Bundle pricing is a widespread phenomenon. However, despite its importance as a pricing tool, surprisingly little is known about how to find optimal bundle prices. Most discussions in the literature are restricted to only two components, and even in this case no algorithm is given for setting prices. Here we show that the single firm bundle pricing problem is naturally viewed as a disjunctive program which is formulated as a mixed integer linear program. Multiple components, and a variety of cost and reservation price conditions are investigated with this approach. Several new economic insights on the role and effectiveness of bundling are presented. An added benefit of the solution to the bundle pricing model is the selection of products which compose the firm's product line. Computational testing is done on problems with up to 21 components (over one million potential product bundles), and data collection issues are addressed.marketing, pricing, product policy, mathematical programming, mixed integer linear

The multinomial logit model is a standard approach for determining the probability of purchase in product line problems. When the purchase probabilities are multiplied by product contribution margins, the resulting profit function is generally nonconcave. Because of this, standard nonlinear search procedures may terminate at a local optimum which is far from the global optimum. We present a simple procedure designed to alleviate this problem. The key idea of this procedure is to find a "path" of prices from the global optimum of a related, but concave logit profit function, to the global optimum of the true (but nonconcave) logit profit function.optimization, logit, pricing, product line modeling

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Researchers report a sizable number of companies that use cost-plus pricing. While such a policy is normatively suspect, it is simple, conventional, and occasionally mandated by regulation. This paper investigates cost-plus pricing as a dynamic adjustment process. As a dynamic policy, cost-plus pricing is more flexible than has commonly been realized. When firms have constant or decreasing average costs, dynamic cost-plus pricing typically exhibits rapid convergence to the attracting member of a pair of fixed points. If the average cost function is sufficiently U-shaped, complicated and chaotic dynamics can emerge.

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