An approach to formulate learning rates and include them in line-of-balance (LOB) calculations is proposed in this paper. Learning rates are generated by modifying historical learning rates of typical construction activities and by incorporating the impact of relevant factors such as, number of operations in one unit, activity complexity, and job and management conditions. Fuzzy set theory is used to develop production rules to treat both factual and uncertain information. An S-type membership function is used to interpret the fuzzy data and produce adjustment factors that are in turn used to modify consecutive learning rates, until an adjusted learning rate is obtained. The adjusted learning rate is then used to calculate expected worker-hours and activity durations at each unit of production (e.g., a floor in a high-rise building, a mile of pavement work, etc.). A final LOB diagram is generated using this information. Different pairs of curves represent the start and the finish times of each activity in sets of units that make use of different numbers of crews. Learning reduces project duration and resource requirements. The proposed approach demonstrates the potential for formalizing the inclusion of learning effects into the LOB scheduling of repetitive-unit construction.