We investigate the performance of a full‐cost heuristic in a service setting. In our model, a service firm determines the amount of capacity, a price, and a price discount each period. Based upon the price, a stochastic number of customers will place service orders. If too many orders arrive in a period, the firm will offer a price discount to those customers willing to back order and accept service the next period. Even though the model is fairly simple, the optimal pricing, price discount, and capacity rules are complex and require extensive calculations. We examine how closely three distinct heuristics approximate the optimal performance. The best‐performing heuristic is a full‐cost pricing rule based upon a constrained version of the firm's optimization program. It consists of setting a price using full costs plus an adjustment based upon the nonlinear elasticity of demand. In 500 random simulations our full‐cost heuristic obtains 99.5 percent of the optimal performance. Preliminary analysis suggests that a modified full‐cost heuristic may continue to do well in settings where interim demand information arrives after the capacity choice, but before the pricing choice. However, a modified full‐cost heuristic may not perform well when capacity lasts several periods.

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