We introduce Lehmer branch-and-price, an exact algorithm for bin packing and cutting stock problems built on the Lehmer transform, the ratio of Mellin transforms of item weights at consecutive orders. The transform maps each bin, a finite multiset of item weights, to a smooth increasing curve, and so gives the discrete problem a continuous representation on which the tools of analysis, such as convexity, exponential families and smoothing, can act. Read along this curve, the problem yields an explicit dual solution, a monotone representation of the dominance order, a family of constructive heuristics with first fit decreasing as its limit, and an integer identity that tells when the linear relaxation can decide an instance and when only a perfect packing can improve the best solution known. Lifted from items to patterns, the transform gives entropic column generation, a new form of column generation that bounds the relaxation from both sides at every iteration without solving the pricing problem exactly, maximizes a smoothed dual at an explicit rate, and measures by an entropy how much the relaxation leaves undecided. We show that the proposed algorithm outperforms the state-of-the-art methods on the standard benchmark in both the number of instances solved and the computational time.

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