variability in operation times. To avoid deviations from production targets, we will use robust optimization, which is a fundamental optimization method that hedge against uncertainty. The other approaches are stochastic programming, sensitivity analysis, parametric programming and fuzzy programming. Robust optimization addresses minmax and minmax regret objectives (see Kouvelis and Yu [1]). However, pessimistic solutions could be generated. To avoid over pessimism, Bertsimas and Sim [2] have proposed a restricted uncertainty model in which only a subset of coefficients are driven to their upper bounds. We use this approach to formulate the robust U-type assembly line problem with minimum number of workstations (UALBP-1). More specifically, we aim to design assembly lines that could are protected against variability in operations times. Cycle time is fixed and variability affect the number of stations installed.