This paper presents a novel approach to refinery planning under uncertainty. To calculate the expectation of plant revenues, which is the main difficulty of the problem, loss functions are derived and applied. Different standard loss function approximation methods are compared and integrated into the planning model. The results show that the piecewise-linear approximation of-the loss function can obtain good accuracy with improved solution speed. A general formulation for revenue and cost calculations is proposed by considering uncertainty in both the raw material and the product demand. To handle possible unmet customer demands, the hard-to-specify penalty functions of the two-stage programming are avoided and replaced by two of the decision maker's service objectives, namely, confidence level and fill rate. Confidence level, which is the probability of satisfying customer demands, is commonly used in chance-constrained programming. However, fill rate, which is the proportion of demands that are met by a plant, is a greater concern of most managers. In this paper, fill rates are effectively calculated using the loss function. The maximum plant profit that satisfies certain fill rate objectives can then be obtained. Case studies show that a planning strategy that satisfies certain confidence level objectives might be overly lenient compared to a strategy that satisfies a fill rate objective. Because very few refinery planning models consider the influences of uncertainty, case studies including real-world large-scale refinery planning problems are used to illustrate the effectiveness of the proposed approach.